Futures time point monitoring method and device
By acquiring and analyzing futures images and identifying trading signals using point-of-time templates, the problems of low monitoring efficiency and poor accuracy in the existing technology are solved, and efficient and real-time futures point-of-time monitoring is achieved.
Patent Information
- Application Number
- CN202510321065.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-18
- Publication Date
- 2025-06-13
AI Technical Summary
The existing technology is difficult to capture the best trading points in the field of material procurement, and lacks real-time data support, resulting in low monitoring efficiency, poor accuracy and high cost.
By acquiring futures images, obtaining identification points and corresponding identification information based on the pre-configured time point template, determining the target identification points, and generating monitoring results to display the target trading signal.
It realizes automated futures point-of-time monitoring, and can achieve real-time batch futures point-of-time monitoring with zero manual intervention, improving the accuracy, timeliness and efficiency of monitoring.
Smart Images
Figure CN120147011A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present application relate to the field of computer technologies, and in particular, to a futures time point monitoring method, apparatus, computer device, computer-readable storage medium, and computer program product. Background Art
[0002] In the field of material procurement, the procurement price is affected by multiple factors such as market supply and demand, policy changes, and international trade situations, with frequent fluctuations, making it difficult to determine the appropriate procurement timing. Especially in the photovoltaic manufacturing industry, on the one hand, the high proportion of raw material costs leads to uncontrollable total costs, directly affecting product prices and market competitiveness. On the other hand, there are a wide variety of materials involved, with a large amount of data. The existing procurement processes and decision-making mechanisms still have certain lags and uncertainties, unable to capture the best trading points in a timely manner, lacking real-time data support, and being difficult to avoid risks. Relying on a large number of manual operations and monitoring, there are problems such as low monitoring efficiency, poor accuracy, and high costs.
[0003] It should be noted that the above content is not necessarily prior art and is not used to limit the patent protection scope of the present application. Summary of the Invention
[0004] Embodiments of the present application provide a futures time point monitoring method, apparatus, computer device, computer-readable storage medium, and computer program product to solve or alleviate one or more of the above technical problems.
[0005] One aspect of embodiments of the present application provides a futures time point monitoring method, and the method includes: Obtain a futures image, where the futures image includes the trading price of a target object within a preset time; Based on the futures image and a pre-configured time point template, obtain identification points and corresponding identification information, where the identification points represent the trading signals of the target object; Based on the identification points and the corresponding identification information, determine a target identification point, where the target identification point represents the target trading signal of the target object; Based on the futures image, the target identification point, and the corresponding identification information, generate a monitoring result, where the monitoring result is used to display the target trading signal.
[0006] Optionally, obtaining a futures image includes: Obtain a configuration file, where the configuration file includes a type identifier of the target object; According to the type identifier, determine the trading period of the target object; Obtain the current time, and determine whether the current time belongs to the trading period; When the current time belongs to the trading period, obtain the futures image.
[0007] Optionally, obtaining the futures image includes: Take a screenshot of the futures software interface to obtain an initial image, and the color channels of the initial image are sorted in the first order type; Adjust the sorting of the color channels of the initial image to obtain the futures image, and the color channels of the futures image are sorted in the second order type; Wherein, the first order type includes RGB, and the second order type includes BGR.
[0008] Optionally, the time point template is configured by the following operations: Capture multiple types of identification points on the futures software interface to obtain multiple screenshots; Generate multiple time point templates based on the multiple screenshots, and one time point template corresponds to one type of identification point; Wherein, each of the time point templates is associated with a corresponding matching threshold, and the matching threshold is used to obtain the identification points in the futures image whose similarity to the corresponding time point template exceeds the matching threshold.
[0009] Optionally, based on the futures image and the pre-configured time point template, obtaining the identification points and the corresponding identification information includes: Determine the invalid area of the futures image, and cut off the invalid area to obtain an intermediate image; Adjust the number of color channels of the intermediate image to convert the intermediate image into a grayscale image; Match the grayscale image with the multiple time point templates to determine multiple types of identification points and the corresponding identification information.
[0010] Optionally, the identification information includes the time of the corresponding identification point; obtaining the identification points and the corresponding identification information includes: Based on the identification information, determine whether the time of the identification point belongs to a preset delay period; When the time of the identification point belongs to the delay period, delete the identification point and the corresponding identification information, obtain the latest futures image, and obtain the latest identification points and the corresponding identification information based on the latest futures image and the time point template until the time of the latest identification point does not belong to the delay period.
[0011] Optionally, the futures image includes a futures price area and a non-futures price area, and the identification information includes the position and time of the corresponding identification point; Based on the identification points and the corresponding identification information, determining the target identification point includes: Determine the position and time of the identification point according to the corresponding identification information; Determine whether the position of the identification point is within the futures price area, and determine the identification points within the futures price area as valid identification points; Determine whether the time of the valid identification point belongs to a preset target time period, and determine the valid identification points belonging to the target time period as the target identification points.
[0012] Optionally, the position includes an abscissa and an ordinate, the abscissa represents time, and the ordinate represents the trading price; the futures price area corresponds to a first abscissa range and a first ordinate range, and the target time period corresponds to a second abscissa range and a second ordinate range; Correspondingly, determining whether the position of the identification point is within the futures price area and determining the identification points within the futures price area as valid identification points includes: Determine the identification points whose abscissa belongs to the first abscissa range and whose ordinate belongs to the first ordinate range as the valid identification points; Correspondingly, determining whether the time of the valid identification point belongs to a preset target time period and determining the valid identification points belonging to the target time period as the target identification points includes: Determine the valid identification points whose abscissa belongs to the second abscissa range and whose ordinate belongs to the second ordinate range as the target identification points.
[0013] Optionally, generating a monitoring result based on the futures image, the target identification points and the corresponding identification information includes: Generate a target identification point list based on the target identification points and the corresponding identification information; Perform a first drawing process on the target identification points in the futures image to obtain a target monitoring image; Generate the monitoring result based on the target identification point list and the target monitoring image.
[0014] Optionally, performing a first drawing process on the target identification points in the futures image to obtain a target monitoring image includes: Perform a second drawing process on the remaining valid identification points that are not target identification points in the futures image; wherein, the second drawing process is different from the first drawing process and is used to distinguish the remaining valid identification points from the target identification points.
[0015] Optionally, generating the monitoring result based on the target identification points and the target monitoring image includes: Match the data structure corresponding to the time period according to the list of target identification points, where the data structure is used to store the list of identification points for the corresponding time period; In the case that the data structure does not store the list of identification points, or the list of identification points stored in the data structure is included in the list of target identification points and is less than the list of target identification points, update the data structure based on the list of target identification points; In the case that the data structure has been updated, generate the monitoring result based on the list of target identification points and the target monitoring image.
[0016] Optionally, the futures time point monitoring method further includes: Obtain a configuration file, where the configuration file includes the push object; Determine a push template based on the push object; Generate the monitoring result based on the list of target identification points, the target monitoring image, and the push template; Send the monitoring result to the push object.
[0017] Another aspect of the embodiments of the present application provides a futures time point monitoring device, where the device includes: A first acquisition module, configured to acquire a futures image, where the futures image includes the trading price of a target object within a preset time; A second acquisition module, configured to acquire identification points and corresponding identification information based on the futures image and a pre-configured time point template, where the identification points represent the trading signals of the target object; A determination module, configured to determine target identification points based on the identification points and the corresponding identification information, where the target identification points represent the target trading signals of the target object; A generation module, configured to generate a monitoring result based on the futures image, the target identification points, and the corresponding identification information, where the monitoring result is used to display the target trading signals.
[0018] Another aspect of the embodiments of the present application provides a computer device, including: At least one processor; and A memory communicatively connected to the at least one processor; Wherein: the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method as described above.
[0019] Another aspect of the embodiments of the present application provides a computer-readable storage medium, where computer instructions are stored in the computer-readable storage medium, and when the computer instructions are executed by a processor, the method as described above is implemented.
[0020] Another aspect of the embodiments of the present application provides a computer program product, including a computer program which, when executed by a processor, implements the method as described above.
[0021] The embodiments of the present application adopting the above technical solutions may include the following advantages: Obtain a futures image including the trading prices of the target object within a preset time, match the futures image with a pre-configured time point template to obtain identification points representing trading signals and corresponding identification information. Based on the identification points and the corresponding identification information, determine the target identification points representing the target trading signals. Based on the futures image, the target identification points and the corresponding identification information, generate a monitoring result for displaying the target trading signals. It can be seen that the embodiments of the present application provide an automated futures time point monitoring method, which can achieve all-weather real-time batch futures time point monitoring without manual intervention, and can effectively improve the accuracy, timeliness and efficiency of futures time point monitoring. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The drawings exemplarily show embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The shown embodiments are only for illustrative purposes and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.
[0023] Figure 1 Schematically shows a flowchart of the futures time point monitoring method according to Embodiment 1 of the present application; Figure 2 Schematically shows Figure 1 a sub-step flowchart of step S100 in Figure 3 Schematically shows Figure 1 a sub-step flowchart of step S100 in Figure 4 Schematically shows the template matching according to Embodiment 1 of the present application; Figure 5 Schematically shows Figure 1 a sub-step flowchart of step S102 in Figure 6 Schematically shows Figure 1 a sub-step flowchart of step S102 in Figure 7 Schematically shows Figure 1 a sub-step flowchart of step S104 in Figure 8 Schematically shows Figure 1 a sub-step flowchart of step S106 in Figure 9 Schematically shows a target monitoring image according to Embodiment 1 of the present application; Figure 10 Schematically shows Figure 8 a sub-step flowchart of step S804 in; Figure 11 Schematically shows a monitoring result according to Embodiment 1 of the present application; Figure 12 Schematically shows a monitoring result according to Embodiment 1 of the present application; Figure 13 Schematically shows an overall flowchart of a futures time point monitoring method according to Embodiment 1 of the present application; Figure 14 Schematically shows a block diagram of a futures time point monitoring device according to Embodiment 2 of the present application; and Figure 15 Schematically shows a schematic diagram of the hardware architecture of a computer device according to Embodiment 3 of the present application. Detailed implementation manners
[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.
[0025] It should be noted that the descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions appears to be contradictory or unable to be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present application.
[0026] In the description of the present application, it should be understood that the numerical labels before the steps do not identify the order of execution of the steps, but are only used to facilitate the description of the present application and distinguish each step, and thus cannot be understood as a limitation to the present application.
[0027] First, provide the following explanations of the terms involved in the present application: BGR (Blue, Green, Red): In the BGR format, the color channels of an image are arranged in the order of blue, green, and red.
[0028] RGB (Red, Green, Blue): In the RGB format, the color channels of an image are arranged in the order of red, green, and blue.
[0029] OpenCV (Open Source Computer Vision Library): An open-source computer vision library.
[0030] matchTemplate: A function in OpenCV used to perform template matching operations.
[0031] HTML (HyperText Markup Language): Hypertext Markup Language.
[0032] MACD (Moving Average Convergence Divergence): A trend-tracking indicator based on moving averages, used to measure the relationship between short-term and long-term price changes.
[0033] RSI (Relative Strength Index): Used to evaluate the speed and magnitude of price changes, and to determine whether the market is overbought or oversold.
[0034] The embodiments of the present application provide a technical solution for futures time point monitoring. In this technical solution, AI vision algorithms are used to fuse automated scripts for fully automated futures time point monitoring, which has the following advantages: (1) Automatic capture programs are separately deployed for different materials, enabling accurate capture of key information and matching it to the corresponding material categories, improving monitoring accuracy (statistics of monitoring results in the past month show that the accuracy rate can reach 100%); (2) The program performs high-frequency image signal capture and time point detection on each monitoring object during the trading period. It only takes about 2.3 seconds from capturing the image to sending out a notification (generating monitoring results), meeting the real-time requirements of business applications. (3) After the program is deployed, it runs automatically in the background without any manual intervention, which can improve the overall automation level and monitoring quality. (4) It realizes all-weather real-time batch monitoring, can capture multiple time points (trading signals), and improves the monitoring effect through timely information push. This technical solution overcomes the following problems: (1) Manual monitoring requires observing six time point signals of more than a dozen categories of materials simultaneously and pushing them to different procurement and supply chain roles, which is extremely error-prone and the monitoring results are inaccurate. (2) Due to the diversity of monitored material categories, time point signals, and push objects, as well as the uncontrollable factors of numerous manual operations, futures time point information cannot be captured in a timely and real-time manner, let alone notify the corresponding roles in a timely manner, resulting in low monitoring timeliness. (3) When manually monitoring futures time points for numerous materials, the trading periods of the materials are different, and at any moment of the day, different materials may be in the trading period. The switching rules are complex, very time-consuming, and inefficient, with high monitoring costs. See the following for details.
[0035] The technical solution of the present application will be introduced through multiple embodiments below. It should be noted that these embodiments can be implemented in various different forms and should not be construed as being limited only to the embodiments described herein.
[0036] Embodiment 1 Figure 1 The flowchart of the futures time point monitoring method according to Embodiment 1 of the present application is schematically shown.
[0037] As Figure 1 shown, the futures time point monitoring method may include steps S100 to S106, where: Step S100, obtain a futures image, where the futures image includes the trading price of the target object within a preset time.
[0038] Step S102, based on the futures image and a pre-configured time point template, obtain identification points and corresponding identification information, where the identification points represent the trading signals of the target object.
[0039] Step S104: Based on the identification points and the corresponding identification information, determine the target identification points, where the target identification points represent the target trading signals of the target object.
[0040] Step S106: Based on the futures image, the target identification points and the corresponding identification information, generate a monitoring result, where the monitoring result is used to display the target trading signals.
[0041] The futures time point monitoring method provided in this embodiment obtains a futures image including the trading prices of a target object within a preset time, matches the futures image with a pre-configured time point template, and obtains identification points representing trading signals and the corresponding identification information. Based on the identification points and the corresponding identification information, determine the target identification points representing the target trading signals. Based on the futures image, the target identification points and the corresponding identification information, generate a monitoring result for displaying the target trading signals. It can be seen that the embodiment of the present application provides an automated futures time point monitoring method, which can achieve all-weather real-time batch futures time point monitoring without manual intervention, and can effectively improve the accuracy, timeliness and efficiency of futures time point monitoring.
[0042] The following Figure 1 and Figure 13 are used to elaborate in detail each step in steps S100 - S106 and other optional steps.
[0043] Step S100 , obtain a futures image, where the futures image includes the trading prices of a target object within a preset time.
[0044] The target object can be any type of material, such as industrial silicon, glass, aluminum materials, etc. used in photovoltaic manufacturing, steel, rubber, batteries, etc. used in automotive manufacturing, cement, sand, steel bars, etc. used in the construction industry. The futures image can display the trading prices of the target object within a preset time, such as: the trading price of industrial silicon within 24 hours, the trading price of glass within 3 days. The solution for obtaining the futures image will be introduced below.
[0045] First, prepare the operating environment, including both hardware and software components. In terms of hardware, the hardware environment for all programs, software, and services to run can be defined. For example, install a server (physical or virtual machine) with an 8-core CPU and 16GB of memory, install the Windows 2019 operating system, configure the language environment, and ensure the availability of a remote uninterrupted access channel. In terms of software, the software environment for all programs, software, and services to run can be defined. For example, configure the permission to access the public network to ensure the normal operation of futures software, email push service, and internal communication software push service; install environment management tools such as miniforge3 / anaconda and create a virtual environment, and install specific version toolkits of open-source software such as Python, OpenCV, numpy, exchangelib, http, and pyautogui in the environment; install futures software and keep the front-end running, and obtain futures images through an automated AI program.
[0046] In an alternative embodiment, as Figure 2 shown, step S100 may include: Step S200, obtain a configuration file, where the configuration file includes the type identifier of the target object.
[0047] Step S202, determine the trading period of the target object according to the type identifier.
[0048] Step S204, obtain the current time and determine whether the current time belongs to the trading period.
[0049] Step S206, obtain the futures image when the current time belongs to the trading period.
[0050] The configuration file may include relevant information of the target object, such as: type identifier, Chinese name, English name, etc. Obtain the configuration file, and the content of the configuration file can be read according to the predefined format and rules to obtain the type identifier of the target object. According to the read type identifier and the predefined rules (such as business rules), the trading period of the target object can be determined. The trading period can be recorded at the hour and minute levels and converted into a timestamp. Obtain the current timestamp (the latest system timestamp). If the current timestamp is within the trading period, the futures image can be obtained. If the current timestamp is not within the trading period, the latest system timestamp can be obtained again after waiting for a period of time (such as 5s) for judgment.
[0051] In this embodiment, by reading the configuration file, it is possible to automatically determine whether the target object is currently in the trading period and obtain the futures image in real time, which can improve timeliness and accuracy and optimize the monitoring process.
[0052] In an alternative embodiment, asFigure 3 As shown in Figure 3 , step S100 may include: Step S300, take a screenshot of the futures software interface to obtain an initial image, and the color channels of the initial image are sorted in the first order type.
[0053] Step S302, adjust the sorting of the color channels of the initial image to obtain the futures image, and the color channels of the futures image are sorted in the second order type; wherein, the first order type includes RGB, and the second order type includes BGR.
[0054] Exemplarily, the program can automatically perform a screenshot operation on the futures software interface to obtain an initial image in pillow format (RGB). Convert the initial image into an array in numpy format, and the sorting of the color channels can be adjusted. Convert it from RGB format to BGR format used by OpenCV to obtain the futures image.
[0055] In this embodiment, by adjusting the sorting of the color channels, a futures image convenient for subsequent algorithm inference and drawing can be obtained.
[0056] Step S102 , based on the futures image and a pre-configured time point template, obtain identification points and corresponding identification information, and the identification points represent the trading signals of the target object.
[0057] The time point can include various types of identification points, and different types of identification points can represent different trading signals. Exemplarily, the identification points can include point B, point S, upward arrow, downward arrow, point B + upward arrow, point S + downward arrow, etc. Among them, point B is a buying point, indicating a suitable time to buy, and prompting traders to consider opening a long position. Point S is a selling point, indicating a suitable time to sell, indicating that traders can consider opening a short position or closing a position. The upward arrow indicates that the market trend is upward, predicting that the price may rise. The downward arrow indicates that the market trend is downward, predicting that the price may fall. The simultaneous appearance of point B + upward arrow represents a strong buying signal. The simultaneous appearance of point S + downward arrow represents a strong selling signal. The time point template can be pre-configured for identifying the identification points included in the futures image. The following provides an exemplary solution.
[0058] In an alternative embodiment, the time point template can be configured through the following operations: Step S400, intercept multiple types of identification points on the futures software interface to obtain multiple screenshots.
[0059] Step S402: Generate multiple time-point templates based on the multiple screenshots, where one time-point template corresponds to one type of identification point; each of the time-point templates is associated with a corresponding matching threshold, and the matching threshold is used to obtain, in the futures image, the identification points whose similarity to the corresponding time-point template exceeds the matching threshold.
[0060] Exemplarily, each type of identification point can be captured in the futures software interface to obtain multiple screenshots. Multiple time-point templates can be generated based on the multiple screenshots, where one time-point template corresponds to one type of identification point, as Figure 4 shown. According to the results of prior development and testing or actual business requirements, a matching threshold can be set for each time-point template, such as 0.7 - 0.8, at which time the matching effect is optimal. The matching threshold is used to obtain, in the futures image, the identification points whose similarity to the corresponding time-point template exceeds the matching threshold. In practical applications, the matching threshold can be adjusted according to different device resolutions, image qualities, template qualities, etc.
[0061] In this embodiment, by presetting multiple time-point templates and setting the matching threshold, efficient and accurate template matching can be achieved. The matching threshold can also be automatically adjusted according to different environments, further improving the matching accuracy and adaptability.
[0062] In an alternative embodiment, as Figure 5 shown, step S102 may include: Step S500: Determine the invalid area of the futures image and cut off the invalid area to obtain an intermediate image.
[0063] Step S502: Adjust the number of color channels of the intermediate image to convert the intermediate image into a grayscale image.
[0064] Step S504: Match the grayscale image with the multiple time-point templates to determine multiple types of identification points and the corresponding identification information.
[0065] To improve the accuracy and efficiency of algorithm inference, further image processing can be performed on the futures image. Exemplarily, the invalid area of the futures image (such as the task bar with a width of 40 pixels at the bottom) can be identified to obtain an intermediate image. Adjusting the number of color channels of the intermediate image to convert it from a BGR-format image to a grayscale image can improve the recognition effect. Read multiple time-point templates and also convert them to grayscale images. Use the matchTemplate toolkit for template matching and traverse the matching results. When there is a recognition result and the similarity to the time-point template is greater than the matching threshold, multiple types of identification points and the corresponding identification information can be determined. Among them, the identification information may include the type, confidence level, central abscissa, central ordinate, etc. of the corresponding identification point. The central abscissa represents time, and the central ordinate represents the trading price.
[0066] In this embodiment, by preprocessing and grayscale processing the futures image, the template matching effect can be improved. Through template matching, various types of identification points and corresponding identification information can be efficiently identified.
[0067] In an alternative embodiment, as Figure 6 shown, step S102 may include: Step S600, based on the identification information, determine whether the time of the identification point belongs to a preset delay period.
[0068] Step S602, in the case where the time of the identification point belongs to the delay period, delete the identification point and the corresponding identification information, obtain the latest futures image, and obtain the latest identification point and the corresponding identification information based on the latest futures image and the time point template until the time of the latest identification point does not belong to the delay period.
[0069] In practical applications, the futures software is updated every certain period of time (such as 15 minutes), and there may be a certain delay (such as 5s), resulting in data lag, which affects the monitoring accuracy. Therefore, a delay judgment can be made on the obtained identification points. For example: based on the identification information, determine the time of the identification point. Judge whether the time of the identification point is within a preset delay period (for example, the first 5 seconds of every 15 minutes at the whole hour). If the identification point is within the delay period, delete the identification point and the corresponding identification information. Re-obtain the latest futures image, and perform matching based on the latest futures image and the time point template to obtain the latest identification point and the corresponding identification information. Judge whether the time of the latest identification point belongs to the delay period. If it does not belong, step S104 can be entered; otherwise, the steps of deletion, re-obtaining, and judgment will be repeatedly executed until the time of the latest identification point does not belong to the delay period.
[0070] In this embodiment, through delay judgment, real-time identification points are obtained to ensure the timeliness and accuracy of monitoring and avoid errors caused by data lag.
[0071] Step S104 , based on the identification point and the corresponding identification information, determine the target identification point, and the target identification point represents the target trading signal of the target object.
[0072] In practical applications, on the basis of showing the trading prices of the target object within a preset time, the futures image will also show relevant analysis information, such as trading volume, open interest, technical indicators (such as MACD, RSI, etc.). These analysis information can also be mis-matched as identification points, resulting in deviations in the monitoring results. Therefore, to improve the accuracy of the monitoring results, based on the identification points and the corresponding identification information, the target identification points can be determined. The target identification points represent the actually required, correct and effective trading signals (i.e., target trading signals), such as: point B, point S, upward arrow, downward arrow, point B + upward arrow, point S + downward arrow, etc. The following provides an exemplary solution.
[0073] In an alternative embodiment, as Figure 7 shown, step S104 may include: Step S700, determine the position and time of the identification point according to the corresponding identification information.
[0074] Step S702, determine whether the position of the identification point is within the futures price area, and determine the identification points within the futures price area as valid identification points.
[0075] Step S704, determine whether the time of the valid identification point belongs to a preset target period, and determine the valid identification points belonging to the target period as the target identification points.
[0076] In practical applications, the futures image may include a futures price area and a non-futures price area. Among them, the identification points located in the non-futures price area are invalid identification points and need to be excluded. The identification information may include the position of the identification point. Determine whether the position of the identification point is within the futures price area, and determine the identification points within the futures price area as valid identification points. To ensure the real-time nature of the monitoring results, based on the time of the identification point, it can also be determined whether the identification point belongs to the current period (target period) or the historical period. The identification information may also include the time of the identification point. Determine whether the time of the identification point belongs to the target period, and determine the valid identification points belonging to the target period as the target identification points.
[0077] In this embodiment, by excluding invalid identification points and determining whether the valid identification points belong to the current period or the historical period, the target identification points are finally obtained, improving the accuracy and real-time nature of the monitoring results.
[0078] In an alternative embodiment, the position may include a (central) abscissa and a (central) ordinate. The abscissa may represent time, and the ordinate may represent the trading price. The futures price region may correspond to a first abscissa range (e.g., 80 - 1670) and a first ordinate range (e.g., 77 - 537). The target time period may correspond to a second abscissa range (e.g., the proportion of the total image width is 80% - 83.3%) and a second ordinate range (e.g., the proportion of the total image height is 8% - 53.9%). Correspondingly, step S702 may include: determining the identification points whose abscissa belongs to the first abscissa range and whose ordinate belongs to the first ordinate range as the valid identification points. Correspondingly, step S704 may include: determining the valid identification points whose abscissa belongs to the second abscissa range and whose ordinate belongs to the second ordinate range as the target identification points.
[0079] Exemplarily, traverse the identification points and the corresponding identification information, perform region judgment, and eliminate the invalid identification points in non - futures price regions. For example, determine the identification points with a central abscissa less than 80, or a central abscissa greater than 1670, or a central ordinate less than 77, or a central ordinate greater than 537 as the invalid identification points in non - futures price regions. Determine the identification points with a central abscissa greater than 80 and less than 1670, and a central ordinate greater than 77 and less than 537 as the valid identification points in the futures price region. Traverse the valid identification points in the futures price region, and distinguish the identification points of the current time period (i.e., the target identification points) from the identification points of the historical time period. For example, determine the identification points with the proportion of the central abscissa in the range of 80% - 83.3% of the width of the total image (futures image) and the proportion of the central ordinate in the range of 8% - 53.9% of the height of the total image as the newly emerged identification points of the current time period, and determine them as the target identification points.
[0080] In this embodiment, through precise region recognition and time period judgment, eliminate the invalid identification points and distinguish the target identification points belonging to the current time period, further improving the timeliness of the monitoring results.
[0081] Step S106 , generate a monitoring result based on the futures image, the target identification points, and the corresponding identification information, and the monitoring result is used to display the target trading signal.
[0082] Exemplarily, it is possible to label the target identification points in the futures image, and generate a table, a list, etc. based on the target identification points and the corresponding identification information. Package the labeled futures image and the ordered target identification points and the corresponding identification information to generate a monitoring result for pushing, and visually display the target trading signal in the form of pictures and text, etc., improving the monitoring effect and efficiency. The following provides an exemplary solution.
[0083] In an alternative embodiment, such asFigure 8 As shown, step S106 may include: Step S800, generating a list of target identification points based on the target identification points and the corresponding identification information.
[0084] Step S802, performing a first drawing process on the target identification points in the futures image to obtain a target monitoring image.
[0085] Step S804, generating the monitoring result based on the list of target identification points and the target monitoring image.
[0086] Exemplarily, a list of target identification points may be generated based on the target identification points and the corresponding identification information. Perform a first drawing process on the target identification points in the futures image. For example, draw a red and white frame and a vertical projection line of the abscissa for the target identification points, as Figure 9 shown, to efficiently prompt the target trading signal. Based on the list of target identification points and the target monitoring image, the monitoring result can be generated. In some embodiments, the target monitoring image may be stored in a specific local directory of the server for subsequent push calls and evidence retention. Since the program will frequently store the algorithm inference result images locally and the server memory is limited, a timed cleaning task can be set to ensure the normal and stable operation of the program. Obtain the current timestamp and determine whether it is within a preset cleaning period (such as the 60 seconds before zero o'clock on Monday every week). If it is within the cleaning period, empty all the images under the image storage path and empty the system recycle bin to reduce memory occupancy.
[0087] In this embodiment, by drawing and annotating the target identification points, the monitoring result is visualized, providing a better monitoring experience.
[0088] In an alternative embodiment, step S802 may include: performing a second drawing process on the remaining valid identification points of the non-target identification points in the futures image; wherein, the second drawing process is different from the first drawing process and is used to distinguish the remaining valid identification points from the target identification points.
[0089] Exemplarily, as Figure 9 shown, a second drawing process may be performed on the remaining valid identification points of the non-target identification points in the futures image. For example, draw a white frame and a vertical projection line of the abscissa for the target identification points to distinguish the target identification points from the remaining valid identification points.
[0090] In this embodiment, by drawing and annotating and distinguishing the target identification points from the remaining valid identification points, wherein the target identification points represent the real-time monitoring result, while the remaining valid identification points can help observe the historical trend, the monitoring result is further optimized.
[0091] In an alternative embodiment, as Figure 10As shown, step S804 may include: Step S1000, according to the target identification point list, match the data structure for the corresponding time period, where the data structure is used to store the identification point list for the corresponding time period.
[0092] Step S1002, when the data structure does not store the identification point list, or when the identification point list stored in the data structure is included in the target identification point list and is less than the target identification point list, update the data structure based on the target identification point list.
[0093] Step S1004, when the data structure has been updated, generate the monitoring result based on the target identification point list and the target monitoring image.
[0094] The data structure can be a key-value pair, linked list, queue, etc. Taking the key-value pair (in the form of a Python dictionary) as an example, the key-value pair for the corresponding time period can be matched based on the target identification point list. For example, every 15 minutes at the whole hour can be recorded as a time period, which serves as the unique key value of the dictionary, and the corresponding value is the identification point list for the corresponding time period. Querying the value means traversing the identification point list recognized in the corresponding time period. When the value is empty, it indicates that the identification point list has not appeared before, so the target identification point list can be used as the value and a monitoring result push signal can be sent. When the value is equal to or includes the target identification point list, it means that the target identification point list has appeared alone or in combination. When the value is included in the target identification point list and is less than the target identification point list, it indicates that a new target identification point has appeared, and the original value can be replaced with the target identification point list and a message push signal can be sent.
[0095] In this embodiment, repeated judgment is performed through the data structure to avoid pushing duplicate monitoring results and further improve the monitoring experience.
[0096] In an alternative embodiment, the futures monitoring method may further include: Step S1100, obtain a configuration file, where the configuration file includes the push object.
[0097] Step S1102, determine the push template based on the push object.
[0098] Step S1104, generate the monitoring result based on the target identification point list, the target monitoring image, and the push template.
[0099] Step S1106, send the monitoring result to the push object.
[0100] Exemplarily, a configuration file associated with the target object can be obtained, and the type identifier, push Chinese name, push objects (such as email sending user group, email carbon copy user group, communication software push user group, etc.) of the target object can be read from it. Based on the push objects, a personalized push template can be determined, and the push template can include a text description of the monitoring result. For example: when "Point B / Point S / up arrow / down arrow" appears independently, the text description of the monitoring result can be "When Point B / Point S / up arrow / down arrow appears, there is a significant upward / downward / slight upward / slight downward trend in the material price to a large extent"; when "Point B + up arrow", "Point S + down arrow" appear in combination, the text description of the monitoring result can be "The material price already has a possible upward / downward trend". Based on the target identification point list, the target monitoring image, and the push template, a monitoring result can be generated, such as Figure 11 and Figure 12 as shown. The monitoring result can be sent to the push objects by means such as emails and communication software messages. For example, according to the account password applied offline, a request credential is set, and this credential is used to connect to the Exchange mailbox account and configure relevant information. The monitoring result is organized into an HTML format, where the target monitoring image can be used as an embedded attachment to form a complete email content. Another example is to pre-build an internal communication software push interface in advance, and organize the monitoring result into a complete request message to send a push request.
[0101] Embodiment 2 Figure 14 FIG. schematically shows a block diagram of a futures time point monitoring device according to Embodiment 2 of the present application. The device can be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the embodiments of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. As Figure 14 shown, the device 1000 may include: a first acquisition module 1100, a second acquisition module 1200, a determination module 1300, and a generation module 1400, where: The first acquisition module 1100 is configured to acquire a futures image, where the futures image includes the trading price of the target object within a preset time; The second acquisition module 1200 is configured to acquire identification points and corresponding identification information based on the futures image and a pre-configured time point template, where the identification points represent the trading signals of the target object; The determination module 1300 is configured to determine a target identification point based on the identification points and the corresponding identification information, where the target identification point represents the target trading signal of the target object; A generation module 1400, configured to generate a monitoring result based on the futures image, the target identification points, and the corresponding identification information, where the monitoring result is used to display the target trading signal.
[0102] As an optional embodiment, obtaining a futures image includes: Obtaining a configuration file, where the configuration file includes a type identifier of a target object; Determining a trading period of the target object according to the type identifier; Obtaining the current time and determining whether the current time belongs to the trading period; When the current time belongs to the trading period, obtaining the futures image.
[0103] As an optional embodiment, obtaining a futures image includes: Performing a screenshot operation on a futures software interface to obtain an initial image, where color channels of the initial image are sorted in a first order type; Adjusting the color channel sorting of the initial image to obtain the futures image, where color channels of the futures image are sorted in a second order type; Wherein, the first order type includes RGB, and the second order type includes BGR.
[0104] As an optional embodiment, the time point template is configured through the following operations: Taking multiple types of identification points on a futures software interface to obtain multiple screenshots; Generating multiple time point templates based on the multiple screenshots, where one time point template corresponds to one type of identification point; Wherein, each of the time point templates is associated with a corresponding matching threshold, and the matching threshold is used to obtain identification points in the futures image whose similarity to the corresponding time point template exceeds the matching threshold.
[0105] As an optional embodiment, based on the futures image and a pre-configured time point template, obtaining identification points and corresponding identification information includes: Determining an invalid area of the futures image and cutting off the invalid area to obtain an intermediate image; Adjusting the number of color channels of the intermediate image to convert the intermediate image into a grayscale image; Matching the grayscale image with the multiple time point templates to determine multiple types of identification points and corresponding identification information.
[0106] As an optional embodiment, the identification information includes the time of the corresponding identification point; obtaining identification points and corresponding identification information includes: Based on the identification information, determine whether the time of the identification point belongs to a preset delay period; When the time of the identification point belongs to the delay period, delete the identification point and the corresponding identification information, obtain the latest futures image, and obtain the latest identification point and the corresponding identification information based on the latest futures image and the time point template until the time of the latest identification point does not belong to the delay period.
[0107] As an optional embodiment, the futures image includes a futures price area and a non-futures price area, and the identification information includes the position and time of the corresponding identification point; Based on the identification point and the corresponding identification information, determine the target identification point, including: According to the corresponding identification information, determine the position and time of the identification point; Determine whether the position of the identification point is located in the futures price area, and determine the identification point located in the futures price area as a valid identification point; Determine whether the time of the valid identification point belongs to a preset target period, and determine the valid identification point belonging to the target period as the target identification point.
[0108] As an optional embodiment, the position includes an abscissa and an ordinate, the abscissa represents time, and the ordinate represents the trading price; the futures price area corresponds to a first abscissa range and a first ordinate range, and the target period corresponds to a second abscissa range and a second ordinate range; Correspondingly, determine whether the position of the identification point is located in the futures price area, and determine the identification point located in the futures price area as a valid identification point, including: Determine the identification point whose abscissa belongs to the first abscissa range and whose ordinate belongs to the first ordinate range as the valid identification point; Correspondingly, determine whether the time of the valid identification point belongs to a preset target period, and determine the valid identification point belonging to the target period as the target identification point, including: Determine the valid identification point whose abscissa belongs to the second abscissa range and whose ordinate belongs to the second ordinate range as the target identification point.
[0109] As an optional embodiment, based on the futures image, the target identification point and the corresponding identification information, generate a monitoring result, including: Generate a target identification point list based on the target identification point and the corresponding identification information; Perform a first drawing process on the target identification point in the futures image to obtain a target monitoring image; Generate the monitoring result based on the list of target identification points and the target monitoring image.
[0110] As an optional embodiment, perform a first drawing process on the target identification points in the futures image to obtain a target monitoring image, including: Perform a second drawing process on the remaining valid identification points of non-target identification points in the futures image; wherein, the second drawing process is different from the first drawing process and is used to distinguish the remaining valid identification points from the target identification points.
[0111] As an optional embodiment, generate the monitoring result based on the target identification points and the target monitoring image, including: Match the data structure corresponding to the time period according to the list of target identification points, and the data structure is used to store the list of identification points corresponding to the time period; In the case where the data structure does not store the list of identification points, or the list of identification points stored in the data structure is included in the list of target identification points and is less than the list of target identification points, update the data structure based on the list of target identification points; In the case where the data structure has been updated, generate the monitoring result based on the list of target identification points and the target monitoring image.
[0112] As an optional embodiment, the apparatus 1000 is further configured to: Obtain a configuration file, and the configuration file includes a push object; Determine a push template based on the push object; Generate the monitoring result based on the list of target identification points, the target monitoring image, and the push template; Send the monitoring result to the push object.
[0113] Embodiment III Figure 15 Schematically shows a hardware architecture diagram of a computer device 10000 suitable for implementing the futures time point monitoring method according to Embodiment III of the present application. In some embodiments, the computer device 10000 may be a terminal device such as a smart phone, a wearable device, a tablet computer, a personal computer, a vehicle-mounted terminal, a game console, a virtual device, a workbench, a digital assistant, a set-top box, a robot, etc. In other embodiments, the computer device 10000 may be a rack-mounted server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of multiple servers). As Figure 15 shown, the computer device 10000 includes, but is not limited to: a memory 10010, a processor 10020, and a network interface 10030 that can communicate with each other through a system bus. Among them: The memory 10010 includes at least one type of computer-readable storage medium. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 10010 may be an internal storage module of the computer device 10000, such as the hard disk or memory of the computer device 10000. In other embodiments, the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the computer device 10000. Of course, the memory 10010 may also include both the internal storage module and the external storage device of the computer device 10000. In this embodiment, the memory 10010 is generally used to store the operating system and various application software installed on the computer device 10000, such as the program code of the futures time point monitoring method. In addition, the memory 10010 may also be used to temporarily store various data that have been output or will be output.
[0114] In some embodiments, the processor 10020 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other chip. The processor 10020 is generally used to control the overall operation of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is used to run the program code stored in the memory 10010 or process data.
[0115] The network interface 10030 may include a wireless network interface or a wired network interface, which is generally used to establish a communication link between the computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal via a network, and establish a data transmission channel and a communication link between the computer device 10000 and the external terminal. The network may be a wireless or wired network such as an enterprise intranet (Intranet), the Internet, Global System of Mobile communication (GSM for short), Wideband Code Division Multiple Access (WCDMA for short), 4G network, 5G network, Bluetooth, Wi-Fi, etc.
[0116] It should be noted that Figure 15 only the computer device with components 10010 - 10030 is shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.
[0117] In this embodiment, the futures time point monitoring method stored in the memory 10010 can also be divided into one or more program modules and executed by one or more processors (such as the processor 10020) to complete the embodiments of the present application.
[0118] Embodiment 4 The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the futures time point monitoring method in the embodiments are implemented.
[0119] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the computer device. Of course, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device, such as the program code of the futures point monitoring method in the embodiment. In addition, the computer-readable storage medium can also be used to temporarily store various data that have been output or will be output.
[0120] Embodiment Five The embodiment of the present application also provides a computer program product, including a computer program, which when executed by a processor implements the method in the above embodiment.
[0121] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the embodiments of the present application can be implemented by a general-purpose computer device. They can be concentrated on a single computer device or distributed on a network composed of multiple computer devices. Optionally, they can be implemented by program codes executable by the computer device, so that they can be stored in a storage device and executed by the computer device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.
[0122] It should be noted that the above are only the preferred embodiments of the present application, and do not limit the patent protection scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.
Claims
1. A futures time point monitoring method, characterized in that: The method comprises: Acquire a futures image, wherein the futures image includes a transaction price of a target object within a preset time; Based on the futures image and a pre-configured time point template, obtaining a marking point and corresponding marking information, wherein the marking point represents a trading signal of the target object; Based on the identification point and the corresponding identification information, determining a target identification point, the target identification point representing a target transaction signal of the target object; Based on the futures image, the target identification point and the corresponding identification information, a monitoring result is generated, and the monitoring result is used to display the target trading signal.
2. The method according to claim 1, characterized in that Get futures images, including: Obtaining a configuration file, wherein the configuration file includes a type identifier of a target object; Determining a transaction period of the target object according to the type identifier; Obtaining the current time, and determining whether the current time belongs to the trading period; In a case where the current time belongs to the trading period, the futures image is acquired.
3. The method according to claim 1, characterized in that Get futures images, including: Taking a screenshot of the futures software interface to obtain an initial image, wherein the color channels of the initial image are sorted according to the first order type; adjusting the color channel order of the initial image to obtain the futures image, wherein the color channels of the futures image are ordered according to a second order type; The first sequence type includes RGB, and the second sequence type includes BGR.
4. The method according to claim 1, characterized in that: The time point template is configured by the following operations: Capture multiple types of identification points on the futures software interface to obtain multiple screenshots; Generate multiple time point templates based on the multiple screenshots, one time point template corresponds to one type of identification point; Each of the time point templates is associated with a corresponding matching threshold, and the matching threshold is used to obtain identification points in the futures image whose similarity with the corresponding time point template exceeds the matching threshold.
5. The method according to claim 4, characterized in that Based on the futures image and the pre-configured time point template, the identification point and the corresponding identification information are obtained, including: Determine an invalid area of the futures image, and cut off the invalid area to obtain an intermediate image; Adjusting the number of color channels of the intermediate image to convert the intermediate image into a grayscale image; The grayscale image is matched with the multiple time point templates to determine multiple types of identification points and corresponding identification information.
6. The method according to claim 1, characterized in that The identification information includes the time corresponding to the identification point; Get the identification point and the corresponding identification information, including: Based on the identification information, determining whether the time of the identification point belongs to a preset delay period; When the time of the identification point belongs to the delay period, the identification point and the corresponding identification information are deleted, the latest futures image is obtained, and the latest identification point and the corresponding identification information are obtained based on the latest futures image and the time point template, until the time of the latest identification point does not belong to the delay period.
7. The method according to any one of claims 1 to 6, characterized in that: The futures image includes a futures price area and a non-futures price area, and the identification information includes a position and time of a corresponding identification point; Determining a target identification point based on the identification point and the corresponding identification information includes: Determine the position and time of the marked point according to the corresponding identification information; Determine whether the position of the identification point is located in the futures price area, and determine the identification point located in the futures price area as a valid identification point; Determine whether the time of the valid identification point belongs to a preset target time period, and determine the valid identification point belonging to the target time period as the target identification point.
8. The method according to claim 7, characterized in that The position includes an abscissa and a ordinate, the abscissa represents time, and the ordinate represents transaction price; the futures price area corresponds to a first abscissa range and a first ordinate range, and the target period corresponds to a second abscissa range and a second ordinate range; Correspondingly, determining whether the position of the identification point is located in the futures price area, and determining the identification point located in the futures price area as a valid identification point includes: Determine the identification point whose abscissa belongs to the first abscissa range and whose ordinate belongs to the first ordinate range as the valid identification point; Correspondingly, determining whether the time of the valid identification point belongs to a preset target time period, and determining the valid identification point belonging to the target time period as the target identification point includes: The valid identification point whose horizontal coordinate belongs to the second horizontal coordinate range and whose vertical coordinate belongs to the second vertical coordinate range is determined as the target identification point.
9. The method according to claim 7, characterized in that: Based on the futures image, the target identification point and the corresponding identification information, a monitoring result is generated, including: Based on the target identification points and the corresponding identification information, generating a target identification point list; Performing a first drawing process on the target identification point in the futures image to obtain a target monitoring image; The monitoring result is generated based on the target identification point list and the target monitoring image.
10. The method according to claim 9, characterized in that Performing a first drawing process on the target identification point in the futures image to obtain a target monitoring image includes: A second drawing process is performed on the remaining valid identification points that are not target identification points in the futures image; wherein the second drawing process is different from the first drawing process and is used to distinguish the remaining valid identification points from the target identification points.
11. The method according to claim 9, characterized in that Generating the monitoring result based on the target identification point and the target monitoring image includes: According to the target identification point list, a data structure corresponding to the time period is matched, and the data structure is used to store the identification point list corresponding to the time period; In a case where the data structure does not store a list of identification points, or the list of identification points stored in the data structure is included in the target identification point list and is less than the target identification point list, updating the data structure based on the target identification point list; When the data structure has been updated, the monitoring result is generated based on the target identification point list and the target monitoring image.
12. The method according to claim 9, characterized in that Also includes: Obtain a configuration file, wherein the configuration file includes a push object; Based on the push object, determine a push template; Generate the monitoring result based on the target identification point list, the target monitoring image and the push template; The monitoring result is sent to the push object.
13. A futures time point monitoring device, characterized in that: The device comprises: A first acquisition module, used to acquire a futures image, wherein the futures image includes a transaction price of a target object within a preset time; A second acquisition module is used to acquire a marking point and corresponding marking information based on the futures image and a pre-configured time point template, wherein the marking point represents a trading signal of the target object; A determination module, configured to determine a target identification point based on the identification point and the corresponding identification information, wherein the target identification point represents a target transaction signal of the target object; A generation module is used to generate a monitoring result based on the futures image, the target identification point and the corresponding identification information, and the monitoring result is used to display the target trading signal.
14. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 12.
15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 12 is implemented.
16. A computer program product comprising a computer program, 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 12 are implemented.