Image data storage method, device and computer equipment

Abstract

The present application relates to an image data storage method, device and computer equipment. The method comprises: obtaining a cleaning strategy for image data according to the storage time and associated information of the image data in the storage module, and cleaning the image data based on the cleaning strategy; judging whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter among the storage cost, accessed information and storage data volume of the storage module; and adjusting the cleaning strategy when judging that the storage module has image data with unreasonable cleaning strategy. The use of this method can solve the problem of low data storage efficiency.

Description

Technical Field

[0001] The present application relates to the field of data storage, and in particular to an image data storage method, device and computer equipment. Background Art

[0002] As countries around the world compete to develop the digital economy, the application of intelligent capture equipment has been gradually promoted. However, with the increase in the number of capture devices, the amount of video image data has also skyrocketed, which has brought severe challenges to its management and value utilization.

[0003] Related technologies often aggregate multi-source data through data warehouses, form data assets through data classification and grading, and manage data assets in a hierarchical manner. However, this data storage management method is difficult to fully explore and apply the value of the data itself, and there is a problem of low data storage efficiency.

[0004] With regard to the problem of low data storage efficiency in related technologies, no effective solution has been proposed so far. Summary of the invention

[0005] Based on this, it is necessary to provide an image data storage method, device and computer equipment that can improve data storage efficiency in response to the above technical problems.

[0006] In a first aspect, an image data storage method is provided in this embodiment, the method comprising:

[0007] Acquire a cleaning strategy for image data corresponding to the storage time and associated information of the image data in the storage module, and clean up the image data based on the cleaning strategy;

[0008] Determining whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module;

[0009] When it is determined that the storage module has image data with an unreasonable cleaning strategy, the cleaning strategy is adjusted according to the analysis parameters.

[0010] In one of the embodiments, the cleanup policy includes a storage time threshold and / or a cleanup priority of the image data.

[0011] In one embodiment, judging whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module comprises:

[0012] Obtaining the storage cost of the storage module according to the total storage cost of the image data, the total amount of stored data, and the amount of stored data of the storage module;

[0013] It is determined whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to the storage cost.

[0014] In one embodiment, judging whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to the storage cost includes:

[0015] In combination with the storage income and the storage cost of the storage module, it is determined whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable.

[0016] In one embodiment, judging whether a cleaning strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module includes:

[0017] Acquire a first storage data volume of the storage module before cleaning the image data based on the cleaning strategy, and a second storage data volume of the storage module after cleaning the image data based on the cleaning strategy;

[0018] Whether a cleaning strategy corresponding to the image data in the storage module is reasonable is determined according to the first storage data volume and the second storage data volume.

[0019] In one embodiment, judging whether a cleaning strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module includes:

[0020] Whether a cleaning strategy corresponding to the image data in the storage module is reasonable is judged according to the access frequency and / or the access user information in the accessed information of the image data in the storage module.

[0021] In one embodiment, obtaining the associated information of the image data in the storage module includes:

[0022] classifying the image data according to the feature information of the image data;

[0023] The associated information of the image data is obtained according to the classification result.

[0024] In one of the embodiments, after cleaning the image data based on the cleaning strategy, the method further includes:

[0025] According to the access requirements of the image data in the storage module, the associated information of the image data is adjusted, and the associated information includes one or more of the following: storage index, cache status, and storage location.

[0026] In a second aspect, in this embodiment, an image data storage device is provided, the device comprising:

[0027] A cleaning module, used to obtain a cleaning strategy for image data corresponding to the storage time and associated information of the image data in the storage module;

[0028] An analysis module, used to determine whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module;

[0029] The adjustment module is used to adjust the cleaning strategy according to the analysis parameters when it is determined that the storage module has image data with an unreasonable cleaning strategy.

[0030] In a third aspect, a computer device is provided in this embodiment, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the image data storage method described in the first aspect is implemented.

[0031] The above-mentioned image data storage method, device and computer equipment clean up part of the image data through the associated information and storage time of the image data, and then analyze the remaining image data to adjust the cleaning strategy so that the storage module can store high-value image data and clean up low-value image data, thereby achieving the effect of improving data storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A diagram showing an application environment of an image data storage method in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of an image data storage method in one embodiment;

[0034] Figure 3 A schematic diagram of a process of a method for storing and managing video image data in one embodiment;

[0035] Figure 4 A schematic diagram of data warehouse table data interaction logic in one embodiment;

[0036] Figure 5 A schematic diagram of the execution of a video image data cleaning strategy in one embodiment;

[0037] Figure 6is a schematic diagram of a data governance system architecture in one embodiment;

[0038] Figure 7 is a structural block diagram of an image data storage device in one embodiment;

[0039] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0041] The image data storage method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Through the communication between the terminal and the server, the data in the data storage system is stored and data managed. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0042] In one embodiment, Figure 2 As shown, a method for storing image data is provided, which is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, and the following steps are included:

[0043] Step 202: Acquire a cleaning strategy for image data corresponding to the storage time and associated information of the image data in the storage module, and clean up the image data based on the cleaning strategy.

[0044] Among them, multiple storage modules can be set, and the amount of data stored in different storage modules can be different or the same, and the image data can be modularly stored through the storage modules. Optionally, the image data can be classified according to the quality of the image data, and the image data can be stored in different storage modules according to the classification results. It is understandable that the classification of image data can also be achieved in other ways based on the label of the image data.

[0045] The associated information may be labels such as source, quality, time, etc. corresponding to the image data, or subordinate information such as the company or department to which the image data belongs; storage locations such as image index and storage location; and other information such as the access object and access frequency of the image data. Optionally, one or more cleaning strategies corresponding to the image data may be obtained based on one or more labels of the image data. In the case of obtaining multiple cleaning strategies, the multiple cleaning strategies may be integrated, and the image data may be cleaned based on the integrated cleaning strategies and the storage time of the image data.

[0046] Optionally, the value of the image data can be determined based on the value of the analysis parameter, thereby adjusting the cleanup strategy. For example, if it is determined based on the value of the analysis parameter that the current image data has a large storage volume, a high storage cost, and a very low access frequency, then the image data cleanup strategy can be modified: the image data cleanup priority can be greatly increased and / or the corresponding storage time threshold can be greatly shortened, or the image data can be directly deleted, or the image data can be moved to a device with a lower storage cost; if it is determined based on the value of the analysis parameter that the current image data cost is relatively high and the image data value is relatively low, then the image data cleanup priority can be slightly increased and / or the corresponding storage time threshold can be slightly shortened.

[0047] The cleaning strategy is used to indicate image data that needs to be deleted and / or moved to an external target device. Optionally, cleaning image data includes deleting image data in a storage module to free up storage space, and may also include adjusting the storage location of image data, such as transferring image data with a high cleaning priority from a current storage module to a target device. The cleaning strategy may be a pre-set strategy, or a strategy obtained by adjusting a pre-set strategy based on analysis parameters.

[0048] Step 204 , judging whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module.

[0049] The storage cost refers to the cost required to maintain the storage of the image data in the current storage module. The storage cost of the storage module can be the storage cost of the storage module after the cleanup strategy cleans up the image data, or it can be the storage cost change curve of the storage module in a specified time period. The accessed information is the access information of the image data in the storage module in a specified time period, including the accessed object, the access frequency, etc. The amount of stored data can be the amount of stored data in the storage module after the cleanup strategy cleans up the image data, or it can be the storage data amount change curve of the storage module in another specified time period.

[0050] Optionally, when it is determined that any one or more of the following situations: the storage cost of the storage module is too high, the access frequency is low, and the amount of stored data is too large, it can be determined that the remaining image data in the storage module needs to be cleaned up and the corresponding cleaning strategy is unreasonable. Furthermore, when it is determined that the remaining image data in the storage module needs to be cleaned up and the corresponding cleaning strategy is unreasonable, the image data can be sampled and tested, and combined with the sampling test results to determine whether the judgment result of the rationality of the cleaning strategy needs to be modified, so as to improve the judgment accuracy.

[0051] Step 206: When it is determined that the storage module has image data with an unreasonable cleaning strategy, the cleaning strategy is adjusted according to the analysis parameters.

[0052] Among them, if by comparing the storage cost of the storage module in the current cycle with the storage cost in the historical cycle, it is determined that the cleaning strategy executed in the current cycle is unreasonable and the corresponding storage efficiency is low, then the cleaning strategy can be adjusted; if by comparing the storage cost of the storage module in the current cycle with the storage cost in the historical cycle, it is determined that the cleaning strategy executed in the current cycle is reasonable and the corresponding storage efficiency is high, then the cleaning strategy may not be adjusted. It can be understood that in the next cycle, the cleaning strategy of the image data is obtained according to the storage time and associated information of the image data in the storage module, which is the adjusted cleaning strategy for the current cycle.

[0053] Optionally, adjusting the cleanup strategy includes but is not limited to: adjusting the cleanup priority of image data that needs to be cleaned and / or the maximum storage time in the storage module; obtaining associated information of image data whose cleanup strategy is unreasonable, and adjusting the cleanup priority of other image data corresponding to the associated information and / or the maximum storage time in the storage module.

[0054] In the above-mentioned image data storage method, a cleaning strategy is obtained through the association information and storage time of the image data, and based on the cleaning strategy, some image data with low importance can be cleaned up to improve the data storage efficiency. Subsequently, the value of the remaining image data is analyzed through one or more dimensions of storage cost, accessed information, and storage data volume, and the cleaning strategy is adjusted according to the analysis results, so that the storage module can store high-value image data and clean up low-value image data, further improving the data storage efficiency.

[0055] In one embodiment, the cleanup policy includes: a storage time threshold of the image data and / or a cleanup priority.

[0056] Each associated information corresponds to a preset storage time threshold and / or cleaning priority. Optionally, the storage time threshold and the cleaning priority are set correspondingly, and the higher the storage time threshold, the lower the cleaning priority of the image data; conversely, the higher the cleaning priority of the image data. The storage time threshold and the cleaning priority of the image data can also be set and modified separately based on needs.

[0057] Optionally, a longer storage time threshold can be set for high-value, core data to achieve long-term storage, while a lower cleanup priority can be set. A shorter storage time threshold can be set for non-core data to achieve short-term storage, while a higher cleanup priority can be set. In particular, when the associated information of the image data changes, the cleanup strategy can also be adjusted. After the non-core data determined according to the associated information is adjusted to core data, the storage period and governance priority of this part of the image data can be adjusted from short-term storage and high cleanup priority to long-term storage and low cleanup priority.

[0058] The method for cleaning up the image data in the storage module includes: deleting the image data with a high cleaning priority in the storage module, migrating the image data with a high cleaning priority to a designated target device, and the like.

[0059] The corresponding relationship between the storage time threshold and the cleaning priority can be set according to user needs, and the cleaning strategy can be expressed as a cleaning priority configuration table. Optionally, when the associated information is a user type, the following cleaning priority configuration table 1 can be obtained according to the user type:

[0060] Table 1

[0061] User Type Storage time threshold Cleanup Priority Core users 36500 days Low Demo User 180 days Low Operational users 180 days middle test 60 days high Development 30 days high

[0062] Among them, when the storage time threshold is 36500 days, it means that the image data is stored for 100 years, which is almost permanent storage, so it is assigned a low cleanup priority; when the storage time threshold is 180 days, it is assigned a medium cleanup priority; when the storage time threshold is 60 days or 30 days, the corresponding time is short, so it is assigned a low cleanup priority.

[0063] When the associated information is image quality, the following cleaning priority configuration table 2 can be obtained according to the image quality:

[0064] Table 2

[0065] Image Quality Storage time threshold Cleanup Priority high 36500 days Low middle 365 days middle Low 180 days high

[0066] When the associated information is the module where the image data is located, the corresponding module category can be determined according to the module name of the storage module, thereby obtaining the following cleaning priority configuration table 3:

[0067] Table 3

[0068] Module Name Module Category Storage time threshold Cleanup Priority Module A core 36500 days Low Module B important 365 days Low Module C edge 180 days middle Module D Deprecated 7 days high

[0069] When the image data includes multiple related information, the storage time thresholds corresponding to the multiple related information can be combined by weighted average, addition, and other mathematical processing methods, and the cleaning priority can be obtained according to the storage time threshold obtained after the combination. It can be understood that the related information can also be data of other dimensions, such as image tags, image access frequency, etc., which will not be elaborated here.

[0070] Optionally, when cleaning image data based on a cleaning strategy, image data with a high cleaning priority can be used as image data that needs to be cleaned, and / or a storage time threshold and the storage time of the image data are compared, and image data with a storage time exceeding the storage time threshold is used as image data that needs to be cleaned. Alternatively, the storage time of image data with a high cleaning priority is obtained, the storage time of the image data is compared with the storage time threshold, and the high-priority image data that needs to be cleaned is determined based on the comparison result. Among them, if the storage time of the image data exceeds the storage time threshold, the cleaning strategy is that the image data can be cleaned; if the storage time of the image data does not exceed the storage time threshold, the cleaning strategy is that the image data does not need to be cleaned.

[0071] In this embodiment, the cleaning priority of the image data is obtained through the associated information of the image data, so as to achieve the effect of judging the importance of the image data from one or more dimensions, and improve the storage efficiency of the storage module by cleaning the corresponding image data.

[0072] In one embodiment, judging whether a cleanup strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module includes: obtaining the storage cost of the storage module based on the total storage cost of the image data, the total storage data volume, and the amount of storage data in the storage module; and judging whether a cleanup strategy corresponding to the remaining image data in the storage module is reasonable based on the storage cost.

[0073] The total storage cost of image data refers to the cost required to maintain the data storage in each storage module; the total storage data volume is the total amount of data stored in each storage module. Optionally, the storage cost of image data of a preset storage unit is obtained based on the total storage cost and the total storage data volume of the current image data; the storage cost of the storage module in the current cycle is obtained based on the storage cost of the storage unit and the number of preset storage units included in the storage module.

[0074] Optionally, it can be determined whether the storage cost is greater than the preset cost. If the storage cost is greater than the preset cost, it is determined that the remaining image data in the storage module needs to be cleaned up, and there are image data in the storage module with an unreasonable cleaning strategy. Alternatively, a storage cost change curve before and after the execution of the cleaning strategy can be obtained, and based on the change curve, it can be determined whether the cleaning strategy has achieved the expected goal. If not, it is determined that there is a need to clean up the remaining image data in the storage module, and there are image data in the storage module with an unreasonable cleaning strategy. Alternatively, the future cost change trend can be analyzed based on the storage cost change curve. If the future cost change trend cannot meet the expected goal, there are image data in the storage module with an unreasonable cleaning strategy. Among them, in the case that there are image data in the storage module with an unreasonable cleaning strategy, the cleaning strategy can be adjusted by increasing the cleaning priority of the cleaning strategy corresponding to the image data, and / or reducing the corresponding storage cycle.

[0075] In this embodiment, whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable is determined based on the dimension of storage cost, thereby avoiding the waste of storage resources by the storage module with high storage cost, thereby achieving the effect of improving storage efficiency.

[0076] Further, in one embodiment, the method for determining whether a cleanup strategy corresponding to the remaining image data in the storage module is reasonable based on the storage cost also includes: determining whether a cleanup strategy corresponding to the remaining image data in the storage module is reasonable in combination with the storage revenue of the storage module and the storage cost.

[0077] Among them, based on the preset standards, the revenue, business opportunities and other information brought by the image data in the storage module are converted into designated data, and the data obtained after the conversion is used as storage income. Optionally, for data with high storage cost and low income, the cleaning strategy can be appropriately adjusted to reduce its storage cycle and / or increase the cleaning priority; for data with low storage cost and high income, the cleaning strategy can be appropriately adjusted to increase its storage cycle and / or reduce the cleaning priority, so that the data stored in the storage module are all high-value data. The rationality of the cleaning strategy is judged by combining storage income and storage cost, so that the judgment of whether the cleaning strategy corresponding to the image data is reasonable is more accurate.

[0078] In one embodiment, judging whether a cleanup strategy corresponding to image data in a storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module includes: obtaining a first storage data volume of the storage module before cleaning the image data based on the cleanup strategy, and a second storage data volume of the storage module after cleaning the image data based on the cleanup strategy; judging whether the cleanup strategy corresponding to the image data in the storage module is reasonable based on the first storage data volume and the second storage data volume.

[0079] The first storage data volume may be the data volume at one moment before the image data is cleaned, or the data volume at multiple moments before the image data is cleaned. Optionally, by performing data operations such as subtraction and multiplication between the first storage data volume and the second storage data volume, or predicting the change trend of the future storage data volume based on the first storage data volume and the second storage data volume, the storage cost that can be saved by the cleaning strategy is determined based on the data operation results and / or the change trend.

[0080] Optionally, if the difference between the first storage data volume and the second storage data volume is large, the storage cost that can be saved by the cleaning strategy is large; if the difference between the first storage data volume and the second storage data volume is small, or it is predicted that the storage data volume will hardly change in the future, the storage cost that can be saved by the current cleaning strategy is small, so it can be judged that there may be a situation where the cleaning strategy is unreasonable. Further, in order to improve the judgment result, the change in the storage data volume before and after the multiple executions of the cleaning strategy is obtained. If it is judged that the difference between the first storage data volume and the second storage data volume is less than the specified value before and after the multiple cleaning strategies, it can be judged that there is a situation where the cleaning strategy is unreasonable. Furthermore, when the difference between the first storage data volume and the second storage data volume is small, a prompt message can be generated to the user, so that the user can view the cleaning strategy of the image data according to the prompt message, and adjust the cleaning strategy according to the query result. In this embodiment, the cost saved before and after the execution of the cleaning strategy is analyzed based on the first storage data volume and the second storage data volume, so as to judge whether there is a need to adjust the cleaning strategy of the stored data.

[0081] Furthermore, the second storage data volume can be directly obtained. When the second storage data volume is larger than the preset storage data volume, and the difference between the second storage data volume and the preset storage data volume is larger than a specified value, a prompt message is generated to the user, so that the user can verify whether there is a possibility of accidental deletion of data based on the prompt message. If it is determined that the data is accidentally deleted after verification, the accidentally deleted data is re-imported.

[0082] In one embodiment, judging whether a cleanup strategy corresponding to image data in the storage module is reasonable based on at least one analysis parameter among the storage cost, accessed information, and storage data volume of the storage module includes: judging whether a cleanup strategy corresponding to image data in the storage module is reasonable based on the access frequency and / or access user information in the accessed information of the image data in the storage module.

[0083] The accessed information is information within a specified time period, and the specified time period can be a period set by the user such as a week or a month. The higher the access frequency of the image data, the lower the possibility that the image data in the storage module needs to be cleaned up; conversely, the higher the possibility. Optionally, the relationship between the access frequency and the image data cleanup priority and / or storage time threshold is preset. If the access frequency and the corresponding image data cleanup priority and / or storage time threshold do not match, it is determined that the cleanup strategy corresponding to the image data is unreasonable.

[0084] The more important and the higher the priority of the user information accessing the image data, the lower the possibility that the image data in the storage module needs to be cleaned up; conversely, the higher the possibility. Optionally, the relationship between the access user information and the image data cleanup priority and / or storage time threshold is preset, and if the access frequency and the corresponding image data cleanup priority and / or storage time threshold do not match, it is determined that the cleanup strategy corresponding to the image data is unreasonable.

[0085] Optionally, the importance of the accessed user and the access frequency are combined to determine whether the cleaning strategy corresponding to the image data in the storage module is reasonable. The corresponding judgment criteria can be set in advance according to the information of the accessed user and the access frequency. For example, the corresponding score can be obtained based on the priority of the accessed user, and the value of the access frequency and the score can be combined to achieve a quantitative judgment on whether the cleaning strategy corresponding to the image data in the storage module is reasonable.

[0086] It is understandable that the judgment of whether the cleaning strategy corresponding to the image data in the storage module is reasonable can be realized by combining multiple analysis parameters such as storage cost, accessed information, and storage data volume. For example, the corresponding scores are obtained according to multiple analysis parameters such as storage cost, accessed information, and storage data volume, and the judgment of whether the cleaning strategy corresponding to the image data in the storage module is reasonable is realized according to the total score.

[0087] Furthermore, based on one or more analysis parameters including storage cost, accessed information, and storage data volume, after determining that the cleanup strategy corresponding to the image data in the storage module is unreasonable, the storage time threshold of this part of the image data can be obtained in combination with the associated information. After obtaining image data with excessively high storage cost based on the storage cost, it can be determined whether the cleanup strategy of the image data is reasonable in combination with the storage time threshold of the image data. For example, when the corresponding storage cost is high, but the image storage time threshold is higher than a specified value, it is determined that the cleanup priority of the image data is high, and the judgment result is modified to increase the cleanup priority in the cleanup strategy corresponding to the image data, and this part of the image data is used as the image data to be cleaned. The other analysis parameter combination methods are similar and will not be elaborated here.

[0088] Furthermore, before judging whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter among the storage cost, access information, and storage data volume of the storage module, it is also possible to obtain part of the image data in the current storage unit by sampling, and analyze the sampled image data in the storage unit based on at least one analysis parameter among the storage cost, access information, and storage data volume of the partial image data to judge whether the cleaning strategy corresponding to the image data is reasonable. In the case where the cleaning strategy corresponding to the sampled image data is unreasonable, the step of judging whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter among the storage cost, access information, and storage data volume of the storage module is performed. Among them, the method of analyzing the sampled image data in the storage unit based on at least one analysis parameter is the same as the method of judging whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable in the above-mentioned embodiment, and will not be repeated here.

[0089] In one embodiment, acquiring the associated information of the image data in the storage module includes: classifying the image data according to the feature information of the image data; and obtaining the associated information of the image data according to the classification result.

[0090] Among them, the characteristic information of the image includes but is not limited to the original characteristics of the image itself, such as the size, resolution, and color of the image. Optionally, the image quality is classified according to the size and pixels of the image data, wherein the more pixels the image data of the same size contains, the higher the image quality; conversely, the lower the image quality. The quality of the image data is used as the association information of the image data. Images of different classifications are stored in different storage modules. Alternatively, the images can also be classified according to the source of the image data to obtain the label of the module to which the image belongs, for example, the image is labeled with the module A or module B according to the source of the image data, and the label is used as the association information. In this embodiment, the classification of the image is realized according to the characteristic information of the image, and the association information of the image is obtained, thereby improving the organization and management methods of the image data and enhancing the analysis ability of the image data.

[0091] In one embodiment, after cleaning the image data based on the cleaning strategy, the method further includes: adjusting the associated information of the image data according to the access requirements of the image data in the storage module, the associated information including one or more of the following: storage index, cache status, storage location.

[0092] The access demand can be obtained based on the access frequency of the image data and the importance of the accessing user. If the access frequency of the image data is high, the access demand is high; if the importance of the accessing user is high, the access demand is high.

[0093] Optionally, in an instant query scenario, one or more associated information in the storage index, cache state, and storage location of data with high access demand is adjusted to achieve one or more of the following effects: adding storage indexes, analyzing and calculating data in advance and caching analysis results, and temporarily storing data on a fast-access storage medium. In this embodiment, fast access and analysis of data can be achieved by adjusting associated information based on access demand.

[0094] In one embodiment, Figure 3 A video image data storage management method is provided, which runs through the entire life cycle of the video image data and mainly includes the following steps:

[0095] Step 301, data collection and management.

[0096] Among them, in data collection and management, the video image data is verified, the equipment and images are labeled, and the data is compressed and transmitted to the data warehouse.

[0097] The data verification method includes: for the device captured data stored in external storage, the accuracy and integrity of the original video image data are improved through data verification such as the capture time and capture device, length verification, etc., to ensure the quality of the original video image data stored in the data warehouse. Data verification can improve the accuracy and integrity of the data and ensure the quality of data entering the warehouse.

[0098] For the raw data that has been verified, the artificial intelligence algorithm is used to perform labeling operations. The device and image labeling methods include but are not limited to: extracting the original information such as the size and pixels of the image, judging the quality score of the image and labeling it with high, medium, and low quality scores; labeling the image with storage module A and storage module B to indicate storage ownership based on the image data source. Labeling data through artificial intelligence algorithms enriches data feature information and empowers the value of the data itself.

[0099] For the labeled data, the data is compressed and the compressed video image data is transmitted to the data warehouse. Through data compression technology, the amount of data transmitted can be saved and the efficiency of data transmission can be improved.

[0100] Step 302, data storage management.

[0101] Figure 4 is a schematic diagram of the data warehouse table data interaction logic in this embodiment, such as Figure 4As shown in the figure, the original data stored in the external ODS layer (Operational Data Store, original data layer) is collected and managed after data verification, and the data is collected and stored in the data warehouse. When storing in the data warehouse, hierarchical storage can be implemented to meet the needs of massive data aggregation storage and real-time query, as follows:

[0102] The DWD layer (Data Warehouse Detail) is used to store the original video image data that is transmitted to the data warehouse after data collection and management.

[0103] The DIM layer (Dimension, public dimension layer) is used to store dimension table data in dimensional modeling. Specifically, the DIM layer can store non-repetitive high-cardinality dimension tables: such as user information, product information tables, etc.; it can also store simple low-cardinality dimension tables: such as enumeration mapping tables, time configuration tables, data governance policies, etc.

[0104] The DWS layer (Data Warehouse Service, summary data layer) is used to store data summary query results. Among them, DWS can generate multi-field wide tables based on the time, region, user and other dimension tables in the DIM layer to provide subsequent business queries and OLAP (On Line Analytical Processing) online analysis.

[0105] Optionally, the video image data in the DWD layer is classified based on the related information such as departments, enterprises, users, tags, etc. in the DIM dimension table, and then summarized according to dimensions such as time to aggregate data into the DWS layer, thereby meeting multi-dimensional aggregation statistical needs such as obtaining the image data volume of the equipment owned by the department this month, the number of captured video images of the company this quarter, the number of captured images of red light running this month, etc.

[0106] ADS (Application Data Service): Data application layer, mainly based on the needs of actual business queries, saves statistical report data. Among them, the DWS layer and ADS layer are also used to realize the storage of real-time analysis results of data governance and the reporting of governance efficiency. Optionally, based on the user's data acquisition needs, based on the data stored in the DWS layer, it is aggregated and summarized by data theme into user query reports for export, and the report data is stored in the ADS layer.

[0107] Furthermore, data storage cleaning can be implemented according to the various data cleaning strategies provided by the DIM layer to provide support for the value analysis of video image data. Among them, the various data cleaning strategies provided by the DIM layer may include TTL-based data cleaning strategies, multi-dimensional data cleaning strategies based on associated information, and emergency data cleaning strategies.

[0108] Figure 5 FIG. 1 is a schematic diagram of the execution of the video image data cleaning strategy in this embodiment. Figure 5 As shown in the figure, after performing data warehouse collection management (data collection management based on data warehouse) and data warehouse tiered storage (data collection management based on data warehouse), configure the TTL (Time to Live, data survival time) management strategy. Among them, TTL is used to specify a certain survival time for stored data. When the survival time reaches the specified storage period, that is, the above storage time threshold, the stored data will be deleted or transferred. The configuration of the TTL management strategy can be implemented through ClickHouse. Taking the storage time threshold of 356 days as an example, the operation is as follows:

[0109] ALTER TABLE my_table MODIFY TTL event_time+ INTERVAL 365 DAY

[0110] Among them, the above operation is used to modify the expiration time configuration of the my_table table through ClickHouse, and specify the expiration time of the event_time column data as 365 days, that is, the data in the event_time column of the my_table table will be automatically deleted 365 days after insertion.

[0111] The TTL governance strategy can be used to simply and quickly determine whether the stored data is low-value table data with a high cleanup priority, such as non-core operation log tables, and configure TTL for the table data to achieve automatic cleanup.

[0112] After completing the TTL governance policy configuration, the data governance policy is executed based on multi-dimensional data such as departments, enterprises, users, tags, etc. Executing the data governance policy based on multi-dimensional data can obtain the storage period of the video image data, that is, the storage time threshold in the above embodiment, and the cleaning priority.

[0113] Based on the multi-dimensional data governance strategy, determine whether the video image data is data with a low storage cycle and a high cleaning priority. Among them, the multi-dimensional data governance strategy based on associated information includes: configuring the corresponding storage cycle and cleaning priority according to the associated information such as departments, enterprises, users, and tags, and obtaining the cleaning priority configuration table corresponding to each associated information, so as to achieve a coarse-grained prediction of the importance of the data and determine the value of the stored data. Optionally, a corresponding enumeration value can be obtained for the cleaning priority mapping. For example, the low cleaning priority enumeration value is set to 1; the medium cleaning priority enumeration value is set to 2; and the high cleaning priority enumeration value is set to 3. After obtaining the cleaning priority configuration table, the image data priority can also be manually reviewed. The cleaning priority configuration table data is associated with the corresponding video image data and stored, and written to the DWS layer through offline calculation, in preparation for the subsequent storage cycle and cleaning priority of the associated stored video image data.

[0114] If the video image data is judged to be data with a low storage period and a high cleaning priority based on the multi-dimensional data governance strategy, a quick screening and deletion operation can be performed, or data deletion can be achieved based on the emergency data governance strategy. If the video image data is judged not to be data with a low storage period and a high cleaning priority based on the multi-dimensional data governance strategy, it is determined whether the video image data is data with a long storage period and a low cleaning priority.

[0115] Optionally, a sampling check is used to determine whether the video image data is data with a long storage period and a low cleaning priority. If so, whether the TTL cleaning priority configuration is reasonable is determined. Optionally, the data can be analyzed by means of data governance effectiveness analysis, and it is determined whether the data in the cleaning priority configuration table of data with a long storage period and a low cleaning priority needs to be modified based on the analysis results. At the same time, after determining that the video image data is data with a long storage period and a low cleaning priority by means of a sampling check, it can also be determined whether the data with a long storage period and a low cleaning priority occupies a large storage space. If the video image data occupies a large storage space at the same time, a hot and cold tiered storage strategy is implemented; if the video image data does not occupy a large storage space, a data governance efficiency analysis can be performed. If not, the video image data does not belong to data with a long storage period and a low cleaning priority, and the cleaning strategy is implemented by modifying the data in the cleaning priority configuration table, or directly deleting this part of the data.

[0116] Among them, the method of deleting data includes: deleting the original data first through the transaction processing control method, and then deleting the metadata in the data warehouse, so as to avoid the problem of data loss indirectly caused by data deletion failure.

[0117] The emergency data governance strategy includes: rapid emergency cleanup of data with low storage cycle, high cleanup priority and large storage space, so as to quickly release space and enhance system availability.

[0118] The hot and cold tiered storage strategy includes: creating a storage area for hot and cold data, setting a corresponding storage strategy during data table creation or data writing, and migrating video data that occupies a large storage space and is not frequently accessed, such as face capture data, from the original storage area to the specified target device based on the storage strategy. Among them, cold storage can be performed on a pay-as-you-go basis, such as object storage. For data with a high access frequency, hot storage is achieved through memory or solid-state drives with high storage costs. Specifically, the configuration of the hot and cold tiered storage strategy can be implemented through Doris, and configuration can be performed by creating resources, creating strategies, and storage policy binding tables.

[0119] Step 303: data query management.

[0120] Data query governance includes the governance of access methods to different stored data based on query scenarios, and the governance of access methods to stored data with different access frequencies.

[0121] Among them, governance based on the access methods of storage data with different access frequencies includes: analyzing the query logs of video image data to obtain the user access frequency of video image data, and further analyzing it in combination with the cleanup priority configuration table related to user types obtained during data storage governance. For example, data with high access frequency for core users is labeled with high attention; data with low access frequency constructed by non-core users such as operations, testing, and development users is labeled with low attention. Data with low attention tags can be included in the goals of emergency data governance strategies in the future to clean up and release storage space in a timely manner.

[0122] Governance of access methods for different stored data based on query scenarios includes: when querying data at the DWS layer in real time, adding indexes, caching methods, pre-calculations and other big data processing technologies to the hierarchical storage data to achieve real-time data query. In report-type statistical analysis query scenarios, the query content includes but is not limited to statistical data storage volume of the image that occupies the largest space, the minimum width of the image, and statistical data on the average pixel value of the image. Fast statistical aggregation analysis can be achieved based on the data storage governance strategy of the data warehouse and combined with the table aggregation capabilities of the big data component. In large-capacity and high-bandwidth query scenarios, the query content includes but is not limited to querying high-definition video and streaming content, which are stored and accessed through object storage.

[0123] Step 304: data governance effectiveness analysis.

[0124] Among them, data governance effectiveness analysis supports governance supervision management and multi-dimensional analysis such as storage data stock / increment analysis, storage cost analysis and estimation, and data governance efficiency analysis.

[0125] Optionally, the data governance effectiveness analysis includes the following aspects:

[0126] 1. Conduct statistical analysis and forecast on the storage volume of video image data, calculate the growth rate and growth trend of statistical data by day, week, month, half year, year and other periods, and estimate the total amount and trend of future growth.

[0127] 2. Perform a coarse-grained calculation of the cost of storing video image data. Use the coarse-grained calculation rule of "cost of a single storage unit = total cost / total data volume" to calculate the storage cost corresponding to the data volume of a single storage unit. Use the coarse-grained calculation rule of "submodule storage cost = cost of each storage unit The calculation method of "sub-module data volume" is used to make a coarse-grained estimate of the data storage cost under the dimensions of department, enterprise, user, and tag. According to the associated information of the dimensions such as department, enterprise, user, and module, the total amount and increment of the stock cost of the data are analyzed, and the corresponding cost ranking display is added to urge users to pay more attention to data governance and improve the efficiency of data storage.

[0128] Furthermore, the value of data can be entered based on project revenue, customer business opportunity leads and other dimensions. The value of data may change with the promotion and operation of the project, and relevant tags can be sorted out at the end or closing stage of the project. Furthermore, the value of data can be adjusted and analyzed.

[0129] 3. To analyze the TTL execution status through TTL execution logs, it is necessary to record the data storage volume and storage cost information before and after the execution, so as to display the effectiveness of data governance before and after with intuitive data indicators.

[0130] 4. Display the performance data of each dimension of data governance in real time and output the data governance performance report.

[0131] Furthermore, based on the storage management method of the video image data in this embodiment, a data management system can be obtained. Figure 6 Schematic diagram of the data governance system architecture in this embodiment. Figure 6 As shown, it includes a data warehouse for realizing data storage, which governs data collection, data storage and data query methods through governance strategies, and governs the data in the data warehouse based on the data collection and data query methods, and governance effectiveness analysis can also be performed based on the data governance strategies.

[0132] In related technologies, although centralized management of data is achieved, it is difficult to further explore and utilize the potential value of data. The following challenges are faced: decentralized data storage means that there is no unified governance standard for data governance; it is difficult to quickly mine data of target value; and the effectiveness of data governance is not supported by corresponding data indicators.

[0133] The method in this embodiment reads the metadata of video image data through the data collection and management method, and then extracts the characteristic information of the video image such as quality, size, repetition rate, and application label through data labeling processing by artificial intelligence algorithm. The collected and managed data is stored in layers.

[0134] In data storage management, by proposing data management strategies and combining the department, enterprise, user, module, and tag information of the image source device, the value and cleaning priority of the video image data are pre-determined, and emergency data management strategies are used to help users quickly filter out low-value data that takes up a lot of space, and use and clean up low-value data. This solves the problem of decentralized data storage, which makes it difficult to quickly mine data of target value due to the lack of unified management standards for data management.

[0135] Through the data governance efficiency analysis and management method, the storage and cost dimensions of data are analyzed and estimated for stock and / or increment. The observability of data governance is improved; real-time monitoring and feedback on the progress of data governance are provided, which effectively improves the efficiency of data governance and helps users to efficiently predict and control costs, solving the problem that the efficiency of data governance is not supported by corresponding data indicators.

[0136] Based on the same inventive concept, the embodiment of the present application also provides an image data storage device for implementing the above-mentioned image data storage method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more image data storage device embodiments provided below can refer to the limitations on the image data storage method above, and will not be repeated here.

[0137] In one embodiment, Figure 7 As shown, an image data storage device is provided, comprising: a cleaning module, an analysis module and an adjustment module, wherein:

[0138] A cleaning module, used to obtain a cleaning strategy for the image data corresponding to the storage time and associated information of the image data in the storage module, and clean the image data based on the cleaning strategy;

[0139] An analysis module, used to determine whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module;

[0140] The adjustment module is used to adjust the cleaning strategy according to the analysis parameters when it is determined that the cleaning strategy corresponding to the image data in the storage module is unreasonable.

[0141] Furthermore, in one embodiment, the analysis module can obtain the data value of the image data in multiple dimensions such as project revenue and customer business opportunity clues after determining whether the cleanup strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module. The data value may change with the promotion and operation progress of the project. At the end or closing stage of the project, relevant tags can be sorted out to extract high-yield valuable data, and then the value of the data can be further adjusted and analyzed. Optionally, the cleanup strategy of the image data includes: storage time threshold and / or cleanup priority.

[0142] In one embodiment, the analysis module determines whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module, including: obtaining the storage cost of the storage module based on the total storage cost of the image data, the total storage data volume, and the amount of storage data in the storage module; and determining whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on the storage cost. Optionally, the analysis module determines whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on the storage cost, including: determining whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on the storage revenue and storage cost of the storage module.

[0143] Optionally, the analysis module determines whether a cleanup strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module, including: obtaining a first storage data volume of the storage module before cleaning the image data based on the cleanup strategy, and a second storage data volume of the storage module after cleaning the image data based on the cleanup strategy; and determining whether the cleanup strategy corresponding to the image data in the storage module is reasonable based on the first storage data volume and the second storage data volume.

[0144] Optionally, the analysis module determines whether the cleanup strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module, including: determining whether the cleanup strategy corresponding to the image data in the storage module is reasonable based on the access frequency and / or access user information in the accessed information of the image data in the storage module.

[0145] In one embodiment, the image data storage device further includes a collection module for acquiring associated information of the image data in the storage module, specifically including: classifying the image data according to feature information of the image data; and obtaining associated information of the image data according to the classification result.

[0146] In one of the embodiments, the image data storage device also includes a query management module, which is used to clean up the image data based on the cleanup strategy. The method also includes: adjusting the associated information of the image data according to the access requirements of the image data in the storage module, and the associated information includes one or more of the following: storage index, cache status, and storage location.

[0147] Each module in the above-mentioned image data storage device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0148] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store image data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an image data storage method is implemented.

[0149] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0150] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0151] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0152] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0153] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0154] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for storing image data, characterized in that: The method comprises: Acquire a cleaning strategy for image data corresponding to the storage time and associated information of the image data in the storage module, and clean up the image data based on the cleaning strategy; Determining whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module; In the case where it is determined that the storage module has image data with an unreasonable cleaning strategy, the cleaning strategy is adjusted according to the analysis parameter; wherein obtaining the associated information of the image data in the storage module includes: classifying the image data according to the feature information of the image data; and obtaining the associated information of the image data according to the classification result; After cleaning the image data based on the cleaning strategy, the method also includes analyzing the stored image data stock or increment and the storage cost of the stored image data to obtain data governance efficiency data, display the data governance efficiency data in each dimension and output a data governance efficiency report.

2. The image data storage method according to claim 1, characterized in that: The cleaning policy includes a storage time threshold and / or a cleaning priority of the image data.

3. The image data storage method according to claim 1, characterized in that: The step of judging whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, the accessed information, and the amount of stored data in the storage module comprises: Obtaining the storage cost of the storage module according to the total storage cost of the image data, the total amount of stored data, and the amount of stored data of the storage module; It is determined whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to the storage cost.

4. The image data storage method according to claim 3, characterized in that: The determining, based on the storage cost, whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable includes: In combination with the storage income and the storage cost of the storage module, it is determined whether the cleaning strategy corresponding to the remaining image data in the storage module is reasonable.

5. The image data storage method according to claim 1, characterized in that: The step of judging whether a cleaning strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, the accessed information, and the amount of stored data in the storage module comprises: Acquire a first storage data volume of the storage module before cleaning the image data based on the cleaning strategy, and a second storage data volume of the storage module after cleaning the image data based on the cleaning strategy; Whether a cleaning strategy corresponding to the image data in the storage module is reasonable is determined according to a difference between the first storage data amount and the second storage data amount.

6. The image data storage method according to claim 1, characterized in that: The step of judging whether a cleaning strategy corresponding to the image data in the storage module is reasonable based on at least one analysis parameter of the storage cost, the accessed information, and the amount of stored data in the storage module comprises: Whether a cleaning strategy corresponding to the image data in the storage module is reasonable is judged according to the access frequency and / or the access user information in the accessed information of the image data in the storage module.

7. The image data storage method according to claim 1, characterized in that: After cleaning the image data based on the cleaning strategy, the method further includes: According to the access requirements of the image data in the storage module, the associated information of the image data is adjusted, and the associated information includes one or more of the following: storage index, cache status, and storage location.

8. An image data storage device, characterized in that: The device comprises: A cleaning module, used to obtain a cleaning strategy for image data corresponding to the storage time and associated information of the image data in the storage module; An analysis module, used to determine whether a cleaning strategy corresponding to the remaining image data in the storage module is reasonable according to at least one analysis parameter of the storage cost, accessed information, and storage data volume of the storage module; An adjustment module is used to adjust the cleaning strategy according to the analysis parameters when it is determined that the storage module contains image data with an unreasonable cleaning strategy; wherein, obtaining the associated information of the image data in the storage module includes: classifying the image data according to the characteristic information of the image data; obtaining the associated information of the image data according to the classification result; after cleaning the image data based on the cleaning strategy, the method also includes analyzing the stored image data stock or increment and the storage cost of the stored image data to obtain data governance efficiency data, displaying the data governance efficiency data in each dimension and outputting a data governance efficiency report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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