General data dictionary determination method and device, equipment, medium and program product

Abstract

The invention provides a general data dictionary determination method which can be applied to the technical field of big data in the financial field. The method comprises the steps of obtaining respective compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, and obtaining respective compression capability evaluation result groups of the M data dictionaries, the compression capability evaluation result represents the capability of compressing business data of business types contained in the financial data centers by using the data dictionaries, and the M data dictionaries are in one-to-one correspondence with the M financial data centers; based on the respective compression capability evaluation result groups of the M data dictionaries, determining respective general compression evaluation results of the M data dictionaries; and determining a general data dictionary from the M data dictionaries based on the respective general compression evaluation results of the M data dictionaries, and sending the general data dictionary to each financial data center. The invention further provides a general data dictionary determination device and equipment, a medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology in the financial field, and in particular to a method, device, equipment, medium and program product for determining a universal data dictionary. Background Art

[0002] With the deployment of multiple financial data centers, the demand for cross-financial data center interactions is increasing, and network traffic is also growing rapidly, increasing the operation and maintenance costs of financial data centers. In order to reduce the bandwidth pressure of financial data centers, the business data of one of the financial data centers is used to train the data dictionary, and the data dictionary is used to compress and decompress the network traffic of each financial data center.

[0003] In the process of realizing the concept disclosed in the present invention, the inventors found that there are at least the following problems in the related technology: since the business data of each financial data center belongs to different business types, the same data dictionary is used to compress the business data of different business types, resulting in poor compression effect of the business data; and there are different interaction requirements between multiple financial data centers. The same data dictionary is used to compress the interactive business data received from other financial data centers, which also results in poor compression effect of the business data. Therefore, the data dictionary obtained by the prior art has poor universality. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium and program product for determining a universal data dictionary.

[0005] According to a first aspect of the present disclosure, a method for determining a universal data dictionary is provided, comprising: obtaining compression capability evaluation results of M data dictionaries of the same compression type from each financial data center, and obtaining compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the capability of compressing business data of business categories contained in the financial data center using the data dictionaries, the M data dictionaries correspond one-to-one to the M financial data centers, each of the data dictionaries is trained using business data of business categories contained in the corresponding financial data center, and the business data of business categories contained in the M financial data centers are different; based on the compression capability evaluation result groups of the M data dictionaries, determining universal compression evaluation results of the M data dictionaries; based on the universal compression evaluation results of the M data dictionaries, determining a universal data dictionary from the M data dictionaries, and sending the universal data dictionary to each financial data center.

[0006] According to an embodiment of the present disclosure, the compression capability evaluation results of each of the M data dictionaries sent by each of the above-mentioned financial data centers are determined in the following manner: when the above-mentioned financial data center has the above-mentioned data dictionary, the above-mentioned data dictionary is used to perform compression capability evaluation on the business data of the business category stored in the above-mentioned financial data center to obtain an initial compression capability evaluation result; when the above-mentioned financial data center does not have the above-mentioned data dictionary, a predetermined compression capability evaluation result is used as the initial compression capability evaluation result of the above-mentioned data dictionary; the initial compression capability evaluation results of each of the M data dictionaries are optimized to obtain the above-mentioned compression capability evaluation results of each of the M data dictionaries.

[0007] According to an embodiment of the present disclosure, the above-mentioned general compression evaluation results of the M data dictionaries are determined based on the compression capability evaluation result groups of the M data dictionaries, including: obtaining the compression ratio of the data dictionary used to compress the business data of the business categories contained in the M data financial centers, and obtaining the compression ratio group of the data dictionary; evaluating the general compression capability of the M data dictionaries according to the compression capability evaluation result groups and compression ratio groups of the M data dictionaries, and obtaining the general compression evaluation results of the M data dictionaries.

[0008] According to an embodiment of the present disclosure, the general compression capabilities of the M data dictionaries are evaluated based on their respective compression capability evaluation result groups and compression ratio groups to obtain general compression evaluation results of the M data dictionaries, including: for each of the data dictionaries, calculating the product of the compression ratio and the compression capability evaluation result of each of the data dictionaries for the same financial data center to obtain the compression evaluation result of the data dictionary for the financial data center; and obtaining the general compression evaluation result of the data dictionaries based on the compression evaluation results for the M financial data centers.

[0009] According to an embodiment of the present disclosure, the compression ratio of the data dictionary used to compress the business data of the business categories contained in each of the M data financial centers is determined in the following manner: when the financial data center is equipped with the data dictionary, the business data of the business categories stored in the financial data center is processed using the data dictionary to obtain the compression ratio of the data dictionary; when the financial data center is not equipped with the data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary.

[0010] According to an embodiment of the present disclosure, based on the general compression evaluation results of each of the M data dictionaries, a general data dictionary is determined from the M data dictionaries, and the general data dictionary is sent to each of the financial data centers, including: comparing the general compression evaluation results of each of the M data dictionaries of the same type, determining the data dictionary with the highest general compression evaluation result as the general data dictionary of each of the financial data centers, and sending the general data dictionary to each of the financial data centers.

[0011] According to an embodiment of the present disclosure, each financial data center includes multiple general data dictionaries of different types; the above-mentioned general data dictionaries perform data compression in the following manner: determine the compression type of the data to be compressed; determine a target general data dictionary that matches the compression type of the above-mentioned data to be compressed from the multiple above-mentioned general data dictionaries; and compress the above-mentioned data to be compressed using the above-mentioned target general data dictionary.

[0012] A second aspect of the present disclosure provides a general data dictionary determination device, including: an evaluation result acquisition module, used to obtain the compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, and obtain compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress business data of the business category contained in the financial data center using the data dictionaries, the M data dictionaries correspond to the M financial data centers one-to-one, each of the data dictionaries is trained using business data of the business category contained in the corresponding financial data center, and the business data of the business category contained in the M financial data centers are different; a general dictionary evaluation module, used to determine the general compression evaluation results of the M data dictionaries based on the compression capability evaluation result groups of the M data dictionaries; a general dictionary determination module, used to determine the general data dictionary from the M data dictionaries based on the general compression evaluation results of the M data dictionaries, and send the general data dictionary to each financial data center.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0016] According to an embodiment of the present disclosure, by obtaining a compression capability evaluation result group of data dictionaries trained by multiple financial data centers, the compression capability of each of the M data dictionaries is quantitatively evaluated, which can more objectively reflect the compression capability of each data dictionary. Then, based on the compression capability evaluation result group of each of the M data dictionaries, the general compression evaluation results of each of the M data dictionaries are determined, and the general compression evaluation results of each of the M data dictionaries are quantified into numbers, which reduces the interference of subjective factors and can more objectively reflect the general compression capability of each data dictionary. The general compression capability of each of the M data dictionaries can reflect the comprehensive effect and ability of each data dictionary to process the business data of the business categories contained in all financial data centers. The data dictionary with the highest general compression capability is determined from the M data dictionaries as the general data dictionary, so that the general data dictionary has higher universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 A diagram schematically illustrates an application scenario of a method, apparatus, device, medium, and program product for determining a general data dictionary according to an embodiment of the present disclosure;

[0019] Figure 2 A flowchart of a method for determining a general data dictionary according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 3 A flowchart of a method for determining a universal data dictionary according to another embodiment of the present disclosure is schematically shown;

[0021] Figure 4 A structural block diagram schematically shows a general data dictionary determination device according to an embodiment of the present disclosure; and

[0022] Figure 5 A block diagram of an electronic device suitable for implementing a general data dictionary determination method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0025] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0027] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0028] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0029] With the popularization of distributed networks and the development of network business diversity, network traffic has surged, especially in the wide area network of the financial industry, where there are a large number of financial business interaction needs. With the deployment of multiple financial data centers in multiple locations, the demand for cross-financial data centers is increasing, and network traffic shows a continuous and rapid growth trend. However, network resources are limited and expensive, and the rapidly growing demand for multi-location and multi-center interaction has increased the operation and maintenance costs of financial data centers. In order to meet the above challenges, wide area network compression technology has become a research hotspot in response to the growing demand for business data interaction between financial data centers. Among them, dictionary-based data compression technology is one of the commonly used compression technologies in the industry. Dictionary compression technology mainly learns to capture correlation from part of the sample data, makes the data with strong correlation into a dictionary, and then replaces the high-frequency data blocks with short identifiers. The compression end and the decompression end refer to the dictionary at the same time to compress and decompress the network traffic packet by packet, and adopts a timing mechanism to implement the routing protocol distribution compression end and decompression end dictionary update.

[0030] Currently, there are two main methods of dictionary compression: one is to use the default universal dictionary of the algorithm to compress data on both the compression and decompression devices; the other is to train a dynamic dictionary that is strongly related to the business based on the business scenarios of the financial data center. When there are multiple different business interaction requirements in multiple financial data centers in multiple locations, the business data samples of a certain financial data center are generally selected to train the data dictionary, and then the same data dictionary is provided to all financial data centers for use.

[0031] When the algorithm's default general dictionary method is used, the algorithm's default general dictionary has a weak correlation with the business and may not be suitable for all business types. It may have no compression effect on irrelevant businesses, and the traditional default general dictionary compression method cannot adapt to the diversified business needs of multiple financial data centers.

[0032] When the dynamic dictionary approach is used, the financial data center is trained according to the business scenario. The compression and decompression ends need to maintain the same data dictionary for correct compression and decompression. In the scenario of multiple financial data centers in multiple locations, each financial data center has different business types and interaction requirements. The data dictionary trained by the same financial data center may not be suitable for the business needs of all financial data centers.

[0033] The stronger the correlation between the trained data dictionary and the compression service, the better the compression effect of the data dictionary. Therefore, data dictionary training should preferably include multiple locations, multiple centers and multiple services. If the services of all centers are simultaneously gathered in the same center for training, and a dictionary is trained for use by all centers, in this case, the interaction of a large number of training data samples will further increase the pressure on the WAN bandwidth.

[0034] As the number of business types increases, it is necessary to pay attention to the degradation of the data dictionary and update the data dictionary in a timely manner. In the dynamic dictionary mode, if the business of multiple financial data centers in multiple locations is simultaneously gathered in the same financial data center for training, when new business scenarios are added, it will be difficult to update the data dictionary.

[0035] If multiple financial data centers in multiple locations train data dictionaries separately, and when business data of multiple business types interact among multiple financial data centers, business data with the same business type requirements need to use the same data dictionary, each financial data center will need to maintain multiple data dictionaries, increasing the complexity of data dictionary maintenance.

[0036] Embodiments of the present disclosure provide a method, apparatus, device, medium and program product for determining a universal data dictionary. The method comprises: obtaining compression capability evaluation results of M data dictionaries of the same compression type from each financial data center, and obtaining compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress business data of business categories contained in the financial data center using the data dictionaries, the M data dictionaries correspond one-to-one to the M financial data centers, each data dictionary is trained using business data of business categories contained in the corresponding financial data center, and the business data of business categories contained in the M financial data centers are different; based on the compression capability evaluation result groups of the M data dictionaries, determining universal compression evaluation results of the M data dictionaries; based on the universal compression evaluation results of the M data dictionaries, determining a universal data dictionary from the M data dictionaries, and sending the universal data dictionary to each financial data center.

[0037] Figure 1 The application scenario diagram of the general data dictionary determination method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0038] like Figure 1 As shown, the application scenario 100 according to this embodiment includes multiple financial data centers 110, a wide area network 120, and an intermediate server 130. Among them, multiple financial data centers 110 include a compression end controller 111, a decompression end controller 112, a compression device 113, and a decompression device 114. According to an embodiment of the present disclosure, the compression device 113 of each financial data center 110 is configured with a compression end controller 111, and the decompression device 114 is configured with a decompression end controller 112. The business data in the compression device is mirrored to the compression end controller 111, and the compression end controller 111 is used to perform data dictionary training, data dictionary scoring, and data dictionary synchronization. The compression end controller 111 of each financial data center 110 communicates with each other through the wide area network 120, which facilitates data dictionary synchronization. At the same time, the decompression end controller 112 communicates with each other through the wide area network 120.

[0039] According to an embodiment of the present disclosure, the intermediate server 130 may be a server that provides various services, and the intermediate server 130 is connected to the compression end controller 111 through the wide area network 120; the intermediate server 130 is also connected to the decompression end controller 112 through the wide area network 120. The wide area network 120 is used to provide a medium for communication links between multiple financial data centers 110 and the intermediate server 130. The wide area network 120 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0040] It should be noted that the method for determining the universal data dictionary provided in the embodiment of the present disclosure can generally be executed by the intermediate server 130. Accordingly, the device for determining the universal data dictionary provided in the embodiment of the present disclosure can generally be arranged in the intermediate server 130. The method for determining the universal data dictionary provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the intermediate server 130 and can communicate with multiple financial data centers 110 and / or intermediate servers 130. Accordingly, the device for determining the universal data dictionary provided in the embodiment of the present disclosure can also be arranged in a server or server cluster that is different from the intermediate server 130 and can communicate with multiple financial data centers 110 and / or intermediate servers 130. The universal data dictionary output by the method for determining the universal data dictionary and the device for determining the universal data dictionary provided in the embodiment of the present disclosure is transmitted to the compression device 113 and the decompression device 114. The compression device 113 applies the universal data dictionary to compress the business data, and the decompression device 114 applies the universal data dictionary to decompress the business data.

[0041] It should be understood that Figure 1The number of financial data centers, wide area networks, and intermediate servers in the embodiment is only for illustration. Any number of financial data centers, wide area networks, and intermediate servers may be provided according to implementation requirements.

[0042] The following will be based on Figure 1 The scene described by Figure 2~Figure 3 The general data dictionary determination method of the disclosed embodiment is described in detail.

[0043] Figure 2 The flowchart of the method for determining a general data dictionary according to an embodiment of the present disclosure is schematically shown. Figure 2 As shown, the general data dictionary determination method of this embodiment includes operations S210 to S230.

[0044] In operation S210, compression capability evaluation results of each financial data center for M data dictionaries of the same compression type are obtained to obtain a group of compression capability evaluation results of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress business data of the business categories included in the financial data center using the data dictionaries, the M data dictionaries correspond one-to-one to the M financial data centers, each data dictionary is trained using business data of the business categories included in the corresponding financial data center, and the business data of the business categories included in the M financial data centers are different.

[0045] In operation S220 , based on the compression capability evaluation result groups of the M data dictionaries, general compression evaluation results of the M data dictionaries are determined.

[0046] In operation S230, a common data dictionary is determined from the M data dictionaries based on the common compression evaluation results of the respective M data dictionaries, and the common data dictionary is sent to each financial data center.

[0047] According to an embodiment of the present disclosure, since the data dictionaries obtained by training the business data of the business categories contained in the M financial data center applications are different, it is necessary to obtain the compression capability evaluation results of each financial data center for the M data dictionaries of the same compression type, wherein the compression capability evaluation results can be the scoring results of the compression capabilities of the M data dictionaries of the same compression type by each financial data center. The higher the compression capability, the higher the score of the data dictionary can be. The compression capability evaluation result group of each of the M data dictionaries can be composed of the score values ​​of the compression capability of each data dictionary in different financial data centers.

[0048] According to the embodiment of the present disclosure, since the business data of the business categories contained in each of the M financial data centers are different, the compression type can be divided into interactive and batch types for the interactive type, which is a real-time online business with high efficiency and low latency forwarding requirements, and the batch backup type, which is a large bandwidth demand business; for example, the operation and maintenance personnel classify the business data traffic mirrored to the compression end controller 311 into interactive business and batch business. The compression end controller 311 samples the distributed dictionary training method, taking 5 financial data centers and interactive business and batch business as examples, the 5 financial data centers are financial data center A, financial data center B, financial data center C, financial data center D and financial data center E, and the 5 financial data centers are trained according to the classification results of the business data. Two data dictionaries, namely, interactive business data dictionaries and batch business data dictionaries, are trained respectively by center A. For example, DictA1 and DictA2 are trained by center A, where the interactive business data dictionary is represented by number 1, and the batch business data dictionary is represented by number 2. A total of 10 data dictionaries are trained by the 5 centers.

[0049] According to an embodiment of the present disclosure, a financial data center with interactive needs will synchronize its data dictionary, while a financial data center without interactive needs does not need to synchronize its data dictionary. Table 1 is a statistical table of data dictionary synchronization information of the financial data center. As shown in Table 1, financial data center A uses interactive business data DataA1 for training to obtain data dictionary DictA1, financial data center A uses batch business data DataA2 for training to obtain data dictionary DictA2, financial data center B uses interactive business data DataB1 for training to obtain data dictionary DictB1, and financial data center B uses batch business data DataB2 for training to obtain data dictionary DictB2. Similarly, financial data center C uses training to obtain data dictionary DictC1 for interactive business and data dictionary DictC2 for batch business. Financial data center D uses training to obtain data dictionary DictD1 for interactive business and data dictionary DictD2 for batch business. Financial data center E uses training to obtain data dictionary DictE1 for interactive business data and data dictionary DictE2 for batch business.

[0050] Table 1 Statistics of data dictionary synchronization information of the financial data center

[0051]

[0052] According to an embodiment of the present disclosure, when there is an interaction demand between financial data center A and financial data center B, the interactive data dictionary DictA1 of financial data center A is synchronized to the interactive data dictionary of financial data center B, and the batch data dictionary DictA2 of financial data center A is synchronized to the batch data dictionary of financial data center B. At the same time, the interactive data dictionary DictB1 of financial data center B is synchronized to the interactive data dictionary of financial data center A, and the batch data dictionary DictB2 of financial data center B is synchronized to the batch data dictionary of financial data center A.

[0053] According to an embodiment of the present disclosure, as shown in Table 1, when there is an interaction demand between financial data center A and financial data center C, and there is an interaction demand between financial data center B, financial data center C, financial data center D and financial data center E, according to the above-mentioned synchronization process, a data dictionary of the same compression type possessed by financial data center A, financial data center B, financial data center C, financial data center D and financial data center E is obtained.

[0054] According to the embodiments of the present disclosure, each financial data center applies its own business data to pre-check the compression effect of the currently available data dictionary. For example, financial data center A compresses the business data DataA1 of the interactive business of financial data center A according to the currently synchronized data dictionary DictA1 to obtain the compression ratio WA1A, compresses the business data DataA1 of the interactive business of financial data center A according to the currently synchronized data dictionary DictB1 to obtain the compression ratio WB1A, and compresses the business data DataA1 of the interactive business of financial data center A according to the currently synchronized data dictionary DictC1 to obtain the compression ratio WC1A. Repeat the above steps to obtain the compression ratio of each financial data center for pre-checking the compression effect. The compression ratio obtained by each financial data center using the business data to pre-check the compression effect of the available data dictionary is expressed as follows: the first letter W represents the compression ratio, the second letter represents which financial data center the data dictionary is trained by, the third letter represents the compression category, 1 represents interactive business, 2 represents batch business, and the fourth letter represents which financial data center the business data comes from.

[0055] According to the embodiment of the present disclosure, after the data dictionaries of the same compression category of each financial data center are sorted, the number of data dictionaries of each compression category is 5. According to the sorting results of the data dictionaries of the same compression category of each financial data center, the data dictionaries are scored, and the compression capability evaluation results of the M data dictionaries of the same compression type of each financial data center are obtained. According to the scores of the data dictionaries of the same compression category of each financial data center, the compression capability evaluation result groups of the M data dictionaries are obtained.

[0056] According to an embodiment of the present disclosure, the compression capability evaluation result group may be composed of the compression capability scores of the same data dictionary in M ​​financial data centers. The compression capability of each data dictionary may be comprehensively evaluated based on the compression capability evaluation result groups of each of the M data dictionaries and the compression ratios of the same data dictionary in the M financial data centers to obtain the general compression evaluation results of each of the M data dictionaries.

[0057] According to an embodiment of the present disclosure, after obtaining the universal compression evaluation results of M data dictionaries, the universal compression evaluation results of the M data dictionaries may be sorted, and the data dictionary with the highest universal compression evaluation result may be determined from the M data dictionaries as the universal data dictionary.

[0058] According to the embodiments of the present disclosure, the method of quantitatively evaluating the compression capability of the data dictionary can more objectively judge the compression capability of each data dictionary, and can more accurately describe the compression capability of each data dictionary, which is convenient for comparison, analysis and calculation in subsequent steps.

[0059] According to the embodiments of the present disclosure, the compression capability scores of the same data dictionary in M ​​financial data centers are objective evaluations of the compression capability of the data dictionary, and the compression ratios of the same data dictionary in M ​​financial data centers are the real compression effects of the data dictionary in actual applications. Evaluating the general compression capability of the M data dictionaries based on these two data can yield more realistic and accurate evaluation results.

[0060] According to an embodiment of the present disclosure, by determining the data dictionary with the highest universal compression evaluation result from M data dictionaries as the universal data dictionary, it means that the data dictionary with the highest universal compression evaluation result has a better compression effect in all financial data centers, and also shows that the universal data dictionary is the data dictionary with the highest universality among all data dictionaries.

[0061] According to the embodiments of the present disclosure, by quantitatively evaluating the compression capabilities of each of the M data dictionaries, the compression capabilities of each data dictionary can be more objectively reflected. The universal compression evaluation results of each of the M data dictionaries are quantified into numbers, which reduces the interference of subjective factors and can more objectively reflect the universal compression capabilities of each data dictionary. The universal compression capabilities of each of the M data dictionaries can reflect the comprehensive effect and capability of each data dictionary in processing the business data of the business categories contained in all financial data centers. The data dictionary with the highest universality is determined from the M data dictionaries as the universal data dictionary, so that the universal data dictionary has higher universality.

[0062] According to an embodiment of the present disclosure, the compression capability evaluation results of each of the M data dictionaries sent by each financial data center are determined in the following manner: when the financial data center is equipped with a data dictionary, the data dictionary is used to perform compression capability evaluation on the business data of the business category stored in the financial data center to obtain an initial compression capability evaluation result; when the financial data center is not equipped with a data dictionary, a predetermined compression capability evaluation result is used as the initial compression capability evaluation result of the data dictionary; and the initial compression capability evaluation results of each of the M data dictionaries are optimized to obtain a compression capability evaluation result of each of the M data dictionaries.

[0063] According to an embodiment of the present disclosure, the compression capability evaluation results of the M data dictionaries sent by each financial data center include multiple situations. For example, when there is an interaction demand among multiple financial data centers, the data dictionaries trained by other financial data centers of each financial data center with the interaction demand are used to evaluate the compression capability of the business data of the business category contained in the financial data center using the data dictionary possessed by the financial data center, and the initial compression capability evaluation result obtained is the compression ratio of the data dictionary or the score for the compression ratio, and the score reflects the size of the compression capability of the data dictionary relative to other data dictionaries, and the initial compression capability evaluation result of the data dictionary that the financial data center does not possess for compressing the business data of the business category contained in the financial data center is a preset fixed value, and the fixed value is lower than the minimum value of the score of the data dictionary possessed by the financial data center or the minimum value of the compression ratio, for example, the preset fixed value can be set to 0.

[0064] According to an embodiment of the present disclosure, when there is no interaction demand between financial data centers, that is, when there is no data exchange between financial data centers, financial data centers without interaction demand do not synchronize data dictionaries, and data dictionaries are only synchronized between financial data centers with interaction demand.

[0065] According to an embodiment of the present disclosure, Table 2 is a statistical table of data dictionary compression ratios. As shown in Table 2, the compression ratio of the data dictionary of a financial data center without interactive requirements is set to 0. The data dictionary is sorted according to the compression ratio. The larger the compression ratio, the better the data dictionary and the higher the ranking. For a financial data center without interactive requirements, the data dictionary is filled with 0 when sorting, and when filling, it is filled according to the compression category.

[0066] Table 2 Data dictionary compression ratio ranking table

[0067]

[0068] According to an embodiment of the present disclosure, the method for optimizing the initial compression capability evaluation results is to sort the initial compression capability evaluation results from large to small, and score the corresponding data dictionaries according to the sorted initial compression capability evaluation results to obtain the scoring value of each data dictionary, wherein each data dictionary is scored from large to small according to the sorting results, and the scoring values ​​decrease in sequence according to the sorting results.

[0069] According to the embodiment of the present disclosure, as shown in Table 2, the sorted data dictionaries are scored for the first time. Each financial data center's interactive business and batch business correspond to 5 data dictionaries. The first data dictionary in each compression category is scored 5, the second is scored 4, and so on. The data dictionary without interactive requirements is scored 0. Table 3 is a data dictionary universality evaluation table, as shown in Table 3.

[0070] Table 3 is the data dictionary universality evaluation table

[0071]

[0072] According to an embodiment of the present disclosure, in Table 3, taking DictA1 as an example, when scoring for the first time, the compression ratio ranking result of DictA1 in financial data center A is ranked first, so the score of DictA1 in financial data center A is 5, the compression ratio ranking result of DictA1 in financial data center B is ranked second, so the score of DictA1 in financial data center B is 4, the compression ratio ranking result of DictA1 in financial data center C is ranked second, so the score of DictA1 in financial data center C is 4, and financial data center A has no interaction requirements with financial data center D and financial data center E, so the scores of DictA1 in financial data center D and financial data center E are 0.

[0073] According to the embodiment of the present disclosure, according to the above-mentioned scoring steps, the scores of DictA2 in the first scoring in the financial data center A, the financial data center B, the financial data center C, the financial data center D and the financial data center E are 5, 3, 3, 0 and 0 respectively. The scores of DictB1 in the first scoring in the financial data center A, the financial data center B, the financial data center C, the financial data center D and the financial data center E are 3, 5, 3, 4 and 3 respectively. The scores of DictB2 in the first scoring in the financial data center A, the financial data center B, the financial data center C, the financial data center D and the financial data center E are 4, 5, 2, 3 and 4 respectively. Applying the same rule, the scores of DictC1, DictC2, DictD1, DictD2, DictE1 and DictE2 in the first scoring in the financial data center A, the financial data center B, the financial data center C, the financial data center D and the financial data center E are obtained respectively. The scores of each data dictionary in the first scoring in all financial data centers are used as the compression capability evaluation result group of each data dictionary. According to an embodiment of the present disclosure, the compression capability evaluation results of the M data dictionaries sent by each financial data center include two situations: the situation where the financial data center has the data dictionary and the situation where the financial data center does not have the data dictionary. Among them, it is not necessary for financial data centers that do not have interaction needs to have each other's data dictionaries, because they will not interact and process each other's business data. On this basis, the data dictionary that the financial data center does not have is used as the initial compression capability evaluation result of the data dictionary according to the predetermined compression capability evaluation result. Among them, when the predetermined compression capability evaluation result is set to 0, the influence of the data dictionary that the financial data center does not have on the compression capability evaluation results of the M data dictionaries can be eliminated in the subsequent calculation steps, thereby improving the accuracy of the general compression evaluation results of the M data dictionaries in the subsequent steps.

[0074] According to an embodiment of the present disclosure, based on the compression capability evaluation result groups of M data dictionaries, the general compression evaluation results of the M data dictionaries are determined, including: obtaining the compression ratio of the data dictionary used to compress the business data of the business categories contained in the M data financial centers, and obtaining the compression ratio group of the data dictionary; evaluating the general compression capability of the M data dictionaries according to the compression capability evaluation result groups and compression ratio groups of the M data dictionaries, and obtaining the general compression evaluation results of the M data dictionaries. According to an embodiment of the present disclosure, the compression ratios of the M data dictionaries can be sorted in order from large to small, and the compression capabilities of the sorted M data dictionaries can be scored in order from large to small, and the scored scores can be used as the compression capability evaluation result groups of the M data dictionaries, and the general compression capabilities of the M data dictionaries can be evaluated according to the compression ratios of the M data dictionaries and the corresponding scored scores.

[0075] According to an embodiment of the present disclosure, the product of the compression ratio of each data dictionary in the compression ratio group of the data dictionary and the compression capability evaluation result can be used as the evaluation result of the general compression capability of the data dictionary in each financial data center, and then the evaluation result of the general compression capability of the data dictionary in each financial data center can be weighted averaged or added to obtain the general compression evaluation result of the data dictionary in all financial data centers, that is, the general compression evaluation result of each of the M data dictionaries.

[0076] According to an embodiment of the present disclosure, the general compression capability of each of the M data dictionaries is described from two dimensions: the compression ratio of the business data of the business categories contained in each of the M data financial centers and the corresponding score value. The general processing capabilities of different data dictionaries for the business data of the business types contained in different financial data centers are fully considered. The general compression evaluation results of each of the M data dictionaries can more accurately reflect the general compression capability of each data dictionary.

[0077] According to an embodiment of the present disclosure, based on the compression capability evaluation result groups and compression ratio groups of the M data dictionaries, the general compression capabilities of the M data dictionaries are evaluated to obtain the general compression evaluation results of the M data dictionaries, including: for each data dictionary, calculating the product of the compression ratio and the compression capability evaluation result of each data dictionary for the same financial data center to obtain the compression evaluation result of the data dictionary for the financial data center; based on the compression evaluation results for each of the M financial data centers, obtaining the general compression evaluation result of the data dictionary.

[0078] According to an embodiment of the present disclosure, as shown in Table 3, the sorted data dictionaries are scored for the second time. For each of the data dictionaries, the product of the compression ratio and the compression capability evaluation result of each data dictionary for the same financial data center is calculated to obtain the compression evaluation result of the data dictionary for the financial data center; the compression evaluation results for each of the M financial data centers are added together to obtain the general compression evaluation result of the data dictionary. For example, taking DictA1 and DictA2 as examples, the second scoring is:

[0079] scoreDictA1=5×WA1A+4×WA1B+4×WA1C+0×0+0×0;

[0080] scoreDictA2=5×WA2A+3×WA2B+3×WA2C+0×0+0×0;

[0081] Wherein, scoreDictA1 is the general compression evaluation result of data dictionary DictA1, and scoreDictA2 is the general compression evaluation result of data dictionary DictA2. According to the above calculation method, the general compression evaluation results of DictB1, DictB2, DictC1, DictC2, DictD1, DictD2, DictE1 and DictE2 are calculated respectively.

[0082] According to an embodiment of the present disclosure, for each data dictionary, by multiplying the compression ratio and compression capability evaluation result of each data dictionary for the same financial data center, the differences between the compression ratios of M data dictionaries and the differences between the compression capability evaluation results are further amplified, making the differences in the general compression capabilities of each data dictionary more obvious.

[0083] According to an embodiment of the present disclosure, the compression ratio of the data dictionary used to compress the business data of the business categories contained in each of the M data financial centers is determined in the following manner: when the financial data center is equipped with a data dictionary, the business data of the business categories stored in the financial data center is processed using the data dictionary to obtain the compression ratio of the data dictionary; when the financial data center is not equipped with a data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary.

[0084] According to an embodiment of the present disclosure, when the financial data center is equipped with a data dictionary, the compression ratio obtained by processing the business data of the financial data center using the data dictionary is the compression ratio obtained by detecting the real data. When the financial data center is not equipped with a data dictionary, the predetermined compression ratio of the data dictionary can be set to 0.

[0085] According to the embodiment of the present disclosure, since the compression ratio sent by the financial data center without data interaction requirements is a predetermined compression ratio, it can be set to 0. This setting method eliminates the influence of the compression ratio of the data dictionary of the financial data center without data interaction requirements on the calculation process of determining the general data dictionary in the subsequent step calculation, and can obtain more accurate general compression evaluation results in the subsequent steps.

[0086] According to an embodiment of the present disclosure, based on the universal compression evaluation results of each of the M data dictionaries, a universal data dictionary is determined from the M data dictionaries, and the universal data dictionary is sent to each financial data center, including: comparing the universal compression evaluation results of each of the M data dictionaries of the same type, determining the data dictionary with the highest universal compression evaluation result as the universal data dictionary of each financial data center, and sending the universal data dictionary to each financial data center.

[0087] According to an embodiment of the present disclosure, a data dictionary with the highest general compression evaluation result is determined from M data dictionaries of the same compression type as the general data dictionary of the compression type, thereby determining a general data dictionary of each compression type, and sending each general data dictionary of the compression type to each financial data center. According to an embodiment of the present disclosure, since the general compression evaluation result represents the general compression capability of each of the M data dictionaries of the same type, the data dictionary with the highest general compression evaluation result is selected as the general data dictionary of each financial data center, so that the general data dictionary has the highest general compression capability in all financial data centers, the best compression effect, and the highest universality.

[0088] According to an embodiment of the present disclosure, each financial data center includes multiple universal data dictionaries of different types; the universal data dictionaries perform data compression in the following manner: determine the compression type of the data to be compressed; determine a target universal data dictionary that matches the compression type of the data to be compressed from multiple universal data dictionaries; and compress the data to be compressed using the target universal data dictionary.

[0089] According to the embodiments of the present disclosure, since the financial data center contains multiple general data dictionaries of different types, when applying the general data dictionary to process the data to be compressed, it is necessary to first determine the compression type of the data to be compressed, and then select the corresponding general data dictionary as the target general data dictionary according to the compression type, and use the target general data dictionary to compress the data to be compressed.

[0090] According to the embodiments of the present disclosure, by determining multiple common data dictionaries of different types, corresponding compression types can be processed, thereby improving compression capabilities and compression effects. On this basis, bandwidth requirements are reduced, and the operating and maintenance costs of the financial data center are reduced.

[0091] Figure 3 The flowchart of a method for determining a universal data dictionary according to another embodiment of the present disclosure is schematically shown.

[0092] like Figure 3 As shown, the steps of determining the implementation of the common data dictionary include operations S310 to S350.

[0093] In operation S310, compression capability evaluation results of each financial data center for M data dictionaries of the same compression type are obtained to obtain compression capability evaluation result groups of the M data dictionaries.

[0094] In operation S320, a compression ratio of the data dictionary used to compress the business data of the business categories contained in each of the M data financial centers is obtained to obtain a compression ratio group of the data dictionary.

[0095] In operation S330, for each data dictionary, the product of the compression ratio and the compression capability evaluation result of each data dictionary for the same financial data center is calculated to obtain the compression evaluation result of the data dictionary for the financial data center.

[0096] In operation S340, based on the compression evaluation results for each of the M financial data centers, a general compression evaluation result of the data dictionary is obtained.

[0097] In operation S350, the universal compression evaluation results of the M data dictionaries of the same type are compared, the data dictionary with the highest universal compression evaluation result is determined as the universal data dictionary of each financial data center, and the universal data dictionary is sent to each of the financial data centers.

[0098] According to an embodiment of the present disclosure, the data dictionary with the highest score is selected from the data dictionary corresponding to the interactive service as the general data dictionary for the interactive service of each financial data center, and the data dictionary with the highest score is selected from the data dictionary corresponding to the batch service as the general data dictionary for the batch service of each financial data center. The general data dictionary for the interactive service and the general data dictionary for the batch service are sent to the compression end controller 111 and the decompression end controller 112 of each financial data center 110 through the wide area network 120, and the compression end controller 111 and the decompression end controller 112 of each financial data center 110 synchronize the general data dictionary to the corresponding compression device 113 and the decompression device 114.

[0099] According to the embodiments of the present disclosure, when the service data changes, such as when a new service is added or a service is reduced, the service data of the compression device 113 can be mirrored to the compression end controller 111 in advance. The process of determining the general data dictionary is repeated, and the general data dictionary is updated. The general data dictionary can be trained and distributed at any time according to actual needs without affecting the operation of the current compression service and without increasing the performance compression of the compression device.

[0100] Based on the above-mentioned general data dictionary determination method, the present disclosure also provides a general data dictionary determination device. Figure 4 The device is described in detail.

[0101] Figure 4 The structure block diagram of the general data dictionary determination device according to the embodiment of the present disclosure is schematically shown.

[0102] like Figure 4 As shown, the general data dictionary determination device 400 of this embodiment includes an evaluation result acquisition module 410 , a general dictionary evaluation module 420 and a general dictionary determination module 430 .

[0103] The evaluation result acquisition module 410 is used to obtain the compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, and obtain the compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress the business data of the business category contained in the financial data center using the data dictionary, the M data dictionaries correspond to the M financial data centers one by one, each data dictionary is trained using the business data of the business category contained in the corresponding financial data center, and the business data of the business category contained in each of the M financial data centers is different. In one embodiment, the evaluation result acquisition module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0104] The general dictionary evaluation module 420 is used to determine the general compression evaluation results of the M data dictionaries based on the compression capability evaluation result groups of the M data dictionaries. In one embodiment, the general dictionary evaluation module 420 can be used to perform the operation S220 described above, which will not be described in detail here.

[0105] The universal dictionary determination module 430 is used to determine a universal data dictionary from the M data dictionaries based on the universal compression evaluation results of each of the M data dictionaries, and send the universal data dictionary to each financial data center. In one embodiment, the universal dictionary determination module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0106] According to an embodiment of the present disclosure, the general dictionary evaluation module 420 includes a compression ratio group determination submodule and a general compression evaluation submodule.

[0107] The compression ratio group determination submodule is used to obtain the compression ratio of the data dictionary for compressing the business data of the business categories contained in each of the M data financial centers, and obtain the compression ratio group of the data dictionary.

[0108] The general compression evaluation submodule is used to evaluate the general compression capabilities of the M data dictionaries according to the compression capability evaluation result groups and compression ratio groups of the M data dictionaries, and obtain the general compression evaluation results of the M data dictionaries.

[0109] According to an embodiment of the present disclosure, the general compression evaluation submodule includes a compression evaluation unit and a general evaluation unit.

[0110] The compression evaluation unit is used to calculate, for each data dictionary, the product of the compression ratio of each data dictionary for the same financial data center and the compression capability evaluation result, so as to obtain the compression evaluation result of the data dictionary for the financial data center.

[0111] The general evaluation unit is used to obtain a general compression evaluation result of the data dictionary based on the compression evaluation results of each of the M financial data centers.

[0112] According to an embodiment of the present disclosure, the general dictionary determination module 430 includes a generality comparison submodule.

[0113] The universality comparison submodule is used to compare the universal compression evaluation results of the M data dictionaries of the same type, determine the data dictionary with the highest universal compression evaluation result as the universal data dictionary of each financial data center, and send the universal data dictionary to each financial data center.

[0114] According to an embodiment of the present disclosure, any multiple modules of the evaluation result acquisition module 410, the general dictionary evaluation module 420 and the general dictionary determination module 430 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the evaluation result acquisition module 410, the general dictionary evaluation module 420 and the general dictionary determination module 430 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the evaluation result acquisition module 410, the general dictionary evaluation module 420 and the general dictionary determination module 430 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0115] Figure 5 A block diagram of an electronic device suitable for implementing a general data dictionary determination method according to an embodiment of the present disclosure is schematically shown.

[0116] like Figure 5As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0117] In RAM 503, various programs and data required for the operation of electronic device 500 are stored. Processor 501, ROM 502 and RAM 503 are connected to each other via bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0118] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0119] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0120] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than ROM 502 and RAM 503.

[0121] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the general data dictionary determination method provided by the embodiment of the present disclosure.

[0122] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0123] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0124] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0125] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0126] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0128] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining a general data dictionary, characterized in that: The method comprises: Obtain compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, and obtain compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress business data of the business category included in the financial data center using the data dictionaries, the M data dictionaries correspond to the M financial data centers one-to-one, each data dictionary is trained using business data of the business category included in the corresponding financial data center, and the business data of the business category included in each of the M financial data centers are different; Determining a general compression evaluation result of each of the M data dictionaries based on the compression capability evaluation result groups of each of the M data dictionaries; Based on the respective universal compression evaluation results of the M data dictionaries, a universal data dictionary is determined from the M data dictionaries, and the universal data dictionary is sent to each of the financial data centers.

2. The method according to claim 1, characterized in that The compression capability evaluation results of the M data dictionaries sent by each of the financial data centers are determined in the following manner: In the case where the financial data center has the data dictionary, using the data dictionary to perform compression capability evaluation on the business data of the business category stored in the financial data center to obtain an initial compression capability evaluation result; In the case that the financial data center does not have the data dictionary, a predetermined compression capability evaluation result is used as an initial compression capability evaluation result of the data dictionary; The initial compression capability evaluation results of each of the M data dictionaries are optimized to obtain the compression capability evaluation results of each of the M data dictionaries.

3. The method according to claim 1, characterized in that The determining, based on the compression capability evaluation result groups of the M data dictionaries, respective universal compression evaluation results of the M data dictionaries comprises: Obtaining a compression ratio of the data dictionary for compressing business data of business categories contained in each of the M data financial centers, and obtaining a compression ratio group of the data dictionary; According to the compression capability evaluation result groups and compression ratio groups of the M data dictionaries, the universal compression capabilities of the M data dictionaries are evaluated to obtain universal compression evaluation results of the M data dictionaries.

4. The method according to claim 3, characterized in that The method of evaluating the general compression capability of each of the M data dictionaries according to the compression capability evaluation result group and the compression ratio group of each of the M data dictionaries to obtain the general compression evaluation result of each of the M data dictionaries includes: For each of the data dictionaries, the product of the compression ratio and the compression capability evaluation result of each of the data dictionaries for the same financial data center is calculated to obtain the compression evaluation result of the data dictionary for the financial data center; Based on the compression evaluation results for each of the M financial data centers, a general compression evaluation result of the data dictionary is obtained.

5. The method according to claim 3, characterized in that: The compression ratio of the data dictionary for compressing the business data of the business categories contained in each of the M data financial centers is determined in the following manner: In the case where the financial data center has the data dictionary, the business data of the business category stored in the financial data center is processed using the data dictionary to obtain a compression ratio of the data dictionary; In the case that the financial data center does not have the data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary.

6. The method according to claim 1, characterized in that The determining a common data dictionary from the M data dictionaries based on the common compression evaluation results of each of the M data dictionaries, and sending the common data dictionary to each of the financial data centers, comprises: The universal compression evaluation results of the M data dictionaries of the same type are compared, and the data dictionary with the highest universal compression evaluation result is determined as the universal data dictionary of each financial data center, and the universal data dictionary is sent to each financial data center.

7. The method according to claim 1, characterized in that Each financial data center includes multiple universal data dictionaries of different types; the universal data dictionaries are compressed in the following manner: Determine the compression type of the data to be compressed; Determine a target universal data dictionary that matches the compression type of the data to be compressed from the plurality of universal data dictionaries; The data to be compressed is compressed using the target universal data dictionary.

8. A general data dictionary determination device, characterized in that: The device comprises: An evaluation result acquisition module is used to obtain the compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, and obtain compression capability evaluation result groups of the M data dictionaries, wherein the compression capability evaluation results represent the ability to compress the business data of the business category contained in the financial data center using the data dictionaries, the M data dictionaries correspond to the M financial data centers one by one, each of the data dictionaries is trained using the business data of the business category contained in the corresponding financial data center, and the business data of the business category contained in each of the M financial data centers is different; a general dictionary evaluation module, configured to determine a general compression evaluation result of each of the M data dictionaries based on a compression capability evaluation result group of each of the M data dictionaries; and A universal dictionary determination module is used to determine a universal data dictionary from the M data dictionaries based on the universal compression evaluation results of each of the M data dictionaries, and send the universal data dictionary to each of the financial data centers.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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