General data dictionary determination methods, apparatus, equipment, media and program products

Abstract

The present disclosure provides a general data dictionary determination method, which can be applied to the field of big data technology in the financial field. The method comprises: obtaining the compression capability evaluation result of each financial data center on M data dictionaries of the same compression type, obtaining the compression capability evaluation result group of the M data dictionaries, wherein the compression capability evaluation result represents the compression capability of the data dictionary on the business data of the business category contained in the financial data center, and the M data dictionaries correspond to the M financial data centers one by one; determining the general compression evaluation result of the M data dictionaries based on the compression capability evaluation result group of the M data dictionaries; determining the general data dictionary from the M data dictionaries based on the general compression evaluation result of the M data dictionaries, and sending the general data dictionary to each financial data center. The present disclosure also provides a general data dictionary determination device, equipment, medium and program product.

Description

Technical Field

[0001] This disclosure relates to the field of big data technology in the financial sector, specifically to a general data dictionary determination method, apparatus, equipment, medium, and program product. Background Technology

[0002] With the deployment of multiple financial data centers, the demand for cross-data center interaction is increasing, and network traffic is showing a rapid growth trend, increasing the operation and maintenance costs of financial data centers. To alleviate the bandwidth pressure on financial data centers, a data dictionary is trained using business data from one of the financial data centers, and this data dictionary is then used to compress and decompress the network traffic of each financial data center.

[0003] In realizing the concept of this disclosure, the inventors discovered that the related technology has at least the following problems: since the business data of each financial data center belongs to different business types, applying the same data dictionary to compress business data of different business types results in poor compression effect of business data. Moreover, there are different interaction needs between multiple financial data centers, and applying the same data dictionary to compress the interactive business data received from other financial data centers also results in poor compression effect of 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, this disclosure provides a general data dictionary determination method, apparatus, device, medium and program product.

[0005] According to a first aspect of this disclosure, a method for determining a general data dictionary is provided, comprising: obtaining compression capability evaluation results for M data dictionaries of the same compression type from each financial data center, thereby obtaining a set of compression capability evaluation results for each of the M data dictionaries, wherein the compression capability evaluation results characterize 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 with the M financial data centers, each data dictionary is trained using business data of the business categories contained in its corresponding financial data center, and the business data of the business categories contained in each of the M financial data centers are different; determining a general compression evaluation result for each of the M data dictionaries based on the set of compression capability evaluation results for each of the M data dictionaries; determining a general data dictionary from the M data dictionaries based on the general compression evaluation results for each of the M data dictionaries, and sending the general data dictionary to each of the financial data centers.

[0006] According to embodiments of this disclosure, the compression capability assessment results of each of the M data dictionaries sent by each of the aforementioned financial data centers are determined as follows: when the financial data center possesses the aforementioned data dictionaries, the compression capability assessment of the business data of the business categories stored in the financial data center is performed using the aforementioned data dictionaries to obtain an initial compression capability assessment result; when the financial data center does not possess the aforementioned data dictionaries, a predetermined compression capability assessment result is used as the initial compression capability assessment result of the aforementioned data dictionaries; the initial compression capability assessment results of each of the M aforementioned data dictionaries are optimized to obtain the compression capability assessment results of each of the M aforementioned data dictionaries.

[0007] According to an embodiment of this disclosure, the above-mentioned determination of the 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 includes: obtaining the compression ratio of the data dictionaries used to compress business data of the business categories contained in each of the M data financial centers, thereby obtaining the compression ratio group of the data dictionaries; and evaluating the general compression capability of each of the M data dictionaries based on the compression capability evaluation result groups and compression ratio groups of each of the M data dictionaries, thereby obtaining the general compression evaluation result of each of the M data dictionaries.

[0008] According to embodiments of this disclosure, the above-mentioned evaluation of the general compression capability of each of the M data dictionaries based on their respective compression capability evaluation result groups and compression ratio groups, to obtain the general compression evaluation result of each of the M data dictionaries, includes: for each of the data dictionaries, calculating the product of the compression ratio and 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 dictionaries for the financial data center; and obtaining the general compression evaluation result of the data dictionaries based on the compression evaluation results for each of the M financial data centers.

[0009] According to embodiments of this disclosure, the compression ratio of the data dictionary used to compress business data of each of the M financial data centers is determined as follows: when the financial data center has the data dictionary, the data dictionary is used to process the business data of the business categories stored in the financial data center to obtain the compression ratio of the data dictionary; when the financial data center does not have the data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary.

[0010] According to an embodiment of this disclosure, the process of determining a general data dictionary from the M data dictionaries based on their respective general compression evaluation results and sending the general data dictionary to each of the financial data centers includes: comparing the general compression evaluation results 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 embodiments of this disclosure, each financial data center includes multiple general data dictionaries of different types; the general data dictionaries are used for data compression in the following manner: determining the compression type of the data to be compressed; determining a target general data dictionary from the multiple general data dictionaries that matches the compression type of the data to be compressed; and compressing the data to be compressed using the target general data dictionary.

[0012] A second aspect of this disclosure provides a general data dictionary determination apparatus, comprising: an evaluation result acquisition module, configured to acquire the compression capability evaluation results of each financial data center for M data dictionaries of the same compression type, thereby obtaining a set of compression capability evaluation results for each of the M data dictionaries, wherein the compression capability evaluation results characterize 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 with the M financial data centers, each data dictionary is trained using business data of the business categories contained in its corresponding financial data center, and the business data of the business categories contained in each of the M financial data centers are different; a general dictionary evaluation module, configured to determine the general compression evaluation result of each of the M data dictionaries based on the set of compression capability evaluation results for each of the M data dictionaries; and a general dictionary determination module, configured to determine a general data dictionary from the M data dictionaries based on the general compression evaluation results for each of the M data dictionaries, and send the general data dictionary to each of the financial data centers.

[0013] A third aspect of this 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 method described above.

[0014] A fourth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0015] The fifth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0016] According to embodiments of this disclosure, by obtaining the compression capability evaluation result sets of data dictionaries trained by multiple financial data centers, the compression capabilities of each of the M data dictionaries are quantitatively evaluated, which can more objectively reflect the compression capabilities of each data dictionary. Then, based on the compression capability evaluation result sets of the M data dictionaries, a general compression evaluation result for each of the M data dictionaries is determined. Quantifying the general compression evaluation result of the M data dictionaries into numbers 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 capability of each data dictionary in processing business data of all business categories included in all financial data centers. The data dictionary with the highest general compression capability is selected as the general data dictionary from the M data dictionaries, making the general data dictionary more universal. Attached Figure Description

[0017] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 The illustration schematically depicts application scenarios of a general data dictionary determination method, apparatus, device, medium, and program product according to embodiments of the present disclosure;

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

[0020] Figure 3 A flowchart illustrating a general data dictionary determination method according to another embodiment of the present disclosure is shown schematically;

[0021] Figure 4 A schematic block diagram of a general data dictionary determination apparatus according to embodiments of the present disclosure is shown; and

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

[0023] The embodiments of the present disclosure will now 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 disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

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

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

[0027] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0028] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this disclosure all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0029] With the widespread adoption of distributed networks and the diversification of network services, network traffic has surged, particularly in the financial industry's wide area networks (WANs) where there is a significant demand for financial business interactions. As multiple financial data centers are deployed across different locations, the need for cross-data center interactions is increasing, leading to a continuous and rapid growth in network traffic. However, network resources are limited and expensive, and the rapidly growing demand for multi-location, multi-center interactions is increasing the operation and maintenance costs of financial data centers. To address these challenges and the ever-increasing demand for business data interaction between financial data centers, WAN compression technology has become a research hotspot. Among these technologies, dictionary-based data compression is one of the most commonly used compression techniques in the industry. Dictionary compression technology primarily learns and captures correlations from a subset of sample data, creating a dictionary from highly correlated data. Then, short identifiers replace frequently occurring data blocks. Both the compression and decompression ends refer to this dictionary to compress and decompress network traffic packet by packet. A timed mechanism is used to distribute dictionary updates between the compression and decompression ends via routing protocols.

[0030] Currently, commonly used dictionary compression methods mainly include two approaches: one is to use a universal dictionary with the algorithm's default value for data compression on both the compression and decompression ends; the other is to train a dynamic dictionary that is strongly related to the business scenarios of financial data centers for data compression. When there are multiple different business interaction needs in multiple financial data centers in multiple locations, a business data sample from a certain financial data center is usually selected to train the data dictionary, and then the same data dictionary is provided to all financial data centers for use.

[0031] When using the algorithm's default general dictionary method, the default general dictionary is not closely related to the business and may not be suitable for all business types. It may not have a compression effect on unrelated businesses, and the traditional default general dictionary compression method cannot adapt to the diverse business needs of multiple financial data centers.

[0032] When using a dynamic dictionary approach, the financial data center trains the dictionary based on the business scenario. The compression and decompression ends need to maintain the same data dictionary to compress and decompress correctly. In scenarios with multiple financial data centers in multiple locations, each financial data center has different business types and different 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, the data dictionary training should ideally include multiple locations, multiple centers, and multiple services. If the services of all centers are simultaneously aggregated into the same center for training, and a single dictionary is trained and provided to all centers, the interaction of a large number of training data samples will further increase the pressure on the wide area network bandwidth.

[0034] As business types increase, it's crucial to constantly monitor data dictionary degradation and update it promptly. With a dynamic dictionary approach, if multiple financial data centers from different locations simultaneously converge on a single financial data center for training, updating the data dictionary can become challenging when new business scenarios emerge.

[0035] If multiple financial data centers in different locations train their own data dictionaries, and there is interaction between business data of different business types in these multiple financial data centers, business data with the same business type requirements will need to use the same data dictionary. This will result in each financial data center needing to maintain multiple data dictionaries, increasing the complexity of data dictionary maintenance.

[0036] Embodiments of this disclosure provide a method, apparatus, device, medium, and program product for determining a general data dictionary. The method includes: obtaining compression capability evaluation results for M data dictionaries of the same compression type from each financial data center, resulting in a set of compression capability evaluation results for each of the M data dictionaries. The compression capability evaluation results characterize the ability to compress business data of business categories contained in the financial data center using the data dictionary. Each of the M data dictionaries corresponds one-to-one with one of the M financial data centers, and each data dictionary is trained using business data of the business categories contained in its corresponding financial data center. The business data of the business categories contained in each of the M financial data centers are different. Based on the set of compression capability evaluation results for each of the M data dictionaries, a general compression evaluation result is determined for each of the M data dictionaries. Based on the general compression evaluation results for 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 financial data center.

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

[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. Each of the multiple financial data centers 110 includes a compression controller 111, a decompression controller 112, a compression device 113, and a decompression device 114. According to an embodiment of this disclosure, each financial data center 110's compression device 113 is configured with a compression controller 111, and its decompression device 114 is configured with a decompression controller 112. Business data in the compression device is mirrored to the compression controller 111, and the compression controller 111 is used for data dictionary training, data dictionary scoring, and data dictionary synchronization. The compression controllers 111 of each financial data center 110 are interconnected via the wide area network 120 for convenient data dictionary synchronization. Simultaneously, the decompression controllers 112 are interconnected via the wide area network 120.

[0039] According to embodiments of this disclosure, the intermediate server 130 may be a server providing various services. The intermediate server 130 is connected to the compression controller 111 via a wide area network 120; the intermediate server 130 is also connected to the decompression controller 112 via the wide area network 120. The wide area network 120 serves as a medium for providing 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 fiber optic cables, etc.

[0040] It should be noted that the general data dictionary determination method provided in this embodiment can generally be executed by the intermediate server 130. Correspondingly, the general data dictionary determination device provided in this embodiment can generally be located in the intermediate server 130. The general data dictionary determination method provided in this embodiment can also be executed by a server or server cluster that is different from the intermediate server 130 and capable of communicating with multiple financial data centers 110 and / or the intermediate server 130. Correspondingly, the general data dictionary determination device provided in this embodiment can also be located in a server or server cluster that is different from the intermediate server 130 and capable of communicating with multiple financial data centers 110 and / or the intermediate server 130. The general data dictionary output by the general data dictionary determination method and the general data dictionary determination device provided in this embodiment is transmitted to the compression device 113 and the decompression device 114. The compression device 113 uses the general data dictionary to compress the business data, and the decompression device 114 uses the general 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 shown is merely illustrative. Any number of financial data centers, wide area networks, and intermediate servers can be included depending on implementation needs.

[0042] The following will be based on Figure 1 The described scene, through Figures 2-3 The general data dictionary determination method of the disclosed embodiments is described in detail.

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

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

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

[0046] In operation S230, 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 sent to each financial data center.

[0047] According to embodiments of this disclosure, since the data dictionaries trained from the business data of the M financial data centers are different for each of their respective business categories, 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. The compression capability evaluation results can be scores given by each financial data center for the compression capability of the M data dictionaries of the same compression type. Data dictionaries with higher compression capabilities can receive higher scores. The set of compression capability evaluation results for the M data dictionaries can consist of the compression capability scores of each data dictionary in different financial data centers.

[0048] According to embodiments of this disclosure, since the business data contained in the M financial data centers are different, the compression type can be divided into interactive and batch types for services requiring efficient and low-latency forwarding in real-time online business (e.g., interactive services) and services requiring high bandwidth in batch backup business (e.g., batch backup services). For example, maintenance personnel classify the business data traffic mirrored to the compression controller 311 into interactive services and batch services. The compression controller 311 uses a distributed dictionary training method. Taking five financial data centers and interactive and batch services as examples, the five 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. Each of the five financial data centers trains two data dictionaries according to the classification results of the business data, namely, an interactive business data dictionary and a batch business data dictionary. For example, center A trains DictA1 and DictA2, where the number 1 represents the interactive business data dictionary and the number 2 represents the batch business data dictionary. A total of 10 data dictionaries are trained from the five centers.

[0049] According to the embodiments of this disclosure, data dictionary synchronization is performed for financial data centers with interaction requirements, while data dictionary synchronization is not required for financial data centers without interaction requirements. Table 1 is a statistical table of data dictionary synchronization information for financial data centers. As shown in Table 1, financial data center A uses interactive business data DataA1 to train and obtains data dictionary DictA1; financial data center A uses batch business data DataA2 to train and obtain data dictionary DictA2; financial data center B uses interactive business data DataB1 to train and obtains data dictionary DictB1; financial data center B uses batch business data DataB2 to train and obtain data dictionary DictB2; similarly, financial data center C trains and obtains interactive business data dictionary DictC1 and batch business data dictionary DictC2; financial data center D trains and obtains interactive business data dictionary DictD1 and batch business data dictionary DictD2; and financial data center E trains and obtains interactive business data dictionary DictE1 and batch business data dictionary DictE2.

[0050] Table 1. Statistics on Data Dictionary Synchronization Information of Financial Data Center

[0051]

[0052] According to embodiments of this disclosure, when there is an interaction requirement between financial data center A and financial data center B, the interaction-type data dictionary DictA1 of financial data center A is synchronized to the interaction-type data dictionary of financial data center B, and the batch-type data dictionary DictA2 of financial data center A is synchronized to the batch-type data dictionary of financial data center B. At the same time, the interaction-type data dictionary DictB1 of financial data center B is synchronized to the interaction-type data dictionary of financial data center A, and the batch-type data dictionary DictB2 of financial data center B is synchronized to the batch-type data dictionary of financial data center A.

[0053] According to embodiments of this disclosure, as shown in Table 1, when there is an interaction requirement between financial data center A and financial data center C, and when there is an interaction requirement between financial data center B, financial data center C, financial data center D and financial data center E, the data dictionary of the same compression type of financial data center A, financial data center B, financial data center C, financial data center D and financial data center E is obtained according to the above synchronization process.

[0054] According to embodiments of this disclosure, each financial data center applies its own business data to perform a compression effect pre-test on its existing data dictionary. For example, financial data center A compresses its interactive business data DataA1 based on its currently available data dictionary DictA1, obtaining a compression ratio WA1A; it compresses the interactive business data DataA1 based on its currently available data dictionary DictB1, obtaining a compression ratio WB1A; and it compresses the interactive business data DataA1 based on its currently available data dictionary DictC1, obtaining a compression ratio WC1A. The above steps are repeated to obtain the compression ratio for each financial data center's compression effect pre-test. The compression ratio obtained by each financial data center using its business data to perform the compression effect pre-test on its existing data dictionary is represented as follows: the first letter W represents the compression ratio; the second letter indicates which financial data center trained the data dictionary; the third letter represents the compression category (1 for interactive business, 2 for batch business); and the fourth letter indicates which financial data center the business data originates from.

[0055] According to embodiments of this disclosure, after sorting the data dictionaries of the same compression category in each financial data center, the number of data dictionaries for each compression category is 5. Based on the sorting results of the data dictionaries of the same compression category in each financial data center, the data dictionaries are scored, thus obtaining the compression capability evaluation results of each financial data center for the M data dictionaries of the same compression type. Based on the scores of the data dictionaries of the same compression category in each financial data center, a group of compression capability evaluation results for each of the M data dictionaries is obtained.

[0056] According to embodiments of this disclosure, the compression capability assessment result set can be composed of the compression capability scores of the same data dictionary in M ​​financial data centers. The compression capability of each data dictionary can be comprehensively evaluated based on the compression capability assessment result sets of the M data dictionaries and the compression ratios of the same data dictionary in the M financial data centers, resulting in a general compression assessment result for each of the M data dictionaries.

[0057] According to embodiments of this disclosure, after obtaining the general compression evaluation results of each of the M data dictionaries, the general compression evaluation results of each of the M data dictionaries can be sorted, and the data dictionary with the highest general compression evaluation result can be determined as the general data dictionary.

[0058] According to embodiments of this disclosure, the method of quantitatively evaluating the compression capability of a data dictionary can more objectively determine the compression capability of each data dictionary and more accurately describe the compression capability of each data dictionary, facilitating comparison, analysis, and calculation in subsequent steps.

[0059] According to the embodiments of this disclosure, the compression capability scores of the same data dictionary in each of the M financial data centers are an objective assessment of the compression capability of the data dictionary, while the compression ratio of the same data dictionary in each of the M financial data centers is the actual compression effect of the data dictionary in actual application. 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 the embodiments of this disclosure, by determining the data dictionary with the highest general compression evaluation result from M data dictionaries as the general data dictionary, it means that the data dictionary with the highest general compression evaluation result has a better compression effect in all financial data centers, and also indicates that the general data dictionary is the most universal among all data dictionaries.

[0061] According to embodiments of this disclosure, by quantifying and evaluating the compression capabilities of each of the M data dictionaries, the compression capabilities of each data dictionary can be reflected more objectively. Quantifying the general compression evaluation results of each of the M data dictionaries into numerical values ​​reduces the interference of subjective factors and can more objectively reflect the general compression capabilities of each data dictionary. The general compression capabilities of each of the M data dictionaries can reflect the comprehensive effect and capability of each data dictionary in processing business data of all business categories included in the financial data center. Determining the data dictionary with the highest generality from the M data dictionaries as the general data dictionary gives the general data dictionary greater universality.

[0062] According to embodiments of this disclosure, the compression capability assessment results of each of the M data dictionaries sent by each financial data center are determined as follows: when the financial data center has a data dictionary, the compression capability assessment of the business data of the business categories stored in the financial data center is performed using the data dictionary to obtain an initial compression capability assessment result; when the financial data center does not have a data dictionary, a predetermined compression capability assessment result is used as the initial compression capability assessment result of the data dictionary; the initial compression capability assessment results of each of the M data dictionaries are optimized to obtain the compression capability assessment results of each of the M data dictionaries.

[0063] According to embodiments of this disclosure, the compression capability evaluation results of the M data dictionaries sent by each financial data center include various scenarios. For example, when multiple financial data centers have interaction requirements, the data dictionaries trained by other financial data centers in each financial data center with interaction requirements are used to evaluate the compression capability of the business data of the business categories contained in that financial data center using the data dictionaries possessed by that financial data center. The initial compression capability evaluation result obtained is the compression ratio of that data dictionary or a score for the compression ratio. The score reflects the magnitude of the compression capability of that data dictionary relative to other data dictionaries. The initial compression capability evaluation result of the data dictionary not possessed by that financial data center for compressing the business data of the business categories contained in that financial data center is a preset fixed value. This fixed value is lower than the minimum score or the minimum compression ratio of the data dictionary possessed by that financial data center. For example, the preset fixed value can be set to 0.

[0064] According to embodiments of this disclosure, when there is no interaction requirement between financial data centers, that is, when there is no data exchange between financial data centers, the financial data centers without interaction requirements do not synchronize their data dictionaries. Only when there is interaction requirement between financial data centers will the data dictionaries be synchronized.

[0065] According to embodiments of this disclosure, Table 2 is a statistical table of data dictionary compression ratios. As shown in Table 2, the compression ratio of the data dictionary in a financial data center without interactive requirements is set to 0. The data dictionaries are sorted according to their compression ratios; the higher the compression ratio, the better the data dictionary and the higher its ranking. For financial data centers without interactive requirements, the data dictionary is padded with 0 during sorting, and the padding is performed according to the compression category.

[0066] Table 2. Data dictionary compression ratio sorting table

[0067]

[0068] According to embodiments of this disclosure, the initial compression capability assessment results can be optimized by sorting the initial compression capability assessment results in descending order, and scoring the corresponding data dictionaries based on the sorted initial compression capability assessment results to obtain a score for each data dictionary. Each data dictionary is scored in descending order of the sorting results, and the score decreases sequentially according to the sorting results.

[0069] According to the embodiments of this disclosure, as shown in Table 2, the sorted data dictionaries are scored for the first time. Each financial data center has 5 data dictionaries for interactive and batch business. The data dictionary ranked first in each compressed category receives a score of 5, the second-ranked receives a score of 4, and so on. Data dictionaries without interactive requirements receive a score of 0. Table 3 is a data dictionary generality evaluation table, as shown in Table 3.

[0070] Table 3 is the data dictionary generality evaluation table.

[0071]

[0072] According to the embodiments of this disclosure, in Table 3, taking DictA1 as an example, during the first scoring, DictA1's compression ratio ranking result in financial data center A is first, so DictA1's score in financial data center A is 5. DictA1's compression ratio ranking result in financial data center B is second, so DictA1's score in financial data center B is 4. DictA1's compression ratio ranking result in financial data center C is second, so DictA1's score in financial data center C is 4. Since there is no interaction requirement between financial data center A and financial data centers D and E, DictA1's score in financial data centers D and E is 0.

[0073] According to embodiments of this disclosure, following the scoring steps described above, the initial scores of DictA2 in financial data centers A, B, C, D, and E are 5, 3, 3, 0, and 0, respectively. The initial scores of DictB1 in financial data centers A, B, C, D, and E are 3, 5, 3, 4, and 3, respectively. The initial scores of DictB2 in financial data centers A, B, C, D, and E are 4, 5, 2, 3, and 4, respectively. Applying the same pattern, the initial scores of DictC1, DictC2, DictD1, DictD2, DictE1, and DictE2 in financial data centers A, B, C, D, and E are obtained, respectively. The initial scores of each data dictionary in all financial data centers are used as the respective compression capability evaluation result group for each data dictionary. According to embodiments of this disclosure, the compression capability assessment results of the M data dictionaries sent by each financial data center include two scenarios: one where the financial data center possesses a data dictionary and the other where it does not. Financial data centers without interaction needs do not need to possess each other's data dictionaries, as they will not interact or process each other's business data. Therefore, the data dictionaries not possessed by the financial data center are used as the initial compression capability assessment results based on a predetermined compression capability assessment result. Setting the predetermined compression capability assessment result to 0 eliminates the impact of the data dictionaries not possessed by the financial data center on the compression capability assessment results of the M data dictionaries in subsequent calculation steps, thereby improving the accuracy of the general compression assessment results of the M data dictionaries in subsequent steps.

[0074] According to embodiments of this disclosure, based on the compression capability evaluation result groups of M data dictionaries, determining the general compression evaluation result of each of the M data dictionaries includes: obtaining the compression ratio of the data dictionaries used to compress business data of the business categories contained in each of the M data financial centers, thus obtaining a compression ratio group for the data dictionaries; and evaluating the general compression capability of each of the M data dictionaries based on the compression capability evaluation result groups and compression ratio groups, thus obtaining the general compression evaluation result for each of the M data dictionaries. According to embodiments of this disclosure, the compression ratios of the M data dictionaries can be sorted in descending order, and the compression capabilities of the sorted M data dictionaries can be scored in descending order. The scores are used as the compression capability evaluation result groups of the M data dictionaries, and the general compression capability of each of the M data dictionaries is evaluated based on their compression ratios and corresponding scores.

[0075] According to embodiments of this disclosure, the product of the compression ratio of each data dictionary in the compression ratio group 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. Then, the evaluation results of the general compression capability of the data dictionary in each financial data center are weighted averaged or summed 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 the embodiments of this disclosure, the general compression capabilities of the M data dictionaries are described from two dimensions: the compression ratio of business data of the business categories contained in each of the M data financial centers and the corresponding scoring values. This comprehensively considers the general processing capabilities of different data dictionaries for business data of different business types contained in different financial data centers. The general compression evaluation results of the M data dictionaries can more accurately reflect the general compression capabilities of each data dictionary.

[0077] According to embodiments of this disclosure, the general compression capability of each of the M data dictionaries is evaluated based on their respective compression capability evaluation result groups and compression ratio groups, resulting in a general compression evaluation result for each of the M data dictionaries. This includes: for each data dictionary, calculating the product of the compression ratio and compression capability evaluation result for the same financial data center to obtain the compression evaluation result of the data dictionary for the financial data center; and obtaining a general compression evaluation result for the data dictionary based on the compression evaluation results for each of the M financial data centers.

[0078] According to embodiments of this disclosure, as shown in Table 3, a second scoring is performed on the sorted data dictionaries. For each data dictionary, the product of the compression ratio and compression capability evaluation results 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 then 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 as follows:

[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 embodiments of this disclosure, for each data dictionary, by multiplying the compression ratio and compression capability evaluation results of each data dictionary for the same financial data center, the differences between the compression ratios of the 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 embodiments of this disclosure, the compression ratio of the data dictionary used to compress the business data of each of the M data financial centers is determined in the following manner: when the financial data center has a data dictionary, the data dictionary is used to process the business data of the business categories stored in the financial data center to obtain the compression ratio of the data dictionary; when the financial data center does not have a data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary.

[0084] According to embodiments of this disclosure, when a financial data center has 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 testing with real data. When a financial data center does not have a data dictionary, the predetermined compression ratio of the data dictionary can be set to 0.

[0085] According to embodiments of this disclosure, since the compression ratio sent by a financial data center that does not require data interaction is a predetermined compression ratio, it can be set to 0. This setting eliminates the influence of the compression ratio of the data dictionary of the financial data center that does not require data interaction on the calculation process of determining the general data dictionary in subsequent steps, and enables more accurate general compression evaluation results to be obtained in subsequent steps.

[0086] According to embodiments of this disclosure, a general data dictionary is determined from the M data dictionaries based on their respective general compression evaluation results, and the general data dictionary is sent to each financial data center. This includes: comparing the general compression evaluation results 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 financial data center, and sending the general data dictionary to each financial data center.

[0087] According to embodiments of this disclosure, the 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 for that compression type. This process is repeated for each compression type, and the general data dictionary for each compression type is then sent to each financial data center. According to embodiments of this disclosure, since the general compression evaluation result represents the general compression capability of each of the M data dictionaries of the same type, selecting the data dictionary with the highest general compression evaluation result as the general data dictionary for each financial data center ensures that the general data dictionary has the highest general compression capability across all financial data centers, the best compression effect, and the highest universality.

[0088] According to embodiments of this disclosure, each financial data center includes multiple general data dictionaries of different types; the general data dictionaries are used for data compression by: determining the compression type of the data to be compressed; determining a target general data dictionary from the multiple general data dictionaries that matches the compression type of the data to be compressed; and compressing the data to be compressed using the target general data dictionary.

[0089] According to embodiments of this disclosure, since a financial data center contains multiple general data dictionaries of different types, when processing data to be compressed using a general data dictionary, 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 embodiments of this disclosure, by determining multiple general data dictionaries of different types, processing can be performed for the corresponding compression types, thereby improving compression capabilities and enhancing compression effects. On this basis, bandwidth requirements are reduced, and the operation and maintenance costs of financial data centers are decreased.

[0091] Figure 3 A flowchart illustrating a general data dictionary determination method according to another embodiment of the present disclosure is shown.

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

[0093] In operation S310, the compression capability evaluation results of each of the M data dictionaries of the same compression type for each financial data center are obtained, and a group of compression capability evaluation results of each of the M data dictionaries is obtained.

[0094] In operation S320, the compression ratio of the business data of each of the M data financial centers is obtained using the data dictionary to compress the business data of each business category, and the compression ratio group of the data dictionary is obtained.

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

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

[0098] According to embodiments of this disclosure, the data dictionary with the highest score is selected from the data dictionary corresponding to interactive services and used as the general data dictionary for interactive services in each financial data center. Similarly, the data dictionary with the highest score is selected from the data dictionary corresponding to batch services and used as the general data dictionary for batch services in each financial data center. The general data dictionaries for interactive services and batch services are sent to the compression controller 111 and decompression controller 112 of each financial data center 110 via a wide area network 120. The compression controller 111 and decompression controller 112 of each financial data center 110 synchronize the general data dictionary to the corresponding compression device 113 and decompression device 114.

[0099] According to embodiments of this disclosure, when business data changes, such as when new or fewer services are added, the business data of the compression device 113 can be mirrored to the compression controller 111 in advance. By repeating the process of determining and updating the general data dictionary, the general data dictionary can be trained and distributed as needed without affecting the current compression service operation or increasing the compression performance of the compression device.

[0100] Based on the aforementioned general data dictionary determination method, this disclosure also provides a general data dictionary determination apparatus. The following will be combined with... Figure 4 The device is described in detail.

[0101] Figure 4 A schematic block diagram of a general data dictionary determination apparatus according to an embodiment of the present disclosure is 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 acquire the compression capability evaluation results of each financial data center for each of the M data dictionaries of the same compression type, resulting in a set of compression capability evaluation results for each of the M data dictionaries. The compression capability evaluation results characterize the ability to compress business data of the business categories contained in the financial data center using the data dictionaries. Each of the M data dictionaries corresponds one-to-one with one of the M financial data centers. Each data dictionary is trained using business data of the business categories contained in its corresponding financial data center, and the business data of the business categories contained in each of the M financial data centers are different. In one embodiment, the evaluation result acquisition module 410 can be used to execute 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 result of each of the M data dictionaries based on the respective 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 repeated here.

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

[0106] According to embodiments of this 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 each of the M data financial centers, and to obtain the compression ratio group of the data dictionary.

[0108] The general compression evaluation submodule is used to evaluate the general compression capability of each of the M data dictionaries based on their respective compression capability evaluation result groups and compression ratio groups, and obtain the general compression evaluation results of each of the M data dictionaries.

[0109] According to embodiments of this 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 the product of the compression ratio and compression capability evaluation results of each data dictionary for the same financial data center, and obtain the compression evaluation result of the data dictionary for the financial data center.

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

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

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

[0114] According to embodiments of this disclosure, any plurality of modules among 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, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this 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 hardware circuitry, 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-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. 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, which, when run, can perform corresponding functions.

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

[0116] like Figure 5As shown, an 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 portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include 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] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0118] According to embodiments of this disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 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 disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0119] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0120] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0121] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the general data dictionary determination method provided in embodiments of this disclosure.

[0122] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

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

[0124] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0125] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0127] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0128] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A general data dictionary determination method characterized by, The method comprises: obtaining the compression capability evaluation result of each financial data center for M data dictionaries of the same compression type, obtaining the compression capability evaluation result group of each of the M data dictionaries, wherein the compression capability evaluation result represents the compression capability of the data dictionary for the business data of the business category contained in the financial data center, the M data dictionaries correspond to the M financial data centers one by one, each data dictionary is obtained by training the business data of the business category contained in the financial data center corresponding to the data dictionary, and the business data of the business category contained in each of the M financial data centers is different; based on the compression capability evaluation result group of each of the M data dictionaries, determining the general compression evaluation result of each of the M data dictionaries; comprising: obtaining the compression ratio of the data dictionary for compressing the business data of the business category contained in each of the M data financial centers, obtaining the compression ratio group of the data dictionary; according to the compression capability evaluation result group and the compression ratio group of each of the M data dictionaries, evaluating the general compression capability of each of the M data dictionaries, and obtaining the general compression evaluation result of each of the M data dictionaries; based on the general compression evaluation result of each of the M data dictionaries, determining the general data dictionary from the M data dictionaries, and sending the general data dictionary to each of the financial data centers; the compression capability evaluation result of each of the M data dictionaries sent by each of the financial data centers is determined by the following method: in the case that the financial data center has the data dictionary, the compression capability of the data dictionary for the business data of the business category stored in the financial data center is evaluated, and the initial compression capability evaluation result is obtained; in the case that the financial data center does not have the data dictionary, the predetermined compression capability evaluation result is used as the initial compression capability evaluation result of the data dictionary; the initial compression capability evaluation result of each of the M data dictionaries is optimized, and the compression capability evaluation result of each of the M data dictionaries is obtained; the compression ratio of the data dictionary for compressing the business data of the business category contained in each of the M data financial centers is determined by the following method: in the case that the financial data center has the data dictionary, the data dictionary is used to process the business data of the business category stored in the financial data center, and the compression ratio of the data dictionary is obtained; in the case that the financial data center does not have the data dictionary, the predetermined compression ratio is used as the compression ratio of the data dictionary; the financial data center without interaction demand does not synchronize the data dictionary, and only the financial data centers with interaction demand synchronize the data dictionary.

2. The method of claim 1, wherein, the general compression evaluation result of each of the M data dictionaries is obtained by 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, comprising: For each of the data dictionaries, a product of a compression ratio and a compression capability evaluation result of each of the data dictionaries for the same financial data center is calculated to obtain a compression evaluation result of the data dictionary for the financial data center; Based on the compression evaluation results of the M financial data centers respectively, a general compression evaluation result of the data dictionary is obtained.

3. The method of claim 1, wherein, The general data dictionary is determined from the M data dictionaries based on the general compression evaluation results of the M data dictionaries respectively, and the general data dictionary is sent to each of the financial data centers, which includes: The general data dictionary with the highest general compression evaluation result is determined as the general data dictionary of each of the financial data centers by comparing the general compression evaluation results of the M data dictionaries of the same type respectively, and the general data dictionary is sent to each of the financial data centers.

4. The method of claim 1, wherein, Each financial data center includes multiple general data dictionaries of different types; the general data dictionary is used for data compression in the following way: Determine the compression type of the data to be compressed; A target general data dictionary matching the compression type of the data to be compressed is determined from the multiple general data dictionaries; The target general data dictionary is used to compress the data to be compressed.

5. A general data dictionary determining apparatus characterized by comprising: The device includes: An evaluation result acquisition module is configured to obtain compression capability evaluation results of M data dictionaries of the same compression type for each financial data center, to obtain a compression capability evaluation result group of the M data dictionaries respectively, wherein the compression capability evaluation result represents the capability of using the data dictionary to compress business data of a business category contained in the financial data center, the M data dictionaries correspond to the M financial data centers one by one, each data dictionary is obtained by training the business data of the business category contained in the financial data center corresponding to the data dictionary, and the business data of the business category contained in the M financial data centers is different; A general dictionary evaluation module is configured to determine general compression evaluation results of the M data dictionaries based on the compression capability evaluation result groups of the M data dictionaries respectively, which includes: obtaining a compression ratio of the data dictionary for compressing business data of a business category contained in the M data financial centers, to obtain a compression ratio group of the data dictionary; according to the compression capability evaluation result groups and the compression ratio groups of the M data dictionaries respectively, evaluating the general compression capability of the M data dictionaries to obtain the general compression evaluation results of the M data dictionaries respectively; and A general dictionary determination module is configured to determine a general data dictionary from the M data dictionaries based on the general compression evaluation results of the M data dictionaries respectively, and send the general data dictionary to each of the financial data centers. The compression capability evaluation result of each of the M data dictionaries sent by the financial data center is determined in the following manner: in the case where the financial data center has the data dictionary, the data dictionary is used to evaluate the compression capability of the business data of the business category stored in the financial data center, and an initial compression capability evaluation result is obtained; in the case where the financial data center does not have the 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 the M data dictionaries are optimized to obtain the compression capability evaluation results of the M data dictionaries; The compression ratio of the data dictionary for compressing the business data of the business category 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 data dictionary is used to process the business data of the business category stored in the financial data center, and the compression ratio of the data dictionary is obtained; in the case where the financial data center does not have the data dictionary, a predetermined compression ratio is used as the compression ratio of the data dictionary; The financial data center without interaction requirement does not synchronize the data dictionary, and only the financial data centers with interaction requirement synchronize the data dictionary.

6. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, 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 1-4.

7. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the steps of the method according to any one of claims 1-4.

8. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the steps of the method according to any one of claims 1-4. The computer program or instructions are executed by the processor to implement the steps of the method according to any one of claims 1-4.

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