Multi-level database dynamic update method and system using microservice architecture

Abstract

The present invention relates to the technical field of dynamic updating of multi-level databases, and in particular to a method and system for dynamically updating a multi-level database using a microservice architecture, the method comprising: by comparing the difference in length between various segmentations and each sentence in their segmentation semantic set, and counting the frequency of occurrence of various segmentations in each sentence in their segmentation semantic set, constructing semantic information content, and determining the segmentation compactness in combination with the difference of different segmentations; by analyzing the frequency of occurrence of various segmentations in each text data, and combining the segmentation compactness, to evaluate the query level priority index, and combining the frequency of occurrence of various segmentations in each text data within a preset time length before the current moment, determining the query priority, and updating the multi-level database at the current moment. The present invention improves the efficiency of dynamic updating of multi-level databases by updating the query priority of segmentations in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-level database dynamic update, and in particular to a multi-level database dynamic update method and system using a microservice architecture. Background Art

[0002] The multi-level database in the microservice framework usually refers to the fact that different services may have their own database instances under the microservice architecture, and there may be data interaction and synchronization requirements between these database instances. This architectural design allows each microservice to manage its own data storage, thereby improving the flexibility and scalability of the system. In order to maintain data consistency in different databases in the multi-level database architecture, multi-level databases often use distributed storage. Since the data is originally stored in multiple databases, it is necessary to query the data when operating on the data. Since mobile game data requires low latency and high performance. Therefore, the multi-level database of the microservice framework for mobile games needs to be dynamically updated in real time to ensure the accuracy of the data.

[0003] Traditional technology is used for storage and update of multi-level databases. Since inverted index can quickly locate documents containing query terms, inverted index technology is often used for index positioning of multi-level databases. However, the inverted index representation method only considers the relationship between the length of data, which results in poor performance for the call of high-frequency word segmentation. Although the inverted index method can improve the query speed of data, hash conflicts are prone to occur when calculating hash codes for keywords, so the speed of constructing an index that combines inverted file index and hash table index is slow, which in turn reduces the efficiency of dynamic update of multi-level databases. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a multi-level database dynamic update method and system using a microservice architecture, and the technical solutions adopted are as follows:

[0005] In a first aspect, the present invention provides a multi-level database dynamic update method using a microservice architecture, the method comprising the following steps:

[0006] In the multi-level database, each text data at the current moment is obtained, and each text data is segmented to obtain different types of segmented words, and the sentences where the various segmented words are located are combined into a segmented semantic set of various segmented words in each text data at the current moment;

[0007] In each text data, by comparing the length difference between various segmentations and each sentence in its segmentation semantic set, and counting the frequency of occurrence of various segmentations in each sentence in its segmentation semantic set, the semantic information content of various segmentations in each text data at the current moment is constructed;

[0008] Compare the differences in characters and lengths between various segmentations and any other segmentations respectively, determine the closeness between various segmentations and any other segmentations in each text data, analyze the average distribution of the closeness between various segmentations and all other segmentations, determine the closeness index of various segmentations in each text data at the current moment, and determine the segmentation compactness of various segmentations in each text data at the current moment in combination with the semantic information content;

[0009] By analyzing the frequency of occurrence of various word segments in each text data and combining the word segment compactness, the query level priority index of various word segments in each text data at the current moment is evaluated;

[0010] In a multi-level database, the frequency of occurrence of various word segments in each text data within a preset time period before the current moment is counted, and combined with the query level priority index, the query priority of various word segments in each text data at the current moment is determined, and the multi-level database at the current moment is updated.

[0011] Preferably, the expression of the semantic information content of various word segments in each text data at the current moment is: ; In the formula, represents the semantic information content of the a-th word segment in the text data i at the current moment; , They respectively represent the length of the bth sentence and the length of the ath word in the word segmentation semantic set of the ath word segmentation in the text data i at the current moment; represents the word frequency-inverse document rate of the a-th word in the text data i at the current moment in the b-th sentence of its word segmentation semantic set; It represents the number of all sentences in the semantic set of the a-th word segmentation in the text data i at the current moment.

[0012] Preferably, the expression for the closeness between various segmentations and any other segmentations in each text data is: ; In the formula, Indicates the closeness between the a-th and j-th participles in the text data i at the current moment; , They represent the length of the a-th word segment and the length of the j-th word segment in the text data i at the current moment respectively; It represents the difference in characters between the a-th word segmentation and the j-th word segmentation in the text data i at the current moment; max() represents the maximum value function.

[0013] Preferably, the difference in characters between the ath type of word segmentation and the jth type of word segmentation in the text data i at the current moment is the edit distance between the ath type of word segmentation and the jth type of word segmentation in the text data i at the current moment.

[0014] Preferably, the closeness index of various participles in each text data at the current moment is the average of the closeness between various participles in each text data at the current moment and all other participles.

[0015] Preferably, the word segmentation compactness of various word segmentations in each text data at the current moment is the product of the compactness index of various word segmentations in each text data at the current moment and the semantic information content.

[0016] Preferably, the expression of the query level priority index of various word segments in each text data at the current moment is: ; In the formula, Indicates the query level priority index of the a-th word segmentation in the text data i at the current moment; Indicates the compactness of the a-th word segmentation in the text data i at the current moment; represents the frequency of occurrence of the ath word segment in the text data i at the current moment; norm() represents the normalization function.

[0017] Preferably, the method for determining the query priority of various word segments in each text data at the current moment is:

[0018] In the multi-level database, the product of the frequency of occurrence of various word segments in each text data within a preset time period before the current moment and the query level priority index is used as the query priority of various word segments in each text data at the current moment.

[0019] Preferably, the updating of the multi-level database at the current moment includes:

[0020] All text data in the current multi-level database and the query priorities of all word segments therein are used as input of the inverted index algorithm, and the inverted index table of the current multi-level database is output to update the multi-level database at the current moment.

[0021] In a second aspect, the present invention also provides a multi-level database dynamic update system using a microservice architecture, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned methods for dynamically updating a multi-level database using a microservice architecture are implemented.

[0022] The present invention has at least the following beneficial effects:

[0023] The present invention constructs semantic information content by analyzing the difference in length between a segmentation word and the sentence in which it is located, as well as the frequency of the segmentation word in the sentence, and can more accurately evaluate the importance in the text data, thereby quickly locating the relevant data in the multi-level database according to the importance of the segmentation word information content, and quickly updating it, thereby improving the efficiency of the dynamic update of the multi-level database; further, by the difference between different types of segmentation words, and in combination with the semantic information content, the segmentation word compactness is constructed, and by analyzing the compactness between the segmentation words, the index construction can be optimized, making the index more compact, improving the query efficiency in the multi-level database, thereby improving the efficiency of the dynamic update of the multi-level database; further, the frequency of occurrence of various segmentations in each text data within a preset time before the current moment is integrated, and combined with the query level priority index, the query priority is constructed, which can more accurately evaluate the query priority of the segmentation word, thereby improving the data retrieval efficiency, and thereby improving the efficiency of the dynamic update of the multi-level database. The present invention responds to the changes of data in the database in real time by updating the query priority of the segmentation word in real time, while maintaining data consistency, improving the efficiency of the dynamic update of the multi-level database. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a multi-level database dynamic update method using a microservice architecture provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a query priority extraction process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the multi-level database dynamic update method and system using a microservice architecture proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0028] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0029] The specific scheme of the multi-level database dynamic update method and system using the microservice architecture provided by the present invention is described in detail below with reference to the accompanying drawings.

[0030] See also Figure 1 , which shows a flowchart of a multi-level database dynamic update method using a microservice architecture provided by an embodiment of the present invention, the method comprising the following steps:

[0031] Step S1: In a multi-level database, obtain various text data at the current moment, and perform word segmentation processing on each text data to obtain different types of word segments, and organize the sentences where various word segments are located into word segmentation semantic sets of various word segments in each text data at the current moment.

[0032] In this embodiment, the game platform data is taken as an example. In the multi-level database, each text data at the current moment is obtained, and each text data is used as the input of the forward maximum matching algorithm, and all the segmented words in each text data are output, and the sentences where the various segmented words are located are composed into the segmented semantic set of various segmented words in each text data at the current moment. For example, if the segmented word is "skill", all sentences containing the character "skill" are composed into the segmented semantic set.

[0033] Among them, the forward maximum matching algorithm is a well-known technology, and its specific principle will not be repeated here.

[0034] Step S2: In each text data, by comparing the length difference between various segmentations and each sentence in its segmentation semantic set, and counting the frequency of occurrence of various segmentations in each sentence in its segmentation semantic set, the semantic information content of various segmentations in each text data at the current moment is constructed.

[0035] In a multi-level database, word segmentation is the process of breaking down text data into smaller, more manageable and searchable units. When a word segment has high information content, it is crucial for data retrieval, full-text indexing, intelligent filtering, user behavior analysis, etc. because of the key data and semantic information it carries. Word segmentations with high information content help improve the relevance of searches, speed up query responses, and enhance semantic understanding. Therefore, by comparing the length differences between various word segmentations and each sentence in their word segmentation semantic set, and counting the frequency of occurrence of various word segmentations in each sentence in their word segmentation semantic set, the semantic information content of various word segmentations in each text data at the current moment is constructed, specifically:

[0036] The semantic information content of the a-th word segment in the text data i at the current moment The expression is: ; In the formula, , They respectively represent the length of the bth sentence and the length of the ath word in the word segmentation semantic set of the ath word segmentation in the text data i at the current moment; represents the word frequency-inverse document rate of the a-th word in the text data i at the current moment in the b-th sentence of its word segmentation semantic set; It represents the number of all sentences in the semantic set of the a-th word segmentation in the text data i at the current moment.

[0037] It should be noted that, in this embodiment, all references to the length of a sentence or the length of a segmented word refer to the total number of characters included in the sentence or the total number of characters included in the segmented word.

[0038] The calculation process of word frequency-inverse document rate is a well-known technology, and its specific calculation principle will not be described in detail.

[0039] According to the semantic information content of various segmented words in each text data at the current moment, it can be understood that when the difference between the length of the segmented words in the text data and the length of the sentence in the segmented word semantic set is smaller, that is, The larger the value is, the greater the proportion of the participle in the sentence, the higher the information content of the participle, and the higher the importance of the participle in each sentence in its participle semantic set, that is, The larger the value is, the more information the word contains, and the greater the semantic information content of the word is; conversely, the greater the difference between the length of the word in the text data and the length of the sentence in the word semantic set is, The smaller it is, the smaller the proportion of the participle in the sentence, the less information the participle contains, and the lower the importance of the participle in each sentence in its participle semantic set. The smaller it is, the less information the word contains, and the smaller the semantic information content of the word.

[0040] At this point, by analyzing the importance of each word in the text data, its semantic information content is obtained. In the process of database management and data analysis, the importance of the word can be identified according to the size of the semantic information content, thereby improving the efficiency of data search.

[0041] Step S3: In each text data, respectively compare the differences in characters and lengths between various participles and any other participles, determine the closeness between various participles and any other participles in each text data, and analyze the average distribution of the closeness between various participles and all other participles, determine the closeness index of various participles in each text data at the current moment, and determine the compactness of various participles in each text data at the current moment in combination with the semantic information content.

[0042] For word segments of different lengths, the length represents the data stored in the database. Longer word segments can reduce the waste of storage space, especially for mobile game data, which contains a large number of similar or identical data sets. For example, the word segments of "click 1 skill" and "click 2 skill" have high similarity. During query traversal, the higher the similarity between the word segment and the other word segment, the more compact the index between the word segment can be, so that the data can be queried faster.

[0043] Therefore, by comparing the differences in characters and lengths between various segmentations and any other segmentations, and combining the semantic information content, the segmentation compactness is determined, specifically:

[0044] The compactness of the a-th word segmentation in text data i The expression is: ; , They represent the length of the a-th word segment and the length of the j-th word segment in the text data i at the current moment respectively; It represents the difference in characters between the a-th word segmentation and the j-th word segmentation in the text data i at the current moment; max() represents the maximum value function.

[0045] Among them, the difference in characters between the ath word segmentation and the jth word segmentation in the text data i at the current moment is the edit distance between the ath word segmentation and the jth word segmentation in the text data i at the current moment. The calculation process of the edit distance is a well-known technology, and its specific calculation process will not be repeated here.

[0046] According to the compactness of various word segments in each text data, it can be understood that the compactness between word segments is represented by calculating the edit distance between two word segments. When the difference between the two word segments is smaller, the edit distance is smaller and the compactness of the word segment is greater. In addition, the maximum word segment length The greater the difference from the edit distance, the greater the compactness between the two participles, that is, the greater the closeness between the two participles; conversely, when the difference between the two participles is greater, the edit distance is also greater, and the compactness of the participles is smaller; in addition, the maximum participle length The smaller the difference from the edit distance, the less compact the two participles are, that is, the smaller the closeness between the two participles is.

[0047] Furthermore, the average of the closeness between various participles in each text data at the current moment and all other participles is used as the closeness index of various participles in each text data at the current moment.

[0048] The product of the compactness index and the semantic information content of various word segments in each text data at the current moment is taken as the word segmentation compactness of various word segments in each text data at the current moment.

[0049] According to the compactness of various segmentations in each text data at the current moment, it can be understood that the larger the segmentation compactness index is, the higher the semantic information content is, and the larger the segmentation compactness is, which means that the segmentation is more important in the text data, and it is more accurate and efficient in transmitting information and retrieving information; conversely, the smaller the segmentation compactness index is, the lower the semantic information content is, and the smaller the segmentation compactness is, which means that the segmentation is less important in the text data.

[0050] Step S4: By analyzing the frequency of occurrence of various word segments in each text data and combining the word segment compactness, the query level priority index of various word segments in each text data at the current moment is evaluated.

[0051] In the field of mobile games, by analyzing the behavior of players, it can be concluded that different players will repeatedly challenge the same level and use the same skills. These repetitive behaviors will generate a large amount of similar or identical data. Therefore, the frequency of occurrence of different participles may be significantly different. The more times a participle appears, the more reusable it is. When updating and querying data, the participle in the query data can be used first to increase the query rate. Therefore, for different participles, the higher the frequency of the participle, the higher the traversal priority of the participle should be when querying data.

[0052] Therefore, by analyzing the frequency of occurrence of various word segments in each text data and combining the word segmentation compactness, the query level priority index of various word segments in each text data at the current moment is evaluated, specifically:

[0053] The query priority index of the a-th word segmentation in the text data i at the current moment The expression is: ; In the formula, Indicates the compactness of the a-th word segmentation in the text data i at the current moment; represents the frequency of occurrence of the ath word segment in the text data i at the current moment; norm() represents the normalization function.

[0054] It should be noted that for the query level priority index of the word segmentation, since the word segmentation compactness may be zero, but the word segmentation has already appeared during the calculation, the probability of the word segmentation appearing is not zero. Therefore, the word segmentation compactness is added with 1 to ensure that the query level priority index of the word segmentation does not have multiple zeros.

[0055] According to the query level priority index of various segmentations in each text data at the current moment, it can be understood that the greater the segmentation compactness of the segmentation, the higher the importance of the segmentation, and the higher the query level should be. Therefore, the larger the query level priority index, and the more frequent the segmentation appears in the text data, that is, The larger the value, the more popular or important the word is, so the value of its query priority index should be larger; conversely, when the word compactness is smaller, it means that the importance of the word is lower, so the query priority index is smaller, and when the frequency of the word in the text data is less, that is, The smaller it is, the less important the word is, so the value of its query priority index should be smaller.

[0056] At this point, the query level priority index of the word segmentation is obtained by comprehensively considering the word segmentation compactness and the frequency of the word segmentation in the text data. By improving the query level priority index of high-frequency word segmentations, the system can respond to dynamic update operations on data more quickly.

[0057] Step S5: In the multi-level database, count the frequencies of occurrence of various word segments in each text data within a preset time period before the current moment, and determine the query priority of various word segments in each text data at the current moment in combination with the query level priority index, and update the multi-level database at the current moment.

[0058] When the data is dynamically updated, the frequency of use of each word segmentation is constantly updated due to the continuous query and update of the data in the database. For example, when a new level appears in a mobile game, new data will be generated, causing the frequency of use of the word segmentation to constantly change.

[0059] Therefore, in the multi-level database, the frequency of occurrence of various word segments in each text data within a preset time length before the current moment is counted, wherein the value of the preset time length is set manually. In this embodiment, the value of the preset time length is 1 minute, and the implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0060] Furthermore, the frequency of occurrence of various word segments in each text data within a preset time period before the current moment is comprehensively considered, and combined with the query level priority index, the query priority of various word segments in each text data at the current moment is determined, specifically:

[0061] In the multi-level database, the product of the frequency of occurrence of various word segments in each text data within a preset time period before the current moment and the query level priority index is used as the query priority of various word segments in each text data at the current moment.

[0062] According to the query priority of various word segments in each text data at the current moment, it can be understood that the larger the query level priority index of the word segmentation, and because different users of mobile game data require different query word segmentations at different levels, when a new level appears, most mobile game users experience the new level, so new high-frequency word segmentations will appear, causing the frequency of word segmentation appearance in the recent time to increase sharply. Therefore, the high-frequency word segmentation in the recent time should have a higher query level, that is, the greater the query priority; conversely, the smaller the query level priority index of the word segmentation, the smaller the frequency of word segmentation appearance in the recent time, so the query priority of the high-frequency word segmentation in the recent time should be lower, that is, the smaller the query priority.

[0063] Preferably, the query priority extraction process diagram provided in this embodiment is as follows: Figure 2 shown.

[0064] Furthermore, all text data in the multi-level database at the current moment and the query priority of all word segments therein are used as input of the inverted index algorithm, and the inverted index table of the multi-level database at the current moment is output to update the multi-level database at the current moment.

[0065] The process of constructing an inverted index table using an inverted index algorithm is a well-known technology, and its specific principle and process will not be described in detail.

[0066] It should be noted that the specific process of updating the multi-level database at the current moment is as follows:

[0067] If it is necessary to delete a certain text data in the multi-level database at the current moment, various participles in the text data to be deleted are extracted and compared with the inverted index table at the current moment, the addresses of all the text data to be deleted in the multi-level database are obtained, all the text data to be deleted are deleted, and the information of the text data in the inverted index table is deleted at the same time; if it is necessary to add a certain text data to the multi-level database, various participles in the text data to be added are extracted and stored in the multi-level database, and the corresponding text data information is added to the inverted index table at the same time.

[0068] So far, this embodiment preprocesses data and calculates the semantic information content, word segmentation compactness, and query priority of the word segmentation, and constructs an inverted index table, which can significantly improve the relevance of data retrieval and query response speed in multi-level databases, enhance semantic understanding, and thus improve the efficiency of dynamic updating of multi-level databases.

[0069] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a multi-level database dynamic update system using a microservice architecture, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the methods described in the above-mentioned multi-level database dynamic update method using a microservice architecture are implemented.

[0070] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-level database dynamic update method using a microservice architecture, characterized in that: The method comprises the following steps: In the multi-level database, each text data at the current moment is obtained, and each text data is segmented to obtain different types of segmented words, and the sentences where the various segmented words are located are combined into a segmented semantic set of various segmented words in each text data at the current moment; In each text data, by comparing the length difference between various segmentations and each sentence in its segmentation semantic set, and counting the frequency of occurrence of various segmentations in each sentence in its segmentation semantic set, the semantic information content of various segmentations in each text data at the current moment is constructed; Compare the differences in characters and lengths between various segmentations and any other segmentations respectively, determine the closeness between various segmentations and any other segmentations in each text data, analyze the average distribution of the closeness between various segmentations and all other segmentations, determine the closeness index of various segmentations in each text data at the current moment, and determine the segmentation compactness of various segmentations in each text data at the current moment in combination with the semantic information content; By analyzing the frequency of occurrence of various word segments in each text data and combining the word segment compactness, the query level priority index of various word segments in each text data at the current moment is evaluated; In a multi-level database, the frequency of occurrence of various word segments in each text data within a preset time length before the current moment is counted, and combined with the query level priority index, the query priority of various word segments in each text data at the current moment is determined; all text data in the multi-level database at the current moment and the query priority of all word segments therein are used as the input of the inverted index algorithm, the inverted index table of the multi-level database at the current moment is output, and the multi-level database at the current moment is updated.

2. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The expression of the semantic information content of various word segments in each text data at the current moment is: In the formula, yu i,a represents the semantic information content of the ath word segment in the text data i at the current moment; WL i,a They respectively represent the length of the bth sentence and the length of the ath word in the word segmentation semantic set of the ath word segmentation in the text data i at the current moment; M represents the word frequency-inverse document rate of the a-th word in the text data i at the current moment in the b-th sentence of its word segmentation semantic set; i,a It represents the number of all sentences in the semantic set of the a-th word segmentation in the text data i at the current moment.

3. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The expression of the closeness between various segmentations and any other segmentations in each text data is: In the formula, Wl represents the closeness between the a-th word segmentation and the j-th word segmentation in the text data i at the current moment; i,a 、Wl i,j They respectively represent the length of the a-th word segment and the length of the j-th word segment in the text data i at the current moment; It represents the difference in characters between the a-th word segmentation and the j-th word segmentation in the text data i at the current moment; max() represents the maximum value function.

4. The multi-level database dynamic update method using microservice architecture according to claim 3 is characterized in that: The difference in characters between the ath word segmentation and the jth word segmentation in the text data i at the current moment is the edit distance between the ath word segmentation and the jth word segmentation in the text data i at the current moment.

5. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The closeness index of various word segments in each text data at the current moment is the average of the closeness between various word segments in each text data at the current moment and all other word segments.

6. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The word segmentation compactness of various word segments in each text data at the current moment is the product of the compactness index of various word segments in each text data at the current moment and the semantic information content.

7. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The expression of the query level priority index of various word segments in each text data at the current moment is: i,a =(W i,a +1)×norm(S i,a );In the formula, Code i,a represents the query level priority index of the a-th word segment in the text data i at the current moment; W i,a S represents the compactness of the a-th word segmentation in the text data i at the current moment; i,a It indicates the frequency of occurrence of the ath word segment in the text data i at the current moment; norm() indicates the normalization function.

8. The multi-level database dynamic update method using microservice architecture according to claim 1 is characterized in that: The method for determining the query priority of various word segments in each text data at the current moment is: In the multi-level database, the product of the frequency of occurrence of various word segments in each text data within a preset time period before the current moment and the query level priority index is used as the query priority of various word segments in each text data at the current moment.

9. A multi-level database dynamic update system using a microservice architecture, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the multi-level database dynamic update method using a microservice architecture according to any one of claims 1 to 8 are implemented.

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