WO2021000671A1

WO2021000671A1 - Database query method and apparatus, server and medium

Info

Publication number: WO2021000671A1
Application number: PCT/CN2020/092811
Authority: WO
Inventors: 郑毅
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-07-03
Filing date: 2020-05-28
Publication date: 2021-01-07
Also published as: CN110471916B; CN110471916A

Abstract

Disclosed are a database query method and apparatus, a server and a medium, which are applicable to the technical field of data processing. The method comprises: acquiring a classification tree model, and generating indexes of all current nodes according to parent nodes of all the current nodes (S101); if a node update request is received, extracting a keyword from the node update request, and determining, on the basis of the keyword and description information of all the nodes, whether to update the classification tree model (S102); if it is determined that the classification tree model is to be updated, selecting a node from the classification tree model as a selected node, adding a child node to the selected node, and generating an index of the child node (S103); after a query instruction input by a user is received, determining whether to execute the query instruction (S104); and if it is determined that the query instruction is to be executed, extracting a target node from the query instruction, and querying and outputting, by means of the indexes of all the nodes in the updated classification tree model, a localized classification tree model with the target node as a root node (S105). The efficiency when the classification tree model is queried or updated is improved.

Description

Database query method, device, server and medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on July 3, 2019, the application number is 201910594593.1, and the invention title is "Database query methods, devices, servers, and media", the entire content of which is incorporated by reference In this application.

Technical field

This application belongs to the field of data processing technology, and in particular relates to a database query method, device, server and medium.

Background technique

At present, when managers of large organizations need to understand the structure of related organizations or the production process information of their products, the server needs to query the related node (related node) of a target node in the related tree structure in the database according to the user's instruction. It can be: a parent node, a child node, or all subordinate nodes with the target node as the root node, etc.), and the prior art is implemented through a recursive database query.

However, the inventor found that the existing database recursive query method has low query efficiency when the amount of data in the tree structure is large. In addition, when the user needs to modify the related tree structure, the modification flexibility and intelligence are also poor. More importantly, when the tree structure is updated, the database recursive query may be disturbed, the difficulty further increases, and the query efficiency may further slow down.

Summary of the invention

In view of this, the embodiments of the present application provide a database query method, device, server, and medium to solve the problems of poor flexibility and low query efficiency in database update in the prior art.

The first aspect of the embodiments of the present application provides a database query method, including: obtaining a classification tree model currently used to characterize the relationship between each data set in the database, and according to the parent node of each node in the current classification tree model , Generate the index of each current node; if a node update request for adding a node is received, keywords are extracted from the node update request according to the node update request, and the current classification tree model is retrieved Node’s description information, and based on the keywords and the description information, determine whether to update the current classification tree model; if it is determined to update the current classification tree model, then from the classification tree model Select a node as the selected node in the selected node, add child nodes to the selected node, and generate the index of the child node to update the classification tree model; after receiving the query instruction input by the user, if it is determined to execute all The query instruction extracts a target node from the query instruction, and queries and outputs a local classification tree model with the target node as a root node through the index of each node in the updated classification tree model.

A second aspect of the embodiments of the present application provides a database query device, including:

The obtaining module is used to obtain the classification tree model currently used to characterize the relationship between the data sets in the database, and to generate the index of each node according to the parent node of each node in the current classification tree model; the judgment module is used for After receiving a node update request for adding a node, multiple keywords are extracted from the node update request according to the node update request, and the description information of each node in the current classification tree model is retrieved, and based on The keyword and the description information determine whether to update the current classification tree model; an update module is used to select from the classification tree model if it is determined to update the current classification tree model A node is used as the selected node, and a child node is added to the selected node, and the index of the child node is generated to update the classification tree model; the query module is used to, after receiving the query instruction input by the user, If it is determined to execute the query instruction, extract the target node from the query instruction, and query and output the local classification tree with the target node as the root node through the index of each node in the updated classification tree model model.

The third aspect of the embodiments of the present application provides a server including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, To implement the steps of the method provided by the first aspect of the embodiments of the present application, for example, when the processor executes the computer program, the following steps may be implemented: obtain the classification tree model currently used to characterize the relationship between the data sets in the database, and The parent node of each node in the current classification tree model generates the index of each current node; if a node update request for adding a node is received, the key is extracted from the node update request according to the node update request Words, retrieve the description information of each node in the current classification tree model, and based on the keywords and the description information, determine whether to update the current classification tree model; The classification tree model is updated, a node is selected from the classification tree model as the selected node, and child nodes are added to the selected node, and the index of the child node is generated to update the classification tree model; After receiving the query instruction input by the user, if it is determined to execute the query instruction, the target node is extracted from the query instruction, and the index of each node in the updated classification tree model is used to query and output The target node is the local classification tree model of the root node.

The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the The steps of the method, for example, when the computer program is executed by the processor, the following steps can be achieved: obtaining the classification tree model currently used to characterize the relationship between the data sets in the database, and according to the parent of each node in the current classification tree model Node, generate the index of each current node; if a node update request for adding a node is received, keywords are extracted from the node update request according to the node update request, and the current classification tree model is called The description information of each node, and based on the keywords and the description information, determine whether to update the current classification tree model; if it is determined to update the current classification tree model, then from the classification tree Select a node in the model as the selected node, add child nodes to the selected node, generate the index of the child node to update the classification tree model; after receiving the query instruction input by the user, if it is determined to execute The query instruction extracts a target node from the query instruction, and queries and outputs a local classification tree model with the target node as a root node through the index of each node in the updated classification tree model.

In the embodiments of the present application, when querying or updating a complex classification tree model, fast processing can be performed based on the index of each node, which significantly improves the completion efficiency of related tasks.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art.

FIG. 1 is an implementation flowchart of a database query method provided by an embodiment of the present application;

Figure 2 is a schematic diagram of a classification tree model provided by an embodiment of the present application;

3 is a specific implementation flowchart of the database query method S102 provided by an embodiment of the present application;

FIG. 4 is a specific implementation flowchart of the database query method S104 provided by the embodiment of the present application;

Figure 5 is a structural block diagram of a database query device provided by an embodiment of the present application;

Fig. 6 is a schematic diagram of a server provided by an embodiment of the present application.

Detailed ways

The technical solution of the present application can be applied to the field of artificial intelligence or big data technology, and solves the problems of poor flexibility and low query efficiency in database updates in the prior art.

Example one

Fig. 1 shows an implementation process of a database query method provided by an embodiment of the present application, and the method process includes steps S101 to S106. The specific implementation principle of each step is as follows.

In S101, the current classification tree model used to characterize the relationship between the data sets in the database is obtained, and the current index of each node is generated according to the parent node of each node in the current classification tree model.

In the embodiment of the present application, the data set may be related data of an organization in a company structure. For example, each data set may be data of different departments, branches, and subsidiaries in the company structure. It is worth noting that, since the structure of a company from the head office will be decomposed into multiple organizations at different levels according to certain classification rules, the corporate structure of a company can form a classification tree, and each organization A data set formed by related data of is a node in the classification tree. Therefore, in summary, the classification tree model in the embodiment of the present application can be used to characterize the relationship between each data set in the database, where the root node of the classification tree model represents the data set corresponding to the head office.

It is worth noting that in this embodiment of the present application, each node of the classification tree model has a corresponding node ID, and the node ID is used to uniquely determine a node. As shown in Figure 2, the node ID of each node can be represented by letters. For example, the node ID of the root node is A, and the node IDs of the second-level nodes are B and C respectively. Obviously, in order to facilitate the description and further introduction below, FIG. 2 is only a very simple classification tree model. The number of nodes of the classification tree model processed in the real project in the embodiment of this application is much more than that of the classification tree in FIG. 2 The number of nodes in the model. Optionally, when the number of nodes is large, multiple digits can be selected as the node IDs of different nodes.

It is worth noting that, in order to avoid the problem of low query efficiency in the traditional database recursive query method when the data volume of the classification tree model is large, the first step of the embodiment of the present application requires generating each node in the classification tree model. The index of each node will be quickly searched below based on the index of each node.

Optionally, generating the index of each node in the classification tree model includes: selecting nodes other than the root node from the current classification tree model, and a path from the parent node of the node being selected to the root node The IDs of all the nodes on the above are combined in the order from the root node to the lower nodes, as the index of the node being selected, so as to traverse all nodes except the root node in the current classification tree model , To generate the index of each current node.

Exemplarily, still taking FIG. 2 as an example, the embodiment of the present application will traverse every node in the classification tree model except for the root node A. Obviously, each node will be selected during the traversal process. Assuming that node D is currently selected, the IDs of all nodes on the path from node D to root node A are combined in the order from the root node to the lower nodes to generate the index of node D being selected. In Figure 2, the index of node D is A! B! D. By analogy, the index of other non-root nodes can be determined.

Understandably, because the index of each node is generated, in the following query or update process, only the data operation is required for the index of each node, and there is no need to recursively analyze the data of each node in this query or update. Connection relationship, which can increase the rate of related operations.

In S102, if a node update request for adding a node is received, keywords are extracted from the node update request, and the description information of each node in the current classification tree model is retrieved, and based on the key Words and the description information to determine whether to update the current classification tree model.

Understandably, a classification tree model is not constant after it is generated, and operations such as addition, deletion, and modification may occur to change the connection relationship of the nodes in the classification tree model or add or delete nodes. The user can pass the request of adding, deleting and modifying the node in the classification tree model to the server through the node update request. After the server parses the node update request, it completes the addition, deletion, and modification operations according to the user's relevant permissions. In the embodiments of this application, considering that deleting and modifying nodes must ensure the accuracy of server analysis, otherwise the data security and integrity of the classification tree model will be affected. Therefore, for requests to delete and modify nodes, users must The ID of the target node and the specific steps of the requested operation are written in the update request. The server can accurately complete the operation of deleting and modifying the node according to the specific content contained in the node update request. However, considering the particularity of adding nodes, because adding nodes often has less impact on the data security and integrity of the classification tree model, in order to improve the convenience of user operations, it is not necessary to input all the information in each operation. The application embodiment provides a set of smarter methods for node update requests for adding nodes, which are detailed as follows:

It is worth noting that, in response to a node update request that a user wishes to add a new node to the current classification tree model, the embodiment of the present application provides an automatic judgment update method that is different from the prior art. In the embodiment of this application, when a user wants to add a node, he needs to input the description information of the node he wants to add into the terminal device in the form of keywords. The terminal device will generate a node update request based on these keywords and send it to the server . Optionally, the keywords may include: the level of the node to be generated, the characteristics of the node to be generated, and so on. Obviously, after the server receives a node update request, it can parse out the keywords contained in it to determine what kind of node the user wants to add to the classification tree model, and further determine whether to agree to add the node to the classification tree model .

As an embodiment of the present application, as shown in FIG. 3, the foregoing S102 includes:

S1021: Count the number of occurrences of the keyword in the description information of each node to the total number of words in each description information.

In the embodiment of the present application, each node has its corresponding description information, and the description information is used to describe various characteristics of the node. Obviously, the description information of each node is composed of multiple words, and the word set of the description information of each node can be obtained through word segmentation (the same words may exist in the word set, and they are not used in this embodiment of the application). Merge) to count the number of occurrences of the keywords in the update request in each description information. For example, suppose that in a piece of description information, the word "risk control" appears 5 times in total, then if the keyword is also "risk control", the number of occurrences of the keyword in the description information is 5. Similarly, in the embodiment of the present application, the total number of words in the description information is the total number of words in the word set of the description information without the same word combination.

S1022: Calculate the total number of nodes included in the current classification tree model as the first node number, and count the number of nodes containing the keyword in the corresponding description information as the second node number.

S1023: Calculate the degree of association between the node update request and each node by using an association degree calculation formula.

Optionally, the formula for calculating the degree of association is:

Wherein, the Y _i is the degree of association between the node update request and the node i in the classification tree model, the n _i is the number of occurrences of the keyword in the description information of the node i, and the k _i Is the total number of words in the description information of the node i, the h is the number of the first node, and the b is the number of the second node.

Understandably, in the embodiments of this application, it is understandable that the degree of association is not simply the probability of a keyword in each description information, but a combination of the number of occurrences of the keyword in the description information of each node, and each The four important parameters of the total number of words in the description information of the node, the total number of nodes contained in the current classification tree model, and the number of nodes containing the keywords in the corresponding description information, are more accurate and objective to calculate the response The parameter of the degree of relevance between the keyword and each node, that is, the degree of relevance between the node update request and each node.

S1024: If the correlation degree between at least one node in the classification tree model and the update request is not less than a preset correlation degree threshold, it is determined not to update the current classification tree model.

It is worth noting that, obviously, the higher the degree of association between a node and the node update request, the more the function of the node that the node update request hopes to create and an existing node have. Therefore, in the embodiment of the present application, if there is a node in the current classification tree model that is associated with a node update request that is higher than the preset threshold, it proves that the functions of the desired node and an existing node overlap too much. It is determined not to update the node, and a notification that "similar node already exists" is returned to the user.

S1025: If the degree of association between all nodes in the classification tree model and the update request is less than a preset degree of association threshold, it is determined to update the current classification tree model.

In this case, it is proved that there is no existing node that overlaps the function of the node that the user wants to create. Therefore, it is necessary to add a new node on the basis of the current classification tree model, and then perform node on the classification tree model. Update.

In the embodiment of the present application, the node update request can be intelligently analyzed and judged to quickly determine whether the current classification tree model needs to be updated.

In S103, if it is determined to update the current classification tree model, a node is selected from the classification tree model as the selected node, and child nodes are added to the selected node to generate the Index to update the classification tree model.

Optionally, after passing the formula:

Calculate the attribution coefficient of the node update request to each node, where W _i represents the attribution coefficient of the node update request to node i, and the Y _i is the degree of association between the node update request and node i, so The F _i is the degree of association between the node update request and the parent node of node i, and the B _i is the average value of the degree of association between the node update request and the sibling nodes of node i; the one with the highest attribution coefficient of the node update request is selected The node is the selected node.

Understandably, the embodiment of the present application determines the attribution coefficient of the node update request attributable to each node by comparing the degree of association between the node update request and the associated nodes (parent node, sibling node), so as to select the child nodes that need to be added. The selected node. Obviously, after adding sub-nodes under the selected node in the embodiment of this application, the current classification tree model is updated. In order to facilitate subsequent queries, the index of the newly added child node will also be generated based on S101.

In S104, after receiving the query instruction input by the user, it is determined whether to execute the query instruction.

The query instruction can be: find all child nodes of the target node. Obviously, by querying all child nodes of the target node, a part of the classification tree model with the target node as the root node can be found.

As mentioned above, because the nodes in the classification tree model have different levels, the root node has the highest level, the leaf node has the lowest level, and the nodes of each level in the middle descend from the root node to the leaf node, so the nodes of different levels correspond to The data security requirements are different, so the requirements for user permissions are also different. Conversely, in this embodiment of the present application, users with lower user rights are not allowed to search for a local classification tree model with a higher-level target node as the root node, and are not allowed to retrieve data corresponding to a higher target node.

Therefore, in the embodiment of the present application, determining whether to execute the query instruction is essentially achieved by comparing the user authority of the user issuing the query instruction with the authority threshold of the target node that the query instruction wishes to query.

As an embodiment of the present application, as shown in FIG. 4, the foregoing S104 includes:

S1041: Extract the target node and user information from the query instruction.

In the embodiment of the present application, the query instruction includes at least two parameters, one parameter is the target node, and the other parameter is user information.

S1042: Identify the authority threshold of the target node according to the description information of the target node, and extract a user authority level from the user information.

Optionally, method one: assign a permission threshold to each level of node in advance. In this case, once the target node is parsed from the query instruction, it can be determined according to the level of the target node in the classification tree model Permission threshold.

Optionally, method two: extract the description information corresponding to the target node as target data, convert the target data into a target matrix according to a preset conversion rule, and determine the authority threshold corresponding to the target matrix through a support vector machine algorithm, The target node is parsed from the query instruction, and if the authority threshold of the target node is higher than the user authority, it is determined not to execute the query instruction. If the authority threshold of the target node is not higher than the user authority, it is determined to execute the query instruction.

The specific method for determining the authority threshold corresponding to the target matrix through the support vector machine algorithm includes: obtaining the principal component matrix corresponding to each authority threshold, calculating the average value of the principal component matrix corresponding to all authority thresholds, generating the average principal component matrix, and passing formula:

Calculate training parameters each permission threshold value, wherein, P _i represents the permission threshold training parameter i, X _i represents the main ingredient matrix permission threshold value of i, X 'represents an average primary ingredient matrix, [delta] represents X _i and X' is a covariance matrix . Repeated execution: select one permission threshold from more than one permission threshold as the selected threshold, compose the training parameters corresponding to the selected threshold into a positive training set, and compose the training parameters corresponding to the permission threshold outside the selected threshold into a negative training set , And according to the positive training set and the negative training set, the classification hyperplane of the selected threshold is calculated through the existing SVM algorithm. Until the classification hyperplane of all permission thresholds is calculated. Calculate the Euclidean distance between the target matrix and the classification hyperplane of each authority threshold as the Euclidean distance corresponding to each authority threshold, and use the authority threshold with the smallest Euclidean distance as the authority threshold corresponding to the target node.

Understandably, the recognition of the permission threshold through the support vector machine is equivalent to a set of pre-setting standards for the permission threshold. (That is, preset multiple principal component matrices corresponding to each authority threshold), instead of directly determining the authority threshold of a level node. In this way, the authority threshold can be objectively determined according to the attribute value of each attribute in the target data, highlighting some subtle differences in the target data, affecting the final authority threshold, and improving the accuracy of the authority threshold.

In addition, a classification tree model may have a large number of nodes, which can reach thousands, so it is difficult to directly set a reasonable permission threshold for each node. Therefore, through method 2, when we set a set of standards , Since each node must have a description information, we can automatically set the accurate permission threshold for each node more conveniently.

S1043: If the user authority level is not lower than the authority threshold, determine to execute the query instruction.

S1044: If the user authority level is lower than the authority threshold, it is determined not to execute the query instruction.

Understandably, the embodiments of this application can ensure the security of data query

In S105, if it is determined to execute the query instruction, extract the target node from the query instruction, and query and output the target node as the root node through the index of each node in the updated classification tree model Local classification tree model.

Optionally, query all the indexes containing the ID of the target node as the target index; delete the ID before the ID of the target node in each target index to obtain more than one partial index data; Piece of the local index data to obtain a local index data set for characterizing the local classification tree model; draw and output the local classification tree model according to the local index data set.

In S106, if it is determined that the query instruction is not executed, the query instruction is intercepted by the struct2 interceptor, and the corresponding relationship between the user information and the query instruction is stored in the historical interception record.

Understandably, in the embodiment of the present application, by obtaining the classification tree model and generating the index of each current node according to the parent node of each current node, if a node update request is received, keywords are extracted from the node update request , And based on the keywords and description information of each node, determine whether to update the classification tree model; if it is determined to update the classification tree model, select a node from the classification tree model as the selected node and add children to the selected node Node, generating the index of the child node; after receiving the query instruction input by the user, if it is determined to execute the query instruction, extract the target node from the query instruction, and pass each node in the updated classification tree model , Query and output the local classification tree model with the target node as the root node, thereby improving the efficiency of querying or updating the classification tree model.

Corresponding to the database query method described in the above embodiment, FIG. 5 shows a structural block diagram of a database query device provided in an embodiment of the present application. For ease of description, only parts related to the embodiment of the present application are shown.

Referring to Figure 5, the device includes:

The obtaining module 501 is configured to obtain a classification tree model currently used to characterize the relationship between each data set in the database, and generate an index of each node in the classification tree model;

The judgment module 502 is configured to, if a node update request for adding a node is received, extract multiple keywords from the node update request, retrieve the description information of each node in the current classification tree model, and Determine whether to update the current classification tree model based on the keywords and the description information;

The update module 503 is configured to, if it is determined to update the current classification tree model, select a node from the classification tree model as the selected node according to the node update request, and add a node to the selected node Child node, generating an index of the child node to update the classification tree model;

The query module 504 is configured to, after receiving the query instruction input by the user, if it is determined to execute the query instruction, extract the target node from the query instruction and pass the index of each node in the updated classification tree model , Query and output the local classification tree model with the target node as the root node.

Optionally, the generating the index of each node in the classification tree model includes: selecting nodes other than the root node from the current classification tree model one by one, and transferring the selected node to the root node on the path The IDs of all nodes are combined in the order from the root node to the lower nodes, as the index of the selected node, to generate the current classification tree model after traversing all nodes except the root node The index of each node.

Optionally, the determining whether to update the current classification tree model based on the keywords and the description information includes:

Count the number of occurrences of the keywords in the description information of each node with the total number of words in each description information; calculate the total number of nodes included in the current classification tree model as the first number of nodes, and Count the number of nodes containing the keyword in the corresponding description information as the number of second nodes; by formula:

Calculate the degree of association between the node update request and each node, the Y _i is the degree of association between the node update request and the node i in the classification tree model, and the n _i is the keyword in the node i The number of occurrences in the description information, the k _i is the total number of words in the description information of the node i, the h is the number of the first node, and the b is the number of the second node; if the classification tree model If there is at least one node in the update request with a degree of relevance that is not less than the preset relevance threshold, it is determined not to update the current classification tree model; if all nodes in the classification tree model are related to the update request If the association degrees are all less than the preset association degree threshold, it is determined to update the current classification tree model.

Optionally, the selecting a node from the classification tree model as the selected node includes: using a formula:

Optionally, after receiving the query instruction input by the user, the method further includes: extracting the target node and user information from the query instruction; and identifying the target node according to the description information of the target node Authority threshold, and extract the user authority level from the user information; if the user authority level is not lower than the authority threshold, it is determined to execute the query instruction; if the user authority level is lower than the authority threshold , It is determined not to execute the query instruction.

Optionally, if it is determined that the query instruction is not to be executed, the query instruction is intercepted by the struct2 interceptor, and the corresponding relationship between the user information and the query instruction is stored in the historical interception record.

Optionally, the querying and outputting the local classification tree model with the target node as the root node through the index of each node in the updated classification tree model includes: querying all IDs containing the target node As the target index; delete the ID before the ID of the target node in each target index to obtain more than one partial index data; combine each piece of the partial index data to obtain The local index data set of the classification tree model; according to the local index data set, the local classification tree model is drawn and output.

Understandably, the embodiment of the present application obtains the classification tree model and generates the index of each current node according to the parent node of each current node. If a node update request is received, keywords are extracted from the node update request, and based on Keywords and the description information of each node to determine whether to update the classification tree model; if it is determined to update the classification tree model, select a node from the classification tree model as the selected node and add child nodes for the selected node to generate The index of the child node; after receiving the query instruction input by the user, if it is determined to execute the query instruction, extract the target node from the query instruction, and pass the index of each node in the updated classification tree model, Query and output the local classification tree model with the target node as the root node, thereby improving the efficiency when querying or updating the classification tree model.

Fig. 6 is a schematic diagram of a server provided by an embodiment of the present application. As shown in FIG. 6, the server 6 of this embodiment includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and running on the processor 60, such as a database query program. When the processor 60 executes the computer program 62, the steps in the above embodiments of the query method for each database are implemented, such as steps 101 to 106 shown in FIG. 1. Alternatively, when the processor 60 executes the computer program 62, the functions of the modules/units in the foregoing device embodiments, such as the functions of the units 501 to 504 shown in FIG. 5, are realized.

Exemplarily, the computer program 62 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 61 and executed by the processor 60 to complete This application. The one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the server 6.

The server 6 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The server may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that FIG. 6 is only an example of the server 6 and does not constitute a limitation on the server 6. It may include more or less components than shown, or a combination of certain components, or different components, for example The server may also include input and output devices, network access devices, buses, and the like.

The so-called processor 60 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The storage 61 may be an internal storage unit of the server 6, such as a hard disk or a memory of the server 6. The memory 61 may also be an external storage device of the server 6, for example, a plug-in hard disk, a smart media card (SMC), or a secure digital (SD) card equipped on the server 6. Flash Card, etc. Further, the memory 61 may also include both an internal storage unit of the server 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the server. The memory 61 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned functions can be allocated to different functional units and modules as required. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated units can be hardware-based Formal realization can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only used to facilitate distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, which is not repeated here.

In the exemplary embodiment of the present application, a computer-readable storage medium is also provided, and the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor. Optionally, the computer-readable storage medium may be a non-volatile storage medium or a volatile storage medium.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

If the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, this application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims

A database query method, including:

Acquire the classification tree model currently used to characterize the relationship between each data set in the database, and generate the index of each node in the classification tree model;

If a node update request for adding a node is received, keywords are extracted from the node update request, and the description information of each node in the current classification tree model is retrieved based on the keywords and the Description information, to determine whether to update the current classification tree model;

If it is determined to update the current classification tree model, a node is selected from the classification tree model as the selected node according to the node update request, and child nodes are added to the selected node to generate the child The index of the node to update the classification tree model;

After receiving the query instruction input by the user, if it is determined to execute the query instruction, the target node is extracted from the query instruction, and the index of each node in the updated classification tree model is used to query and output The target node is a local classification tree model of the root node.
5. The database query method according to claim 1, wherein said generating the index of each node in the classification tree model comprises:

Select nodes other than the root node from the current classification tree model one by one, and combine the IDs of all nodes on the path from the selected node to the root node in the order from the root node to the lower nodes, as The index of the selected node is used to generate the index of each current node after traversing all nodes except the root node in the current classification tree model.
The database query method according to claim 1, wherein the determining whether to update the current classification tree model based on the keywords and the description information comprises:

Counting the number of occurrences of the keywords in the description information of each node and the total number of words in each description information;

Calculate the total number of nodes included in the current classification tree model as the number of first nodes, and count the number of nodes containing the keywords in the corresponding description information as the number of second nodes;

By formula:
Calculate the degree of association between the node update request and each node, the Y i is the degree of association between the node update request and the node i in the classification tree model, and the n i is the keyword in the node i The number of occurrences in the description information, the k i is the total number of words in the description information of the node i, the h is the number of the first nodes, and the b is the number of the second nodes;

If the correlation degree between at least one node in the classification tree model and the update request is not less than a preset correlation degree threshold, it is determined not to update the current classification tree model;

If the degree of association between all nodes in the classification tree model and the update request is less than a preset degree of association threshold, then it is determined to update the current classification tree model.
The database query method according to claim 3, wherein the selecting a node from the classification tree model as a selected node according to the node update request comprises:

By formula:
Calculate the attribution coefficient of the node update request to each node, where W i represents the attribution coefficient of the node update request to node i, and the Y i is the degree of association between the node update request and node i, so The F i is the degree of association between the node update request and the parent node of node i, and the B i is the average of the degree of association between the node update request and the sibling nodes of node i;

The node with the highest attribution coefficient of the node update request is selected as the selected node.
8. The database query method according to claim 1, wherein after said receiving the query instruction input by the user, the method further comprises:

Extract the target node and user information from the query instruction;

Identifying the authority threshold of the target node according to the description information of the target node, and extracting the user authority level from the user information;

If the user authority level is not lower than the authority threshold, it is determined to execute the query instruction;

If the user authority level is lower than the authority threshold, it is determined not to execute the query instruction.
The database query method according to claim 5, wherein if it is determined that the query instruction is not executed, the query instruction is intercepted by the struct2 interceptor, and the corresponding relationship between the user information and the query instruction Store in historical interception records.
5. The database query method according to claim 1, wherein the query and output of the local classification tree model with the target node as the root node through the index of each node in the updated classification tree model comprises:

Query all indexes containing the ID of the target node as the target index;

Deleting the ID before the ID of the target node in each target index to obtain more than one local index data;

Combine each piece of the local index data to obtain a local index data set for representing the local classification tree model;

According to the local index data set, draw and output the local classification tree model.
A database query device, wherein the device includes:

The acquiring module is used to acquire the classification tree model currently used to characterize the relationship between the data sets in the database, and to generate the index of each node in the classification tree model;

The judgment module is configured to, if a node update request for adding a node is received, extract multiple keywords from the node update request according to the node update request, and call each node in the current classification tree model , And based on the keywords and the description information, determine whether to update the current classification tree model;

The update module is configured to, if it is determined to update the current classification tree model, select a node from the classification tree model as the selected node, and add a child node to the selected node to generate the child The index of the node to update the classification tree model;

The query module is used to extract the target node from the query instruction if it is determined to execute the query instruction after receiving the query instruction input by the user, and pass the index of each node in the updated classification tree model, Query and output a local classification tree model with the target node as the root node.
A server includes a memory, a processor, and a computer program that is stored in the memory and can run on the processor, wherein the processor implements the following steps when the processor executes the computer program:

Acquire the classification tree model currently used to characterize the relationship between each data set in the database, and generate the index of each node in the classification tree model;

If a node update request for adding a node is received, keywords are extracted from the node update request, and the description information of each node in the current classification tree model is retrieved based on the keywords and the Description information, to determine whether to update the current classification tree model;

If it is determined to update the current classification tree model, a node is selected from the classification tree model as the selected node according to the node update request, and child nodes are added to the selected node to generate the child The index of the node to update the classification tree model;

After receiving the query instruction input by the user, if it is determined to execute the query instruction, the target node is extracted from the query instruction, and the index of each node in the updated classification tree model is used to query and output The target node is a local classification tree model of the root node.
9. The server according to claim 9, wherein the processor specifically executes the following steps when executing the index of each node in the generated classification tree model:

Select nodes other than the root node from the current classification tree model one by one, and combine the IDs of all nodes on the path from the selected node to the root node in the order from the root node to the lower nodes, as The index of the selected node is used to generate the index of each current node after traversing all nodes except the root node in the current classification tree model.
9. The server according to claim 9, wherein the processor performs the following steps when determining whether to update the current classification tree model based on the keywords and the description information:

Counting the number of occurrences of the keywords in the description information of each node and the total number of words in each description information;

Calculate the total number of nodes included in the current classification tree model as the number of first nodes, and count the number of nodes containing the keywords in the corresponding description information as the number of second nodes;

By formula:
Calculate the degree of association between the node update request and each node, the Y i is the degree of association between the node update request and the node i in the classification tree model, and the n i is the keyword in the node i The number of occurrences in the description information, the k i is the total number of words in the description information of the node i, the h is the number of the first nodes, and the b is the number of the second nodes;

If the correlation degree between at least one node in the classification tree model and the update request is not less than a preset correlation degree threshold, it is determined not to update the current classification tree model;

If the degree of association between all nodes in the classification tree model and the update request is less than a preset degree of association threshold, then it is determined to update the current classification tree model.
The server according to claim 11, wherein the processor performs the following steps when performing the selecting a node from the classification tree model as the selected node according to the node update request:

By formula:
Calculate the attribution coefficient of the node update request to each node, where W i represents the attribution coefficient of the node update request to node i, and the Y i is the degree of association between the node update request and node i, so The F i is the degree of association between the node update request and the parent node of node i, and the B i is the average of the degree of association between the node update request and the sibling nodes of node i;

The node with the highest attribution coefficient of the node update request is selected as the selected node.
The server according to claim 9, wherein the processor, after executing the index of each node in the classification tree model after the update, queries and outputs a local classification tree model with the target node as the root node When, perform the following steps:

Query all indexes containing the ID of the target node as the target index;

Deleting the ID before the ID of the target node in each target index to obtain more than one local index data;

Combine each piece of the local index data to obtain a local index data set for representing the local classification tree model;

According to the local index data set, draw and output the local classification tree model.
9. The server of claim 9, wherein the processor further executes the following steps after executing the query instruction input by the user:

Extract the target node and user information from the query instruction;

Identifying the authority threshold of the target node according to the description information of the target node, and extracting the user authority level from the user information;

If the user authority level is not lower than the authority threshold, it is determined to execute the query instruction;

If the user authority level is lower than the authority threshold, it is determined not to execute the query instruction.
The server of claim 14, wherein the processor further performs the following steps:

If it is determined that the query instruction is not executed, the query instruction is intercepted by the struct2 interceptor, and the corresponding relationship between the user information and the query instruction is stored in the historical interception record.
A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the following steps:

Acquire the classification tree model currently used to characterize the relationship between each data set in the database, and generate the index of each node in the classification tree model;

If a node update request for adding a node is received, keywords are extracted from the node update request, and the description information of each node in the current classification tree model is retrieved based on the keywords and the Description information, to determine whether to update the current classification tree model;

If it is determined to update the current classification tree model, a node is selected from the classification tree model as the selected node according to the node update request, and child nodes are added to the selected node to generate the child The index of the node to update the classification tree model;

After receiving the query instruction input by the user, if it is determined to execute the query instruction, the target node is extracted from the query instruction, and the index of each node in the updated classification tree model is used to query and output The target node is a local classification tree model of the root node.
16. The computer-readable storage medium according to claim 16, wherein, when the index of each node in the classification tree model is generated, the computer program is executed by a processor to specifically implement the following steps:

Select nodes other than the root node from the current classification tree model one by one, and combine the IDs of all nodes on the path from the selected node to the root node in the order from the root node to the lower nodes, as The index of the selected node is used to generate the index of each current node after traversing all nodes except the root node in the current classification tree model.
The computer-readable storage medium of claim 16, wherein the computer program is executed by a processor when determining whether to update the current classification tree model based on the keywords and the description information To implement the following steps:

Counting the number of occurrences of the keywords in the description information of each node and the total number of words in each description information;

Calculate the total number of nodes included in the current classification tree model as the number of first nodes, and count the number of nodes containing the keywords in the corresponding description information as the number of second nodes;

By formula:
Calculate the degree of association between the node update request and each node, the Y i is the degree of association between the node update request and the node i in the classification tree model, and the n i is the keyword in the node i The number of occurrences in the description information, the k i is the total number of words in the description information of the node i, the h is the number of the first nodes, and the b is the number of the second nodes;

If the correlation degree between at least one node in the classification tree model and the update request is not less than a preset correlation degree threshold, it is determined not to update the current classification tree model;

If the degree of association between all nodes in the classification tree model and the update request is less than a preset degree of association threshold, then it is determined to update the current classification tree model.
The computer-readable storage medium according to claim 18, wherein, when a node is selected from the classification tree model as a selected node according to the node update request, the computer program is executed by a processor to specifically implement The following steps:

By formula:
Calculate the attribution coefficient of the node update request to each node, where W i represents the attribution coefficient of the node update request to node i, and the Y i is the degree of association between the node update request and node i, so The F i is the degree of association between the node update request and the parent node of node i, and the B i is the average of the degree of association between the node update request and the sibling nodes of node i;

The node with the highest attribution coefficient of the node update request is selected as the selected node.
16. The computer-readable storage medium according to claim 16, wherein when the updated index of each node in the classification tree model is used to query and output the local classification tree model with the target node as the root node, The computer program is executed by the processor to specifically implement the following steps:

Query all indexes containing the ID of the target node as the target index;

Deleting the ID before the ID of the target node in each target index to obtain more than one local index data;

Combine each piece of the local index data to obtain a local index data set for representing the local classification tree model;

According to the local index data set, draw and output the local classification tree model.