Dynamic index generation method and system driven by business form fields

Abstract

The invention belongs to the technical field of index generation, and particularly relates to a dynamic index generation method and system driven by business form fields, and the method comprises the steps: constructing a business form query configuration tree, and generating a minimum effective field adjustment tree based on real-time configuration change in combination with a Bayesian algorithm and an optimization algorithm; fields needing to be adjusted and associated query operation of the fields are accurately positioned, and an index optimization scheme is dynamically generated with the minimum change constraint and the maximum query integrating degree as targets; the index adjustment is automatically executed through the index strategy engine, and the closed-loop feedback mechanism is utilized for continuous optimization, so that the complexity of index change and the resource overhead are remarkably reduced, the response rate and the self-adaptive capability of the system to the change of the query demand of the service form are effectively improved, and high-performance and high-availability dynamic index management is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of index generation, and particularly relates to a business form field driven dynamic index generation method and system. BACKGROUND

[0002] In a traditional data reporting and management system, the business form query function is mostly realized by hard coding, and the developer needs to create a database index for a specific field in advance according to fixed business requirements, and write a backend query logic. However, with the development of business, users frequently add or adjust the queryable fields, the query condition types, and the complex logical combinations between conditions, so that the developer repeatedly modifies the code, adjusts the index, and redeploys the system, resulting in slow response of the system to business changes, high development and maintenance costs, in addition, in the prior art, the static database index strategy cannot adapt to the dynamic changes of the business form field configuration; when the field query effectiveness, the hierarchical relationship between fields, or the query logic combination changes, the system cannot automatically perceive and adjust the index structure, resulting in problems such as a large number of redundant indexes, invalid joint indexes, and query mode mismatch; this not only causes waste of storage space and decline of write performance, but also makes the query optimizer unable to effectively use the index, and is forced to use full table scanning, ultimately causing degradation of database query efficiency and reduction of system response speed. SUMMARY

[0003] In view of the deficiencies of the prior art, the application provides a business form field driven dynamic index generation method and system, which constructs a business form query configuration tree, and generates a minimum effective field adjustment tree based on real-time configuration changes combined with a Bayesian algorithm and an optimization algorithm to accurately locate the fields and associated query operations that need to be adjusted, and dynamically generates an index optimization scheme with the minimum change constraint and the maximum query fit degree as the target; the index strategy engine automatically executes the index adjustment, and continuously optimizes by using a closed-loop feedback mechanism, which significantly reduces the complexity and resource overhead of index changes, effectively improves the response speed and adaptive ability of the system to changes in business form query requirements, and realizes high-performance and high-availability dynamic index management.

[0004] To achieve the above object, the application provides the following technical scheme:

[0005] The business form field driven dynamic index generation method comprises:

[0006] Obtaining a business form query configuration tree based on the query configuration items of the form fields in the business form configuration subsystem combined with a graph algorithm and a tree database;

[0007] Obtaining a minimum effective field adjustment tree with the minimum change constraint and the maximum fit degree as the target by acquiring real-time configuration change operations of the query configuration items of the form fields in the business form configuration subsystem combined with an optimization traversal strategy.

[0008] Based on the minimum effective field adjustment tree, a database index creation or adjustment statement is obtained through an index strategy engine;

[0009] The database index creation or adjustment statement is automatically executed to adjust the business form query configuration tree, and in response to real-time query requirements of newly added business forms, when any query requirement is abnormal, the real-time query requirement of the business form and the corresponding updated query index are located;

[0010] The real-time query requirement of the business form and the corresponding updated query index are fed back to the optimal traversal strategy for real-time updating until all query requirements are met.

[0011] Specifically, the minimum effective field adjustment tree is obtained by traversing based on the change operation complexity of the query fields and the corresponding associated query fields to be changed, the query permission of the query operator corresponding to the single or composite query field, the query granularity index, and the parent-child hierarchical relationship of the business form query configuration tree, combining the Bayesian algorithm with the optimal traversal algorithm, and presetting the target function constructed by the minimum change operation range and the maximum fitting degree. Wherein, the fitting degree is represented by the query exception probability obtained by the Bayesian algorithm during the query process of the changed business form query configuration tree for the business form, and is used to represent the effectiveness of the changed business form query configuration tree;

[0012] The acquisition process of the minimum change operation range is as follows: based on the parent-child hierarchical relationship of the business form query configuration tree, taking the field to be changed as the initial parent field, traversing each associated child field corresponding to the initial parent field, and using the Bayesian algorithm, taking the query permission, query granularity index and dependency degree corresponding to the parent field and associated child field as conditions, calculating the conditional probability of each associated child field needing to adjust with the corresponding parent field;

[0013] The associated child field with a conditional probability lower than a preset threshold is excluded, and the set of remaining fields is taken as the minimum change operation range of the current change operation; the query granularity index is constructed according to the selection degree of the query result of the single or composite query operator corresponding to each single or composite query field, and is used to optimize the precision of the query index.

[0014] Specifically, the acquisition process of the business form query configuration tree includes:

[0015] obtain a form unit data field table based on the business form text information and a text analysis algorithm; the form unit data field table at least includes a form ID, a field and a corresponding ID set, a field association pointer, a field association logical relationship operator set, a field query combination dependency relationship, a field query validity state label, a field data type and constraint attribute, a query operator mapping set, a historical query frequency, and a form business attribution label;

[0016] construct a root node with the form ID and the form business attribution label, a field driven node layer with the field and the corresponding ID set, the field data type and constraint attribute, and the field association pointer, a query configuration node layer with the field association logical relationship operator set, and a target index layer with the business form information data;

[0017] construct a first cross-layer driven connection based on the root node, the connection relationship between each field node in the field driven node layer and the field nodes, the connection relationship between the query operation nodes in the query configuration node layer and the query operation nodes, the connection relationship between the target index character nodes corresponding to the target index layer and the target index character nodes, and the form ID and the field ID, construct a second cross-layer driven combination connection based on the field association logical relationship operator set and the field query combination dependency relationship, construct a third cross-layer driven index connection based on the query operator mapping set and the corresponding field and the historical query frequency corresponding to the query operator, and then construct a business form query configuration tree through a graph neural network and a tree database.

[0018] Specifically, the acquisition process of the first cross-layer driven connection includes:

[0019] construct a one-to-many first connection sequence between the root node and the field driven node layer based on the form ID and the field and the corresponding ID set;

[0020] obtain business domain classification features, field association business index type features, period association matching features, and scene constraint matching features through a text feature extraction algorithm based on the business form attribution label and the field attribute features;

[0021] obtain a query driven association degree between the business form and each field through an association analysis algorithm based on the business domain classification features, the field association business index type features, the period association matching features, and the scene constraint matching features;

[0022] obtain a corresponding binary label based on the field query validity state label, the binary label including 1 and 0, 1 corresponding to a field being valid and 0 corresponding to a field being disabled;

[0023] embed the binary label and the query driven association degree into the first connection sequence to construct the first cross-layer driven connection.

[0024] Specifically, the acquisition process of the field-driven node layer includes:

[0025] Based on the binary label, field and corresponding ID set, field data type and constraint attribute, and field association pointer in the first cross-layer driving connection, valid fields are screened through an availability checking rule to obtain a valid field basic list;

[0026] Based on the valid field basic list, a parent-child field hierarchical relationship feature in each field association pointer is extracted to construct a vertical query dependency chain, and a horizontal causal feature of each field corresponding to a same-level associated query field is extracted to construct a horizontal query causal chain.

[0027] Based on the vertical query dependency chain and the horizontal query causal chain corresponding to all fields, a field-driven node layer is constructed by using a graph neural network.

[0028] Specifically, the construction process of the horizontal query causal chain includes: based on the fixity of logical operators of any two same-level fields in a combined query, the intersection proportion of two field query result sets, the mutual constraint degree of field query conditions, and the business semantic association score, an association degree between any two same-level fields is obtained as a horizontal connection between the two fields; based on the horizontal connection between the two same-level fields and the field nodes constructed by the two fields, a horizontal query causal chain is obtained.

[0029] The construction process of the vertical query dependency chain is: the query enablement control degree of a parent field to its child field, the constraint degree of a child field value range to a parent field, the reference depth of a child field query logic to a parent field, the passive adjustment rate of a child field when a parent field is changed, and the data bloodline association degree between parent and child fields are combined with an infectious disease model to construct the query dependency degree between any parent and child fields as the vertical connection between the parent and child nodes constructed by the corresponding parent and child fields; based on all parent and child nodes and the corresponding vertical connection, a vertical query dependency chain is constructed.

[0030] Specifically, the construction process of the second cross-layer driving combined connection includes:

[0031] Based on the set of field association logical relationship operators, an entity-relation extraction algorithm is combined to obtain an operator-combination-field node information set.

[0032] Based on the operator-combination-field node information set, the association field ID of the field association logical relationship operator, the combination association field ID list of the field query combination dependency relationship, and the ID of the valid field node in the field-driven node layer are matched through an association matching algorithm to screen out the valid field nodes corresponding to the operators and the combination dependency, and an operator-combination-valid field node matching set is obtained.

[0033] Specifically, the construction process of the second cross-layer driven combination connection further includes:

[0034] Based on the operator-combination-effective field node matching set, through the fitness algorithm, the fitness of each operator-combination and the corresponding effective field node is calculated with the operator type and the fitness of the data type of the effective field node, the consistency of the combination logic structure and the longitudinal query dependency chain and the transverse query causal chain as the evaluation target, and the operator-combination-effective field node combination with the fitness greater than or equal to the preset fitness threshold is retained to obtain the qualified operator-combination-field node set;

[0035] Based on the qualified operator-combination-field node set, the corresponding effective field node is connected through the associated field association logic relationship operator according to the combination logic structure of the field query combination dependency relationship to obtain a combination connection containing the effective field node, the associated operator and the combination logic, and the corresponding longitudinal dependency identifier or transverse causal identifier of each combination connection is labeled to obtain the second cross-layer driven combination connection.

[0036] Specifically, the construction process of the third cross-layer driven index connection includes:

[0037] Based on the target index layer, the query operator mapping set and the field driven node layer, the index type, the index associated field identifier, the operator type, the operator and field mapping relationship, the effective field node identifier and the historical query frequency are extracted to obtain the index-operator-field hierarchical information set;

[0038] Based on the index-operator-field hierarchical information set, the operator associated field identifier and the effective field node identifier are matched through the field identifier matching algorithm, and the operator type and the index adaptation operation type are matched through the matching algorithm to obtain the index-operator-effective field matching set;

[0039] Based on the index-operator-effective field matching set, the fitness of the operator and the field data type, the fitness of the operator and the index type and the priority score based on the historical query frequency are calculated through the multi-dimensional fitness evaluation algorithm, and the combination with the comprehensive fitness reaching the preset threshold is retained to obtain the qualified index-operator-field set;

[0040] Based on the qualified index-operator-field set, the associated connection between the effective field node, the query operator and the target index node is established through the connection unit construction algorithm according to the operator and field mapping relationship to obtain the basic index connection unit set labeled with the historical query frequency weight and the adaptation state identifier;

[0041] Based on the basic index connection unit set, the hierarchical compatibility verification algorithm is used to verify the structural compatibility of the connection unit with the field driven node layer and the target index layer, the connection unit with logical conflicts or destroying the hierarchical structure is removed, and a compatible index connection unit set is obtained.

[0042] Based on the compatible index connection unit set, the sorting is performed according to the product of the historical query frequency weight and the adaptive degree score, the query configuration node layer and the target index layer are bound according to the mapping relationship between the operators and the fields based on the sorted compatible index connection unit set, and the third cross-layer driven index connection is constructed.

[0043] The business form field driven dynamic index generation system comprises a configuration tree module, an analysis module, an index construction module and an abnormality adjustment module.

[0044] The configuration tree module obtains a business form query configuration tree based on the query configuration items of the form fields in the business form configuration subsystem and a graph algorithm and a tree database.

[0045] The analysis module is used to obtain the real-time configuration change operation of the query configuration items of the form fields in the business form configuration subsystem and an optimal traversal strategy, and obtain a minimum effective field adjustment tree with minimum change constraint and maximum fitness as the target.

[0046] The index construction module obtains a database index creation or adjustment statement through an index strategy engine based on the minimum effective field adjustment tree.

[0047] The abnormality adjustment module is used to automatically execute the database index creation or adjustment statement to adjust the business form query configuration tree, and respond to the real-time query demand of the newly added business form, and when any query demand is abnormal, the business form real-time query demand and the corresponding updated query index are located; the business form real-time query demand and the corresponding updated query index are fed back to the optimal traversal strategy for real-time updating until all query demands are met.

[0048] Compared with the prior art, the beneficial effects of the present application are:

[0049] The present application aims at the deficiencies of the prior art, by constructing the minimum effective field adjustment tree, accurately identifying the query fields and associated adjustment fields that need to be changed most, combining the adjustment depth, query permission and other key elements, only adjusting the necessary fields and query operations, greatly reducing the redundant changes, significantly reducing the difficulty of changing the business form query configuration; based on the Bayesian algorithm and optimization algorithm, while meeting the minimum change constraint, the maximum matching degree is realized, ensuring the accuracy and effectiveness of the adjustment; through the index strategy engine, the index creation or adjustment statement is automatically generated and executed, combined with the real-time feedback mechanism to quickly respond to the new query demand, significantly improving the response rate of the system to the business form change; in addition, by positioning the abnormal query and dynamically optimizing the index, a closed-loop adjustment mechanism is formed, continuously adapting to the dynamic changes of the business form, ensuring the stability and accuracy of the query, and enhancing the adaptability of the system to complex business scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A flow chart of the business form field-driven dynamic index generation method of the present application;

[0051] Figure 2 A module diagram of the business form field-driven dynamic index generation system of the present application. DETAILED DESCRIPTION

[0052] Example 1

[0053] Please refer to Figure 1 An embodiment provided by the present application: a business form field-driven dynamic index generation method, the steps include:

[0054] S1, based on the query configuration items of the form fields in the business form configuration subsystem, combining the graph algorithm and the tree database, obtaining the business form query configuration tree;

[0055] S2, obtaining the real-time configuration change operation of the query configuration items of the form fields in the business form configuration subsystem, combining the optimization traversal strategy, taking the minimum change constraint and the maximum matching degree as the target, obtaining the minimum effective field adjustment tree;

[0056] S3, based on the minimum effective field adjustment tree, through the index strategy engine, obtaining the database index creation or adjustment statement; it needs to be further explained that the index strategy engine in the present embodiment is constructed and trained by the minimum effective field adjustment tree with the maximum corresponding matching degree in the historical change process combining the code automatic generation algorithm, which is used to parse the minimum effective field adjustment tree, and generate the database index creation or adjustment statement according to the result.

[0057] S4, automatically executing the database index creation or adjustment statement adjusts the business form query configuration tree, and in response to the real-time query demand of the newly added business form, when any query demand is abnormal, the real-time query demand of the business form and the corresponding updated query index are located;

[0058] S5, feeding the real-time query demand of the business form and the corresponding updated query index to the optimal traversal strategy for real-time updating until all query demands are met.

[0059] It should be further explained that the acquisition process of the business form query configuration tree in the embodiment includes:

[0060] Based on the business form text information and the text parsing algorithm, a form unit data field table is obtained; the form unit data field table at least includes form ID, field and corresponding ID set, field association pointer, field association logical relationship operator set and field query combination dependency relationship, field query validity state label, field data type and constraint attribute, query operator mapping set, historical query frequency, form business attribution label; the text parsing algorithm is preferably an entity-relation extraction model constructed by a Bert model;

[0061] The root node is constructed with the form ID and the form business attribution label, the field driven node layer is constructed with the field and corresponding ID set, the field data type and constraint attribute, and the field association pointer, the query configuration node layer is constructed with the field association logical relationship operator set, and the target index layer is constructed with the business form information data;

[0062] Based on the connection relationship between each field node in the root node and the field driven node layer, the connection relationship between the query operation nodes in the query configuration node layer, the connection relationship between the target index character nodes corresponding to the target index layer, and the form ID and the field ID, a first cross-layer driven connection is constructed, a second cross-layer driven combination connection is constructed based on the field association logical relationship operator set and the field query combination dependency relationship, a third cross-layer driven index connection is constructed based on the query operator mapping set and the corresponding field and query operator corresponding historical query frequency, and a business form query configuration tree is constructed through a graph neural network and a tree database;

[0063] It should be further explained that the acquisition process of the first cross-layer driven connection in the embodiment further includes:

[0064] Based on the form ID, the field and the corresponding ID set, a one-to-many first connection sequence between the root node and the field driven node layer is constructed; it should be further explained that the one-to-many first connection sequence in the embodiment is constructed according to the form ID and each field ID in the field driven node layer;

[0065] Based on the business form attribution label and the field attribute characteristics, through a text feature extraction algorithm, business domain classification characteristics, field associated business index type characteristics, period associated matching characteristics and scene constraint matching characteristics are obtained; the business domain classification characteristics are used to represent the consistency of the business form and the field belonging to the business domain, and provide a field dimension basis for the associated matching of the business form and the field; the field associated business index type characteristics are used to represent the associated attributes of the field and the core business index of the business form, and to clearly indicate the core support degree of the field to the business form query demand; the period associated matching characteristics are used to represent the adaptability of the field query period and the business period of the business form, and to ensure that the field query logic meets the time dimension business demand of the business form; the scene constraint matching characteristics are used to represent the consistency of the field query scene and the target query scene of the business form, and to ensure that the field query operation meets the scene limitation requirements of the business form.

[0066] Based on the business domain classification characteristics, the field associated business index type characteristics, the period associated matching characteristics and the scene constraint matching characteristics, through an association analysis algorithm, the query driven association degree between the business form and each field is obtained; it needs to be further explained that the query driven association degree between the business form and each field in the embodiment is a core index for measuring the matching degree of the field and the business demand of the form, and its core role is to accurately select the fields highly consistent with the form query demand, and to provide a quantitative basis for subsequent effective field extraction, hierarchical structure construction and cross-layer connection establishment: the fields with high association degree are preferentially included in the effective field set as the core or key auxiliary nodes supporting the form query, to ensure that the form query configuration focuses on the business core demand; the fields with low association degree are identified as unnecessary fields, to reduce the interference of redundant information on the query logic, to reduce the configuration complexity and maintenance cost, and to have efficient query response ability, to improve the accuracy and adaptability of the form query from the source.

[0067] Based on the field query effectiveness state label, a corresponding binary label is obtained, the binary label includes 1 and 0, 1 corresponds to field effective, and 0 corresponds to field disabled;

[0068] The binary label and the query driven association degree are embedded in the first connection sequence, and a first cross-layer driven connection is constructed;

[0069] It needs to be further explained that the obtaining process of the field driven node layer in the embodiment further includes:

[0070] Based on the binary label, the field and the corresponding ID set, the field data type and the constraint attribute, and the field association pointer in the first cross-layer driving connection, valid fields are screened through an availability checking rule to obtain a valid field basic list; wherein the availability checking rule at least includes that the field query effectiveness state is enabled, the data type is adapted to the business query scene, and the constraint attribute meets the query basic requirement;

[0071] It needs to be further explained that the process of screening valid fields through the availability checking rule to obtain the valid field basic list in the embodiment includes:

[0072] All field IDs in the first cross-layer driving connection are traversed, and the binary label, the data type and the constraint attribute corresponding to each field are extracted one by one;

[0073] The binary label of each field is read, and only when the label value is in the enabled state, the next checking step is entered;

[0074] When entering the next checking step, the field data type is matched with a predefined queryable data type set, when the field data type meets the matching requirement, it is checked whether the field constraint attribute contains a valid value range definition, whether it contains a non-empty constraint, whether it contains a default value definition, and each constraint attribute meets the basic requirement of the business query; the queryable data type at least includes numerical type, date and time type, character type and enumeration type, excluding binary large object, super long text and other data types not suitable for query scene;

[0075] For the fields that pass all three checks, the field ID, the data type and the constraint attribute are added to the valid field basic list; for the fields that do not pass any check, the specific reason and the check timestamp are recorded;

[0076] When all field checks are completed, the valid field basic list containing the complete attribute information of all fields that pass the check is output.

[0077] Based on the valid field basic list, the parent-child field hierarchical relationship features in each field association pointer are extracted to construct a vertical query dependency chain, and the horizontal causal features of each field corresponding to the same level associated query field are extracted to construct a horizontal query causal chain;

[0078] It needs to be further explained that the construction process of the horizontal query causal chain in the embodiment includes: based on the fixity of the logical operator of any two same level fields in the combined query, the intersection proportion of the two field query result sets, the mutual constraint degree of the field query conditions and the business semantic association score combined with the association analysis algorithm, the association degree between any two same level fields is obtained as the horizontal connection between the corresponding two fields;

[0079] Based on the transverse connection between two fields of the same level and the field nodes constructed by the two fields, a transverse query causal chain is obtained;

[0080] It should be further explained that the acquisition process of the transverse query causal chain in the embodiment includes:

[0081] A1: Obtain the historical query log data corresponding to the target business scenario, first define the same level field range, the same level field is a field belonging to the same business form and not having a parent-child hierarchical dependency relationship; all query records containing any two same level fields are screened out from the historical query log data, and the query records are taken as a target analysis record set; logical operator analysis is performed on the target analysis record set, the number of query records in which two same level fields are connected by the same logical operator is counted, and the total number of query records in which two same level fields appear simultaneously in the target analysis record set is counted; the ratio of the number of query records in which two same level fields are connected by the same logical operator to the total number of query records in which two same level fields appear simultaneously is calculated, and the ratio is determined as the logical operator fixity score of the two same level fields.

[0082] A2: Obtain the query execution result data corresponding to the target analysis record set in A1, the query execution result data being the query result set corresponding to the two same level fields respectively after each query record in the target analysis record set is executed; the intersection record number and the union record number between the two field query result sets corresponding to each query record are counted; the ratio of the intersection record number to the union record number of the two field query result sets in each query record is calculated, and the average of the ratios corresponding to all query records is calculated, and the calculated average is determined as the intersection proportion of the two same level field query result sets.

[0083] A3: Collect the query condition value data of the two same level fields in the historical query process, the query condition value data being the specific value or value range set for the two fields in each query; based on the query condition constraint relationship, a Bayesian model is constructed, the query condition value data of one field is taken as the input variable of the model, and the query condition value data of the other field is taken as the output variable of the model; the value range restriction probability of the output variable when the input variable takes a specific value is calculated through the Bayesian model, the value range restriction probability representing the degree of restriction of the input variable value on the value range of the output variable; the value range restriction probabilities corresponding to all specific values of the input variable are weighted and averaged, and the obtained average value is determined as the mutual constraint degree of the query conditions of the two same level fields.

[0084] A4: Obtain the business form text information corresponding to the target business scenario, the business form text information including the business form name, the function description text of each field, and the business association rule document between fields; adopt knowledge graph construction technology to extract entities and entity relationships from the business form text information, the entity being a field in the business form and the entity relationship being the business association logic between fields; based on the extracted entities and entity relationships, construct a business knowledge graph of the target business scenario; based on the business knowledge graph, adopt a semantic analysis algorithm to analyze the association path length and association relationship type importance of any two fields of the same level in the knowledge graph; calculate the semantic matching degree of the two fields according to the association path length and association relationship type importance, and determine the semantic matching degree as the business semantic association score of the two fields of the same level.

[0085] A5: Determine the weight allocation rule of the logical operator fixity score obtained by A1, the intersection ratio obtained by A2, the mutual constraint degree obtained by A3, and the business semantic association score obtained by A4, the weight allocation rule being based on the query requirement of the target business scenario and being set by a business domain expert, and the sum of all index weights being 1; adopt a weighted average algorithm to multiply each index score by its corresponding weight, and then perform summation operation on all weighted scores; determine the summation result as the comprehensive association degree between any two fields of the same level.

[0086] A6: Set a comprehensive association degree threshold, the threshold being determined based on the field horizontal association effectiveness standard in the target business scenario; adopt a graph construction algorithm to take any two fields of the same level as nodes in the graph, judge whether the comprehensive association degrees of the two nodes reach the preset association degree threshold, and if so, establish a horizontal connection between the two nodes; obtain the conditional probability of each associated subfield adjusted with the corresponding parent field, the parent field being a field with business dominance in the horizontal association, and the conditional probability being obtained based on historical field adjustment records; calculate the product of the conditional probability and the comprehensive association degree score of the corresponding horizontal connection; based on the HSV color space mapping rule, map the product result to the corresponding color parameter, the color parameter including hue, saturation, and lightness, the product result being greater, the saturation being higher, and the hue being more consistent with the key identification color of the target business scenario, and the lightness being kept in a preset moderate range; embed the mapped color parameter into the corresponding horizontal connection to form a horizontal query causal chain with visual color identification.

[0087] The construction process of the longitudinal query dependency chain is: extracting the query enablement control degree of the parent field to its child field in each field association pointer, the constraint degree of the child field value range to the parent field, the reference depth of the child field query logic to the parent field, the passive adjustment rate of the child field when the parent field is changed, and the data blood relationship degree of the parent and child fields, combining the infectious disease model, to construct the query dependency degree between any parent and child fields, as the longitudinal connection between the parent and child nodes constructed for the corresponding parent and child fields; based on all parent and child nodes and the corresponding longitudinal connection, the longitudinal query dependency chain is constructed; the query enablement control degree of the parent field to its child field is that whether the enablement of the child field query permission is premised on the enablement of the parent field query permission, and the proportion of the number of times that the premise condition is established to the total number of child field queries is obtained; the constraint degree of the child field value range to the parent field is that the proportion of the number of times that the effective value set of the child field is limited by the current value of the parent field to the total number of child field queries; the reference depth of the child field query logic to the parent field is that the number of levels that the query condition of the child field directly or indirectly contains the query result of the parent field; the passive adjustment rate of the child field when the parent field is changed is that the proportion of the number of times that the child field must be adjusted synchronously due to business logic association after the parent field occurs query configuration change to the total number of parent field changes; the data blood relationship degree of the parent and child fields is that whether the data generation of the child field depends on the data calculation or screening result of the parent field, and the degree that the dependency relationship is explicitly defined in the business rules.

[0088] It needs to be further explained that the more detailed implementation process of the longitudinal query dependency chain in the embodiment includes:

[0089] Step 1: First, define the parent and child field range of the business form in the target business scenario, the parent and child fields are determined based on the hierarchical logical relationship and data dependency relationship of the business form, that is, the parent field is the field that plays a leading control role in the query logic, and the child field is the field that needs to be based on the existence of the parent field as a prerequisite; five dependency characteristics between parent and child fields are extracted, and corresponding relationships are established between them and the core parameters of the infectious disease model:

[0090] The query enablement control degree of the parent field to its child field corresponds to the basic transmission source strength of the infectious disease model, the query enablement control degree is the control degree of the enablement state of the parent field query permission to the enablement of the child field query permission, and the basic transmission source strength is used to represent the basic ability of the parent field to produce dependency impact on its child field by means of the control degree;

[0091] The constraint degree of the child field value range to the parent field corresponds to the susceptibility of the infectious disease model, the constraint degree is the degree that the effective value range of the child field is limited by the current value of the parent field, and the susceptibility is used to represent the easy acceptance degree of the child field to accept the dependency impact based on the constraint degree;

[0092] The reference depth of the child field to the parent field corresponds to the propagation path length of the infectious disease model, the reference depth being the number of levels in the query condition of the child field that directly or indirectly reference the query result of the parent field, and the propagation path length representing the level distance of the dependent influence of the parent field passing through the reference levels to the child field;

[0093] The passive adjustment rate of the child field when the parent field changes corresponds to the historical propagation frequency of the infectious disease model, the passive adjustment rate being the frequency at which the child field must be adjusted synchronously due to business logic association after the query configuration of the parent field is changed, and the historical propagation frequency representing the actual occurrence frequency of the dependent influence of the parent field passing to the child field based on the adjustment frequency in the historical process;

[0094] The data bloodline association degree between the parent and child fields corresponds to the propagation affinity in the infectious disease model, wherein the data bloodline association degree includes two core dimensions, the first dimension being the dependence degree of the data generation process of the child field on the calculation result or the screening result of the parent field, specifically whether the calculation result (such as a value derived based on the parent field data) or the screening result (such as data selected from the data set range defined by the parent field) of the parent field is used as a basis when the child field generates data, the more direct and irreplaceable the dependence relationship is, the higher the data bloodline association degree in this dimension is; the second dimension being the degree of clear definition of the dependence relationship between the parent and child fields in the business rules, specifically whether the dependence relationship is clearly and explicitly defined between the parent and child fields through formal documents, system configurations or code logic, the more standardized and clear the definition is, the higher the data bloodline association degree in this dimension is; and the propagation affinity, as a core parameter of the infectious disease model, represents the internal association strength of the support of the dependent influence transmission between the parent and child fields based on the data bloodline association degree, that is, the higher the data bloodline association degree is, the closer the internal association between the parent and child fields is, and the higher the value of the propagation affinity is, and the dependent influence (such as query configuration change, permission adjustment, etc.) of the parent field is more easily transmitted to the child field.

[0095] Step 2: Based on the basic propagation source intensity and the susceptibility determined in step 1, an initial propagation probability is obtained by using a product calculation method; the logic basis of the product calculation is that the basic propagation source intensity represents the ability of the parent field to produce dependent influence, and the susceptibility represents the degree of the child field to accept the influence, and the product thereof can quantitatively reflect the possibility of the preliminary transmission of the dependent influence of the parent field to the child field, forming the initial propagation probability.

[0096] Step 3: Based on the initial propagation probability obtained in step 2 and the propagation path length determined in step 1, the initial propagation probability is corrected by a path attenuation correction algorithm to obtain a path corrected probability; wherein the execution process of the path attenuation correction algorithm includes:

[0097] First, a preset hierarchical attenuation association rule is constructed, which is based on the historical parent-child field dependency transmission data of the target business scenario, analyzes the linear relationship between the field reference hierarchy number and the dependency degree of the child field on the parent field, and is constructed in combination with a linear function, a variable of the linear function being the reference hierarchy number and a function result being a decay ratio of the dependency influence transmission under the corresponding hierarchy number;

[0098] Based on the hierarchical attenuation association rule and the propagation path length, a path attenuation coefficient is calculated, the path attenuation coefficient being a dependency influence decay ratio corresponding to the propagation path length;

[0099] The path attenuation coefficient and the initial propagation probability are multiplied to obtain a path correction probability, and the process is used to quantitatively decay and correct the dependency propagation probability through the reference hierarchy distance, so as to reflect the business logic that the deeper the hierarchy, the weaker the dependency influence.

[0100] Step 4: Based on the obtained path correction probability and the determined historical propagation frequency, the path correction probability is enhanced to obtain an enhanced propagation probability; the specific process of the enhancement is as follows:

[0101] A preset frequency enhancement rule is obtained based on the historical parent-child field adjustment case statistical analysis of the target business scenario, that is, the higher the historical propagation frequency, the more actual frequencies of the parent field dependency influence transmission to the child field in the past, and the larger the corresponding enhancement coefficient;

[0102] Based on the above frequency enhancement rule, the enhancement coefficient corresponding to the historical propagation frequency is determined;

[0103] The path correction probability and the enhancement coefficient are multiplied to obtain the enhanced propagation probability, so as to reflect the enhancement effect of the past transmission frequency on the current dependency influence transmission possibility.

[0104] Step 5: Based on the enhanced propagation probability obtained in step 4 and the propagation affinity determined in step 1, the enhanced propagation probability is weighted by a weighting calculation algorithm to finally obtain the dependent propagation probability between the parent-child fields; wherein the execution logic of the weighting calculation algorithm is: the propagation affinity is taken as a weight coefficient, which is a parameter quantified based on the parent-child field data blood relationship degree (the degree of dependence of the child field data on the calculation result or screening result of the parent field data and the degree of explicit definition of this dependence relationship in the business rules); the higher the parent-child field data blood relationship degree, the larger the value of the weight coefficient, and the lower the degree, the smaller the value of the weight coefficient; the enhanced propagation probability (the preliminary probability value of the dependent influence transmission from the parent field to the child field after the path attenuation correction and the historical propagation frequency enhancement) is multiplied by the weight coefficient, and the possibility of dependent influence transmission is adjusted by numerical operation, especially when the parent-child field data blood relationship degree is high, the large weight coefficient will amplify the enhanced propagation probability, so that the finally obtained dependent propagation probability is increased, so that the close internal blood relationship can reflect the strengthening effect on the possibility of dependent influence transmission in this technical way; when the parent-child field data blood relationship degree is low, the small weight coefficient will reduce the enhanced propagation probability, so that the finally obtained dependent propagation probability is reduced, so that the loose internal blood relationship can reflect the weakening effect on the possibility of dependent influence transmission in this technical way; at the same time, the value of the weight coefficient is verified in combination with the business rules of the target business scene (such as the business logic specification of the field data generation, the business constraint requirement of the parent-child field association) to confirm the matching degree with the actual business dependence relationship, and to ensure that the weighted calculation result can accurately reflect the real possibility of dependent influence transmission between the parent-child fields in the business scene.

[0105] Step 6: Based on the dependent propagation probability value between the parent-child fields obtained in step 5, it is divided into four dependent levels by a preset probability threshold interval; wherein the determination basis of the preset probability threshold interval is: combined with the query stability demand of the target business scene, the historical parent-child field dependence fault cases are counted, the occurrence probability of the child field query exception under different dependence probabilities is analyzed, and then the probability range corresponding to each dependent level is determined:

[0106] When the dependent propagation probability value is located in the interval [0.8, 1.0], it is divided into a strong dependent level;

[0107] When the dependent propagation probability value is located in the interval [0.5, 0.8), it is divided into a medium dependent level;

[0108] When the dependent propagation probability value is located in the interval [0.2, 0.5), it is divided into a weak dependent level;

[0109] When the dependent propagation probability value is located in the interval [0.0, 0.2), it is divided into a non-dependent level.

[0110] Step 7: Based on the dependency level divided in step 6, obtain the quantization value corresponding to each dependency level through a preset level-quantization value mapping rule; the construction basis of the level-quantization value mapping rule is: analyze the influence degree of different dependency levels on the query logic of parent-child fields, and the higher the influence degree, the larger the corresponding quantization value; wherein, the highest quantization value corresponds to the strong dependency level, because it has the greatest influence on the query logic; the medium quantization value corresponds to the medium dependency level, and its influence degree is second; the weak dependency level corresponds to a lower quantization value, and its influence degree is weaker; the zero value corresponds to the no dependency level, because it has no influence on the query logic; and the quantization value needs to be used for the identification distinction of the subsequent vertical connection between parent and child nodes.

[0111] Step 8: Based on the quantization value obtained in step 7, assign a corresponding connection line identifier to the vertical connection between parent and child nodes through a preset connection line identification rule; the connection line identification rule is obtained by mapping the quantization value corresponding to the dependency level in combination with the corresponding weight of the connection line;

[0112] Step 9: Based on the connection line identifier obtained in step 8 and the quantization value obtained in step 7, establish a vertical connection between the corresponding parent and child nodes through a preset node connection logic, and then obtain a vertical query dependency chain; the node connection logic includes:

[0113] Determine the positions of the parent and child nodes in the business form query configuration tree, take the parent field node as the starting end of the vertical connection, and take the child field node as the terminal end of the vertical connection;

[0114] According to the corresponding connection line identifier, draw a vertical connection line between the starting end and the terminal end, and associate the quantization value with the connection line to clearly indicate the dependency strength quantization information of the connection;

[0115] Verify the correctness of the established vertical connection, check whether the hierarchical relationship of the parent field and the child field is consistent with the business logic, whether the connection line identifier and the quantization value match the dependency level, and form a vertical query dependency chain after verification.

[0116] Step 10: Obtain the vertical query dependency chain corresponding to all fields in the target business scenario, and obtain the horizontal query causal chain corresponding to each field, take the vertical query dependency chain and the horizontal query causal chain as the edge data of the graph structure, and take each field as the node data of the graph structure; input the graph structure data into the graph neural network, learn and extract the dependency relationship features and association relationship features between nodes through the graph neural network, and construct a structured field association network; perform effectiveness verification on the field association network to ensure that the relationship between nodes and edges in the network conforms to the business form query logic, and obtain the field driving node layer after the verification is passed.

[0117] It needs to be further explained that the construction process of the second cross-layer driving combined connection in the embodiment includes:

[0118] Based on the field association logic relationship operator set, combined with the entity-relation extraction algorithm, an operator-combination-field node information set is obtained; the operator-combination-field node information set includes field query combination dependency relationship and field driving node layer, and the operator type, operator associated field ID, operator priority in the field association logic relationship operator set, combination logic structure, combination associated field ID list, and combination query validity state in the field query combination dependency relationship, and the ID of the effective field node, field data type, longitudinal dependency parent or child node ID, and transverse associated field ID in the field driving node layer are extracted respectively; the operator type is used to determine the logical type of the query operation in the query configuration node layer, and provides a logical rule for field combination; wherein, the operator associated field ID is used to accurately bind the operator and the effective field node of the field driving node layer, to ensure the correctness of the operator object; the operator priority is used to clearly define the execution order of the multi-operator combination in the query configuration node layer, to ensure the order of logical operation; the combination logic structure is used to construct the combination logic framework of the query configuration node layer, to provide a structural basis for field combination; the combination associated field ID list is used to limit the field range participating in the combination, to clearly define the field composition of the combination; the combination query validity state is used to filter valid combinations and exclude invalid combinations, to ensure the connection validity; the ID of the effective field node is used as a unique identifier, to ensure the accurate association of the operator, combination and field node of the field driving node layer; the field data type is used to verify the adaptability of the operator and the field, to ensure the legality of the logical operation; the longitudinal dependency parent or child node ID and the transverse associated field ID are respectively associated with the longitudinal query dependency chain and the transverse query causal chain, to ensure that the second cross-layer driving combination connection conforms to the hierarchical and inter-level relationship between fields, and to maintain structural consistency.

[0119] Based on the operator-combination-field node information set, through the association matching algorithm, the associated field ID of the field association logic relationship operator, the combination associated field ID list of the field query combination dependency relationship, and the ID of the effective field node in the field driving node layer are matched, to filter out the effective field nodes corresponding to the operator and the combination dependency, to obtain an operator-combination-effective field node matching set;

[0120] Based on the operator-combination-effective field node matching set, through the adaptability algorithm, taking the adaptability of the operator type and the effective field node data type, and the consistency of the combination logic structure and the longitudinal query dependency chain and the transverse query causal chain as the evaluation target, the adaptability of each operator-combination and the corresponding effective field node is calculated, and the operator-combination-effective field node combination with the adaptability greater than or equal to the preset adaptability threshold is retained, to obtain a qualified operator-combination-field node set;

[0121] It needs to be further explained that the obtaining process of the qualified operator-combination-field node set in the embodiment includes:

[0122] B1: Obtain a type adaptation degree score through a predefined type adaptation mapping rule based on the operator type and the data type of the effective field node; wherein the construction process of the type adaptation mapping rule is:

[0123] B1.1, Clearly define the range of operator types involved in the target business scenario, including basic logic operators, comparison operators, aggregation operators, etc., each of which corresponds to a specific logical function;

[0124] B1.2, Define the range of data types of the effective field node, including numeric type, character type, date and time type, enumeration type, etc., each of which corresponds to a specific data characteristic;

[0125] B1.3, Analyze the matching possibility of different operator types and different data types, determine the data types and matching degrees that each operator type can match based on the legality of business query logic, and the matching degree includes complete adaptation, partial adaptation and complete inadaptation;

[0126] B1.4, Assign a corresponding adaptation degree score to each matching degree, the highest score corresponds to complete adaptation, the medium score corresponds to partial adaptation, and the lowest score corresponds to complete inadaptation, forming a type adaptation mapping rule;

[0127] B1.5, Substitute the operator type and the data type of the effective field node into the type adaptation mapping rule, query the corresponding matching degree and score, and determine the score as the type adaptation degree score.

[0128] B2: Obtain a vertical consistency score through a structure consistency evaluation algorithm based on the combined logic structure and the vertical query dependency chain of the effective field node; wherein the execution process of the structure consistency evaluation algorithm is:

[0129] B2.1, Analyze the combined logic structure, extract the hierarchical relationship between the field nodes contained therein, and clearly define the parent node field, the child node field, and the hierarchical nesting relationship of the parent and child nodes;

[0130] B2.2, Extract the vertical query dependency chain of the effective field node, and clearly define the parent-child dependency relationship of the field nodes in the chain, including the control relationship of the parent field node to its child field node and the dependency relationship of the child field node to the parent field node;

[0131] B2.3, Compare the hierarchical relationship in the combined logic structure with the parent-child dependency relationship in the vertical query dependency chain: if the parent-child node correspondence relationship is completely consistent and the hierarchical nesting depth is the same, it is determined to be completely consistent; if the parent-child node correspondence relationship is partially consistent, or the hierarchical nesting depth has differences but the core parent-child relationship is consistent, it is determined to be partially consistent; if the parent-child node correspondence relationship is completely mismatched, it is determined to be completely inconsistent;

[0132] B2.4, assigning a vertical consistency score according to the consistency determination result, complete consistency corresponding to the highest score, partial consistency corresponding to the medium score, and complete inconsistency corresponding to the lowest score.

[0133] B3: based on the horizontal query causal chain of the combined logic structure and the effective field node, obtaining a horizontal consistency score through a causal correlation evaluation algorithm; wherein the execution process of the causal correlation evaluation algorithm is:

[0134] B3.1, analyzing the combined logic structure, extracting the logical connection relationship between the field nodes of the same level, and determining the logical operator used for connection and the associated meaning expressed by the operator;

[0135] B3.2, extracting the horizontal query causal chain of the effective field node, and determining the association relationship between the field nodes of the same level in the chain, including the association strength, the association direction, and the business semantics of the association;

[0136] B3.3, analyzing the consistency of the logical connection relationship in the combined logic structure and the association relationship in the horizontal query causal chain: if the meaning of the logical connection relationship completely matches the business semantics of the association relationship, and the logical tightness corresponding to the association strength is consistent, it is determined as complete consistency; if the meaning is partially matched, or the logical tightness corresponding to the association strength has differences but the core association direction is consistent, it is determined as partial consistency; if the meaning is completely unmatched, or the association direction is opposite, it is determined as complete inconsistency;

[0137] B3.4, assigning a horizontal consistency score according to the consistency determination result, complete consistency corresponding to the highest score, partial consistency corresponding to the medium score, and complete inconsistency corresponding to the lowest score.

[0138] B4: based on the type adaptation degree score obtained by B1, the vertical consistency score obtained by B2, and the horizontal consistency score obtained by B3, obtaining a comprehensive adaptation degree score through a weighted summation algorithm; wherein the execution process of the weighted summation algorithm is:

[0139] B4.1, determining the weight coefficients of each score, the weight coefficients being set based on the influence degree of each factor on the query combination effectiveness in the target business scenario, being obtained by business domain experts combined with historical query success cases, and the sum of all weight coefficients being 1;

[0140] B4.2, multiplying the type adaptation degree score, the vertical consistency score, and the horizontal consistency score by the respective corresponding weight coefficients to obtain the weighted values of each score;

[0141] B4.3, adding all the weighted values to obtain the sum, which is the comprehensive adaptation degree score of the operator-combination-effective field node combination.

[0142] B5: Based on the comprehensive adaptation score obtained in B4, a threshold comparison filtering algorithm is used to obtain a set of compliant operator-combination-field node sets based on a preset adaptation threshold; wherein the execution process of the threshold comparison filtering algorithm is as follows:

[0143] B5.1, determine a preset adaptation threshold, which is based on statistical analysis of the comprehensive adaptation scores of historical effective query combinations in the target business scenario, to ensure that the threshold can distinguish between effective combinations and ineffective combinations, and the query success rate of the effective combinations needs to reach a preset standard;

[0144] B5.2, compare the comprehensive adaptation score of each operator-combination-effective field node combination with the preset adaptation threshold;

[0145] B5.3, retain combinations with a comprehensive adaptation score greater than or equal to the preset adaptation threshold, and discard combinations with a comprehensive adaptation score less than the preset adaptation threshold;

[0146] B5.4, perform a second validity check on the retained combinations to check whether the operators, field nodes and logical relationships in the combinations conform to the business query rules, and form a set of compliant operator-combination-field node sets after the check is passed.

[0147] B6: Based on the set of compliant operator-combination-field node sets obtained in B5, the corresponding effective field nodes are connected through associated field association logical relationship operators according to the combination logical structure of the field query combination, to obtain a second cross-layer driving combination connection, and the specific process is as follows:

[0148] B6.1, analyze the combination logical structure corresponding to each combination in the set of compliant operator-combination-field node sets to determine the connection order of the field nodes, the type of operators used and the logical operation rules;

[0149] B6.2, according to the connection order of the combination logical structure, the effective field nodes and the associated field association logical relationship operators are connected in turn to form a combination connection unit containing effective field nodes, associated operators and combination logic;

[0150] B6.3, label the corresponding dependency identifier for each combination connection unit: if there is a parent-child hierarchical relationship between the field nodes in the combination connection unit, and the relationship is consistent with the vertical query dependency chain, then label the vertical dependency identifier; if the field nodes in the combination connection unit are of the same level and the association relationship is consistent with the horizontal query causal chain, then label the horizontal causal identifier;

[0151] B6.4, verify the integrity of the labeled combination connection unit to ensure that each unit contains complete effective field nodes, associated operators, combination logic and dependency identifiers, and there is no logical conflict between the elements;

[0152] B6.5, integrate all the combination connection units that pass the verification according to the business query logic to form a complete second cross-layer driving combination connection.

[0153] It needs to be further explained that the construction process of the second cross-layer driving combination connection in the embodiment includes:

[0154] Based on the "standard operator-combination-field node set", the combination logic structure defined in the field query combination dependency relationship is extracted by a combination logic analysis unit; in the business form query configuration tree, the field is the data entity unit of the query (such as order amount), the operator is the logical operator acting on the field (such as greater than), and the combination defines the logical structure and relationship between multiple field-operator conditions (such as and, or); the three form a "standard operator-combination-field node set" through association matching and adaptation degree calculation, and then an executable query logic module is constructed. For example: the field order amount and the operator > constitute condition E, the field order status and the operator IN constitute condition F, and then conditions E, F and condition C are integrated into a complete query rule through combination (E AND F) OR C.

[0155] Based on the combination logic structure, the corresponding effective field node and the associated field association logic relationship operator are connected by a node connection algorithm to form a three-tuple connection unit containing field nodes, logical operators and combination logic;

[0156] Determine the dependency characteristics of each connection unit by a dependency relationship analysis algorithm: when there is a hierarchical relationship between the field nodes in the connection unit, mark the vertical dependency identifier, and when there is a same-level association relationship between the field nodes in the connection unit, mark the horizontal causal identifier;

[0157] Verify the integrity and consistency of each connection unit by a connection verification algorithm to ensure that each connection unit contains complete field nodes, logical operators and combination logic three elements;

[0158] Aggregate all the connection units that pass the verification by a structure integration algorithm to form a complete second cross-layer driving combination connection. For example, a standard combination composed of field A, field B and logical operator AND:

[0159] First, the combination logic structure A AND B is obtained by parsing;

[0160] Connect field node A and field node B through AND operator to form connection unit {A, B, AND};

[0161] Analyze the relationship between fields A and B: if A is the parent field of B, mark the vertical dependency identifier; if A and B are sibling fields, mark the horizontal causal identifier;

[0162] After the verification connection unit contains all the necessary elements, it is integrated into the second cross-layer driver combination connection.

[0163] It should be further explained that the construction process of the third cross-layer driver index connection in the embodiment includes:

[0164] Based on the target index layer, the query operator mapping set and the field driver node layer, the index type, the index associated field identifier, the operator type, the operator and field mapping relationship, the effective field node identifier and the historical query frequency are extracted to obtain the index-operator-field layered information set; It should be further explained that the index type of the embodiment is used to clarify the category attribute of the index in the target index layer, and provides a type benchmark for the adaptation of the index and the operator; The index associated field identifier is used to associate the index of the target index layer with the corresponding field, and to ensure that the index points to the correct field object; The operator type is used to define the logical type of the query operation, and to provide a basis for the operation requirement of the index adaptation; The operator and field mapping relationship is used to establish the corresponding rule of the operator and the field, and to ensure that the operator is applicable to the matching field; The effective field node identifier serves as a unique identifier to ensure the accurate binding of the index, the operator and the field driver node layer; The historical query frequency is used to quantify the usage frequency of the operator and the index, and to provide a basis for the priority ordering of the connection.

[0165] Based on the index-operator-field layered information set, the operator associated field identifier and the effective field node identifier are matched through a field identifier matching algorithm, and the operator type and the index adaptation operation type are matched through a matching algorithm to obtain an index-operator-effective field matching set;

[0166] The acquisition process of the index-operator-effective field matching set is as follows:

[0167] D1: Obtain an index-operator-field hierarchical information set corresponding to a target service scenario, which is formed by integrating index attribute data of a target index layer, operator configuration data of a query operator mapping set, and effective field data of a field driven node layer; first, perform information structuring extraction on the information set, focusing on core information directly related to subsequent matching during the extraction process, which specifically includes operator associated field identification, effective field node identification, operator type, and index adaptation operation type; wherein the operator associated field identification is identification information that a query operator is pre-bound to in a preset configuration file and is used to uniquely point to a certain service field, which is extracted from the operator configuration data and needs to correspond to the actual identification of the service field; the effective field node identification is a unique identification corresponding to an effective field that can participate in the query after the field driven node layer is verified for availability (including query availability, data type adaptability, etc.); the operator type is an identification used to define the logical function category of the query operator, such as a comparison type operator used to implement accurate comparison, a logic type operator used to implement multi-condition combination, etc., which is extracted from the operator configuration data; the index adaptation operation type is a range of query operation types that can be normally responded by various indexes in the target index layer according to their technical characteristics (such as index structure, query support capability), which is extracted from the index attribute data; then, perform standardization processing on the extracted core information, for identification type information (such as operator associated field identification, effective field node identification), adopt a preset unified coding rule for standardization, which predefines the constituent elements and arrangement order of the identification, including business domain identification, form attribution identification, and field sequence identification, to ensure that identifications from different sources are completely unified in terms of character composition, length, and expression logic, for example, the same field identification from different systems has the same business domain prefix and form attribution suffix after standardization; for type type information (such as operator type, index adaptation operation type), adopt a preset unified expression form for standardization, which predefines the standard naming format of the type, the operator type is uniformly named as “operator function category-concrete operation name”, and the index adaptation operation type is uniformly named as “index structure type-support operation category”; through the above standardization processing, eliminate the matching deviation caused by format differences of information from different sources, ensure that the subsequent matching operation has a consistent benchmark, and finally form a standardized matching data set.

[0168] D2: Taking the standardized to-be-matched data set obtained in D1 as the operation basis, a field identifier matching algorithm is used to perform a field identifier matching operation. First, the operator-associated field identifier set and the effective field node identifier set are separated from the standardized to-be-matched data set, and the standardized operator-associated field identifier is determined as the matching reference key value. The selection of the reference key value is based on the pre-set relationship between the operator and the field, and the field identifier bound to the operator is used as the reference to ensure that the operator can be accurately associated with the corresponding effective field node. Then, for each reference key value, full-quantity traversal searching and comparison are performed in the standardized effective field node identifier set. In the searching process, character-by-character comparison is performed according to the standardized character format to ensure that the identifiers are completely consistent in terms of character order, character content, and expression integrity. If an identifier completely consistent with the current reference key value is found in the effective field node identifier set, it is determined that the operator and the corresponding effective field node are successfully matched in the field identifier. If the entire effective field node identifier set is traversed and no identifier completely consistent with the current reference key value is found, it is determined that the current matching fails. After the matching operation is completed, all matching successful association records are retained. The records need to completely contain the unique identifier of the operator, the corresponding operator-associated field identifier, and the matching successful effective field node identifier to ensure that the association relationship is traceable. At the same time, all matching failed association records are excluded, including the records in which the operator-associated field identifier does not exist in the effective field node identifier set or the identifier format does not pass the standardization check, and finally a field identifier matching subset is formed.

[0169] D3: Taking the field identifier matching subset obtained in D2 as the operation input, the type matching algorithm is used to perform type adaptability check on the operators in which the field identifier matching has been completed. First, the preset operator type and index adaptation operation type adaptation rule set is obtained, the rule set is constructed based on the technical characteristics of various indexes in the target index layer and the logical functions of the query operators, and clearly specifies the operator type range corresponding to each index adaptation operation type, for example, the hash index only supports exact match query due to technical characteristics, and the operator type corresponding to its adaptation operation type only includes the exact comparison class operator; the B-tree index supports exact match and range query, and the operator type corresponding to its adaptation operation type includes the exact comparison class operator and the range comparison class operator; then for each associated record in the field identifier matching subset, the operator type of the operator in the record is extracted, and from the standardized to-be-matched data set obtained in D1, the index adaptation operation type of the index associated with the operator (determined by the index association identifier of the operator) is found; the extracted operator type and the found index adaptation operation type are judged for compliance according to the adaptation rule set, if the operator type is determined to be within the range defined by the index adaptation operation type of the associated index according to the rule set, it is determined that the type matching is successful; if the operator type is determined to be not within the above defined range according to the rule set, it is determined that the type matching fails; after the check is completed, all the associated relationship records of the type matching success are retained, the record needs to completely contain the unique identifier of the index, the unique identifier of the operator and the unique identifier of the effective field node, to ensure the completeness and traceability of the association relationship among the three; at the same time, all the associated relationship records of the type matching failure are eliminated, including the records whose operator type exceeds the index adaptation range or whose index adaptation operation type is not clear, and finally the type matching subset is formed.

[0170] D4: Taking the field identifier matching subset obtained in D2 and the type matching subset obtained in D3 as common operation inputs, performing integration and structured processing of the associated data by using an association integration algorithm, specifically: first, extracting core identifier information contained in each associated record from the two subsets, constructing an associated data tuple with index unique identifier, operator unique identifier and effective field node unique identifier as elements, each data tuple needs to correspond to an associated record completely, and the three types of identifiers in the tuple need to be able to uniquely determine the association relationship of index-operator-effective field node; second, performing consistency filtering on the associated data tuples of the two subsets, the filtering basis is that the index unique identifier, operator unique identifier and effective field node unique identifier in the tuple are completely consistent, to ensure that the filtered tuples are associated data existing in the field identifier matching subset and the type matching subset at the same time; third, based on the associated data tuples obtained by filtering, according to the fixed correspondence relationship of index-operator-effective field node, combined with the tree database to perform hierarchical mounting operation, in the mounting process, taking the index corresponding to the index unique identifier as the root node of the tree structure, taking the operator (corresponding to the operator unique identifier) adapted to the index as the child node under the root node, and taking the effective field node (corresponding to the effective field node unique identifier) adapted to the operator as the leaf node under the child node, to ensure that the hierarchical relationship of the tree structure is completely consistent with the association logic of index-operator-effective field node, through association integration and hierarchical mounting operation, an index-operator-effective field matching set is obtained.

[0171] Based on the index-operator-effective field matching set, the adaptation degree of the operator and the field data type, the adaptation degree of the operator and the index type, and the priority score based on the historical query frequency are calculated by an adaptation degree evaluation algorithm, combinations with a comprehensive adaptation degree reaching a preset threshold are retained, and a standard index-operator-field set is obtained;

[0172] Based on the standard index-operator-field set, an associated connection between the effective field node, the query operator and the target index node is established according to the operator and field mapping relationship by a connection unit construction algorithm, and a basic index connection unit set annotated with historical query frequency weight and adaptation state identifier is obtained;

[0173] Based on the basic index connection unit set, the structure compatibility of the connection unit and the field driven node layer and the target index layer is verified by a hierarchical compatibility verification algorithm, the connection units with logical conflicts or destroying hierarchical structure are eliminated, and a compatible index connection unit set is obtained.

[0174] It needs to be further explained that the acquisition process of the compatible index connection unit set in the embodiment includes:

[0175] G1, based on the basic index connection unit set, each connection unit is traversed by a connection unit information extraction algorithm to extract its core information to constitute a connection unit core information set; the basic index connection unit set contains the association relationship between the valid field node, the query operator, the target index node, and the historical query frequency weight and the adaptation state identifier; the core information includes: the valid field node ID and its hierarchical attribute in the field driven node layer, the hierarchical attribute includes the parent node ID, the child node ID, the longitudinal dependency level, the horizontal associated field ID; the query operator type; the target index node ID and its hierarchical attribute in the target index layer, the hierarchical attribute includes the index level, the parent index node ID, the supported operator type set; the cross-layer association path of the connection unit, that is, the mapping relationship from the field node to the index node through the operator;

[0176] G2, based on the connection unit core information set and the longitudinal query dependency chain and the horizontal query causal chain in the field driven node layer, a field layer dual compatibility verification algorithm is used for verification, specifically including:

[0177] Check whether the field node in the connection unit meets the parent-child relationship rules defined in the longitudinal query dependency chain; when the field is a child node, verify that its parent node has been correctly associated and the current operator does not exceed the constraint range set by the parent node; when the field is a parent node, verify that the index node connected by it has the coverage ability to meet the potential query requirements of its child node;

[0178] Check whether the connection relationship of the field node and the associated field in the horizontal query causal chain is consistent; for fields with causal association, the operator logic thereof should be consistent, and a connection unit should not be established for only one associated field while ignoring other necessary associated fields;

[0179] After the above verification is completed, the field layer compatibility verification result is output;

[0180] G3, based on the field layer compatibility verification result and the index hierarchical structure and index rules defined in the target index layer, an index layer triple compatibility verification algorithm is used for verification, and the index rules at least include the operator types supported by the index and the field range covered by the index; specifically including:

[0181] Check whether the level of the target index node matches the level of the field node, prohibit low-level index nodes from connecting high-level field nodes, and ensure that the basic index nodes relied on by high-level index nodes have established effective connection units;

[0182] Verify whether the current query operator type is included in the operator type set supported by the target index node;

[0183] checking whether a field range covered by a target index node contains all associated fields of a current field node;

[0184] After the above verification is completed, an index layer compatibility verification result is outputted;

[0185] G4, based on the index layer compatibility verification result, each connection unit is determined by a conflict and hierarchy destruction determination algorithm according to the following preset rules:

[0186] If the connection unit violates the vertical dependency rule or the horizontal association rule, it is determined to be a logical conflict, and the specific process includes:

[0187] All vertical dependency relationships of the field node in the connection unit are traversed, and it is checked whether the parent node ID exists in the current effective connection unit set; if it exists, it is further verified whether the value range of the current operator exceeds the range defined by the parent node constraint condition. If the parent node is missing or the operator is out of bounds, it is marked as violating the vertical dependency rule.

[0188] The horizontal query causal chain is queried to obtain a set of all necessary field IDs that have causal association with the field node; it is checked whether these associated fields have been correctly associated in the connection unit or other associated units, and it is verified whether the operator logic used by all associated fields is consistent. If a necessary associated field is missing or the operator logic is inconsistent, it is marked as violating the horizontal association rule.

[0189] If any of the above rules is violated, it is determined that the connection unit has a logical conflict, and the determination result of each connection unit is outputted.

[0190] If the connection unit violates the index hierarchy matching rule or the index coverage rule, it is determined to destroy the hierarchy structure, and the specific process includes:

[0191] The index level value of the target index node is compared with the hierarchy level value of the field node; if the index level value is lower than the field hierarchy level value, it is determined that the low-level index connects the high-level field, which violates the hierarchy matching rule; at the same time, if the target index is a high-level index node, it is checked whether all connection units corresponding to the underlying index nodes on which it depends have been established and are valid. If there is a missing underlying index connection unit, it is also determined to violate the hierarchy matching rule.

[0192] The coverage field range set declared by the target index node and the ID set of the current field node and all associated fields thereof are obtained. If the coverage field range set is not a superset of the associated field ID set, i.e., there is an associated field that is not covered by the index, it is determined to violate the index coverage rule.

[0193] If any of the above rules is violated, it is determined that the connection unit destroys the hierarchy structure, and the determination result of each connection unit is outputted.

[0194] If there is no above-mentioned violation, it is determined that there is no exception, and the determination result of each connection unit is output.

[0195] Based on the connection unit determination result, through the connection unit screening and sorting algorithm, the connection units determined to be logical conflicts or to destroy the hierarchical structure are removed, the connection units without exception are retained, and the retained connection units are sorted in the order of field node hierarchy→index node hierarchy, to obtain a compatible index connection unit set.

[0196] Based on the compatible index connection unit set, the sorting is performed according to the product of the historical query frequency weight and the adaptation score, and based on the sorted compatible index connection unit set, the query configuration node layer and the target index layer are bound according to the mapping relationship between the operators and the fields, to complete the third cross-layer driven index connection.

[0197] It needs to be further explained that the minimum effective field adjustment tree in the embodiment is obtained by traversing based on the query fields that need to be changed in the business form query configuration tree, the change operation complexity of the corresponding associated query fields, the query permission of the query operator corresponding to the single or composite query field, the query fine-grained index, and the parent-child hierarchical relationship, through the Bayesian algorithm and the optimization traversal algorithm, combined with the preset minimum change operation range and the maximum fitting degree target. The optimization traversal strategy is constructed, and the minimum effective field adjustment tree is obtained by traversing. The fitting degree is represented by the query exception probability of the changed business form query configuration tree for the business form, which is detected and obtained by the Bayesian algorithm during the query process, and is used to represent the effectiveness of the changed business form query configuration tree. The fitting degree is represented by the following technical means, and is used to represent the effectiveness of the changed business form query configuration tree. Specifically, for the changed business form query configuration tree and the corresponding business form, in the process of executing the query operation of the business form, the historical query exception data of the business form, the field association relationship in the changed business form query configuration tree, the query operator configuration and the field query permission attribute are used as input parameters. The Bayesian algorithm is used to calculate the probability of potential abnormal conditions such as data filtering logic conflict, field permission mismatch, and query result data exception during the query operation, to obtain the query exception probability corresponding to the query operation. The query exception probability directly represents the fitting degree. The numerical value of the query exception probability is associated with the fitting degree and the effectiveness of the changed business form query configuration tree. That is, the lower the query exception probability, the higher the fitting degree, the stronger the adaptability of the changed business form query configuration tree to the business form query logic, and the higher the effectiveness. The higher the query exception probability, the lower the fitting degree, the weaker the adaptability of the changed business form query configuration tree to the business form query logic, and the lower the effectiveness.

[0198] It needs to be further explained that the construction process of the minimum effective field adjustment tree in the embodiment includes:

[0199] S2.1, based on the service form query configuration tree, receiving and parsing the externally input service demand change instruction, extracting the explicitly specified query fields that need to be changed to form an initial change set; taking each field in the initial change set as a traversal starting point, using a depth-first search algorithm to traverse the vertical query dependency chain to obtain all hierarchical associated fields, and using a breadth-first search algorithm to traverse the horizontal query causal chain to obtain all peer associated fields; performing a set operation on the initial change set and all obtained associated field sets to generate a full set of fields to be adjusted after removing duplicate fields;

[0200] S2.2, based on the full set of fields to be adjusted, calculating the change operation complexity value of each field one by one through a multi-factor weighted calculation algorithm, specifically including:

[0201] Data type conversion difficulty coefficient calculation: based on the source data type and target data type of the field to be evaluated, query the predefined type compatibility rule matrix for matching, which is established according to the type conversion specifications of SQL standards and mainstream database products, divide the conversion operation into multiple safety levels according to potential data precision loss and semantic consistency risk, and output the corresponding preset difficulty level coefficient;

[0202] Based on the constraint condition set defined on the field, the parser of the integrated Apache Calcite SQL verification framework is used for semantic analysis and dependency relationship analysis, the logical expression complexity, the number of associated objects and the verification overhead of the constraint condition are calculated, and a quantitative constraint redefinition complexity value is output;

[0203] Based on the associated pointer attribute defined by the field in the business form query configuration tree, the field network directly and indirectly associated with it is analyzed through a graph traversal algorithm, the total number of affected associated fields is counted and standardized by applying a logarithmic function model, and an adjustment amplitude coefficient is output;

[0204] Based on the depth level value of the field in the query configuration tree structure, a preset exponential decay weight model is used for calculation, and a level depth coefficient negatively related to the depth value is output;

[0205] Based on the difficulty level coefficient, the constraint redefinition complexity value, the adjustment amplitude coefficient and the level depth coefficient, a comprehensive calculation is performed through a weighted summation algorithm, wherein the weight coefficients corresponding to the difficulty level coefficient, the constraint redefinition complexity value, the adjustment amplitude coefficient and the level depth coefficient are determined in advance through AHP hierarchical analysis method based on the requirements of specific business scenarios, and the change operation complexity value of each field is output.

[0206] S2.3, based on the set of fields to be adjusted, querying the query permission attribute and the query fine-grained index of each field node from the business form query configuration tree through the attribute extractor; wherein the query permission attribute includes readable, writable, and filterable state identifier; the query fine-grained index is obtained by analyzing the historical query result selection degree of the field corresponding to the query operator;

[0207] S2.4, based on the set of fields to be adjusted and its associated information, a network model is constructed by using a Bayesian network construction engine, specifically including:

[0208] Taking the field as the node, and taking the parent-child hierarchical relationship and the horizontal association relationship between the fields as the directed edge;

[0209] The node prior probability is determined by the reciprocal of the field change operation complexity value after normalization processing;

[0210] The conditional probability is obtained by jointly calculating the field dependency level, the association strength, and the query permission matching degree;

[0211] The network contains the query fine-grained index adaptation degree as an observation variable, which is obtained by comparing the similarity of the current fine-grained index of the field and the expected index after the change;

[0212] S2.5, based on the Bayesian network model, an improved algorithm is used for optimal traversal search, specifically including:

[0213] Taking the minimization of the total field change operation complexity as the objective function;

[0214] Taking the fit degree not less than the preset threshold as the constraint condition, wherein the fit degree is obtained by taking the reciprocal of the comprehensive abnormal probability after collecting the query failure rate, the result error rate and the performance decline rate three indexes through the real-time simulator executing the query test after the change;

[0215] A dynamic pruning strategy is used in the traversal process, and the path search is immediately terminated when the cumulative change complexity of the path exceeds the preset upper limit or the real-time fit degree is lower than the threshold;

[0216] Output the field adjustment subset that meets the constraint condition and has the optimal objective function;

[0217] S2.6. Based on the optimal field adjustment subset, a tree structure reconstruction algorithm is used to construct the minimum effective field adjustment tree, specifically including:

[0218] The tree structure is established according to the parent-child hierarchical relationship of the field in the original business form query configuration tree;

[0219] The horizontal association relationship between the fields is retained as an auxiliary connection;

[0220] labeling complete change attribute set for each field node, including change operation complexity value, query permission attribute and query granularity index;

[0221] generating independent minimum effective field adjustment tree, which only contains the minimum field set and its complete associated information necessary for implementing change requirements.

[0222] It should be further explained that the specific process of the optimal traversal strategy in the embodiment includes:

[0223] Based on the business form query configuration tree, the query field to be changed specified by the external business requirement change instruction is taken as the traversal starting point; by traversing the vertical query dependency chain and the horizontal query causal chain in the configuration tree, all directly or indirectly associated query fields are obtained to form the full set of fields to be adjusted; a Bayesian network construction algorithm is used to construct a network model with fields as nodes and parent-child hierarchical relationship and horizontal association relationship between fields as edges, wherein the node probability distribution is determined by the change operation complexity obtained by weighted summation of field data type conversion difficulty, constraint condition redefinition complexity, associated pointer adjustment amplitude and field hierarchical depth, as well as the query permission identifier and query granularity index of the field; based on the Bayesian network model, the minimum change operation range and the maximum fitness degree are taken as the joint optimization objectives, the heuristic search algorithm is used as the optimal traversal method, the root node is taken as the starting point, the depth-first traversal is performed along the parent-child hierarchical relationship, and the breadth expansion is performed on the horizontal causal chain, the cumulative change operation complexity and the fitness degree score under different paths are dynamically calculated; wherein the minimum change operation range is determined by calculating the conditional probability between the parent field and the associated child field and applying a preset probability threshold; the fitness degree is represented by the reciprocal of the comprehensive abnormal probability of the query configuration tree after change in query failure rate, result error rate and performance decline rate; during the traversal process, branches exceeding the change range or having a fitness degree lower than a preset threshold are pruned in real time; finally, the field node set covered by the path with the lowest cumulative change complexity and the highest fitness degree score is selected, organized according to the original tree hierarchy and association relationship, and labeled with the change attributes of each field, to generate an independent minimum effective field adjustment tree.

[0224] The acquisition process of the minimum change operation range is as follows: based on the parent-child hierarchical relationship of the business form query configuration tree, the field to be changed is taken as the initial parent field, each associated child field corresponding to the initial parent field is traversed, and a Bayesian algorithm is used to calculate the conditional probability of each associated child field needing to be adjusted with the corresponding parent field, based on the query permission, query granularity index and dependency degree of the parent field and the associated child field.

[0225] The associated sub-fields with the conditional probability lower than the preset threshold are excluded, and a set of the remaining fields is taken as a minimum change operation range of the current change operation; the query fine-grained index is constructed according to the selection degree of the query result of each single or composite query field corresponding to a single or composite query operator, and is used to optimize the accuracy of the query index.

[0226] It needs to be further explained that the process of the database index creation or adjustment statement in the embodiment includes:

[0227] S3.1: A traversal operation is performed on the minimum effective field adjustment tree, and a hierarchical traversal or a breadth-first traversal is adopted to ensure that all field nodes in the tree are covered; in the traversal process, complete attribute information of each field node is extracted, and the attribute information specifically includes a field identifier, a data type, a change operation complexity value, a query permission attribute, a query fine-grained index, a parent-child hierarchical relationship identifier, and a horizontal association relationship identifier; the field identifier is identifier information for uniquely distinguishing the field, which is obtained from field metadata of the business form query configuration tree; the data type is a category attribute of field stored data, including numerical type, character type, date and time type, etc.; the change operation complexity value is an index quantifying the amount of work required to modify the field, and the calculation basis includes the number of business logic nodes associated with the field, the influence range on the downstream query link after modification, and the number of tables associated with the field data; the wider the influence range and the more associated nodes, the higher the complexity value; the query permission attribute is an identifier of whether the field has query-related permissions, including whether it contains filtering permissions (i.e., whether the field can be used as a query filtering condition), read-only permissions, etc.; the query fine-grained index is an index describing the selection degree of the field in the query, and the selection degree is represented by the uniqueness of the field value; the more unique the field value, the more accurate the target data filtered in the query, and the higher the selection degree; the parent-child hierarchical relationship identifier is information representing the parent-child hierarchical association of the field with other fields, specifically the parent field identifier (if there is a parent field) or the list of child field identifiers (if there are child fields) corresponding to the field; the horizontal association relationship identifier is information representing the association of the field with other fields in the same level, specifically a list of associated field identifiers in the same level corresponding to the field.

[0228] S3.2: Based on the field node attribute information extracted in S3.1, a set of candidate index schemes is generated, and the specific operation is as follows: first, fields that meet the index generation basic conditions are selected, and then single-column or composite index candidate schemes are generated for different conditions, and corresponding index types are assigned; specifically:

[0229] S3.2.1, fields that are explicitly included in the filtering permission attribute in the query permission and have a change operation complexity value below a preset threshold are individually generated as single-column index candidate schemes, the preset threshold is based on historical change operation data of the target business scenario, specifically, the complexity value corresponding to the average workload in the historical change operation is calculated, fields with a modification cost below this value have a lower cost and are suitable for individual index establishment;

[0230] S3.2.2, according to the parent-child hierarchical relationship identifier and the horizontal association relationship identifier, determine the field combination that has business association (such as fields with parent-child hierarchical relationship, fields in the same hierarchical level and frequently appearing in query conditions), generate a composite index candidate scheme using a combination generation algorithm, the combination generation algorithm is a greedy algorithm or an Apriori algorithm, if the greedy algorithm is used, fields with high query frequency and high data selection degree are preferentially selected to join the combination, and the combination is gradually expanded until the number of combination fields reaches a preset upper limit; if the Apriori algorithm is used, frequent field combinations are mined as the field composition of the composite index based on the frequency of fields appearing together in query conditions; after generating the composite index candidate scheme, the fields in the composite index are sorted according to a predefined field order rule, the field order rule is based on the data selection degree and query frequency of the fields, fields with high data selection degree and high query frequency are placed in the front row of the composite index to improve the index filtering efficiency;

[0231] S3.2.3, according to the data type of the field and the predefined index type mapping rule, assign a corresponding index type to each candidate index scheme, the predefined index type mapping rule is constructed based on the query characteristics of different data types and the index technology characteristics, for example, numerical fields are suitable for B-tree index (supporting range query and exact query), character fields are assigned hash index if they are commonly used for exact match query, or B-tree index if they are commonly used for prefix match query, date and time fields are suitable for B-tree index (supporting time range query).

[0232] S3.3: each candidate index scheme is evaluated in multiple dimensions using a predefined cost model, and the scheme decision is made through a multi-objective optimization function, specifically: first, calculate the query performance improvement degree of each candidate scheme, then calculate various cost indicators, and finally select the optimal scheme through an optimization algorithm;

[0233] S3.3.1, based on the historical query log data of the target business scenario, statistics the query frequency of the field corresponding to each candidate index scheme in the historical query (i.e. the number of times the field appears as a query condition), and calculates the data selectivity of the field, which is a quantitative indicator of the index filtering data capability. The data selectivity is determined by calculating the ratio of the number of records returned by the query to the total number of records in the data table under common query value conditions (such as equal to, greater than, less than, range containing, etc.). The smaller the ratio, the higher the selectivity and the higher the precision of the filtered data. Combining the historical query frequency and the data selectivity, the performance improvement degree of each candidate index scheme is calculated through a pre-defined performance evaluation formula. The higher the query frequency and the data selectivity, the greater the performance improvement degree.

[0234] S3.3.2, according to the real-time collected database storage data, statistics the storage cost (i.e. the storage space size occupied by the index in the database) of each candidate index scheme; by analyzing the write maintenance mechanism of the index, statistics the write maintenance cost (i.e. the time and resource consumption required to update the index when data is added, modified or deleted in the data table); by evaluating the impact of index adjustment on the existing business system, obtain the change compatibility cost, which is used to measure the impact of index adjustment on the existing system running. The impact is determined by analyzing whether the business service needs to be suspended during the index adjustment process, whether the existing query statements will be blocked, whether the existing application program configuration needs to be modified, etc. The greater the impact, the higher the change compatibility cost.

[0235] S3.3.3, taking maximizing query performance benefit (i.e. query performance improvement degree), minimizing storage cost, minimizing write maintenance cost, and minimizing change compatibility cost as four optimization objectives, using a multi-objective optimization algorithm to optimize and filter all candidate index schemes. The multi-objective optimization algorithm is a constrained genetic algorithm or a non-dominated sorting genetic algorithm. If a constrained genetic algorithm is used, the constraint conditions are pre-set, including at least that the storage cost does not exceed the pre-set proportion of the total available storage space of the database, and the change compatibility cost does not exceed the pre-set impact threshold. Through the selection, crossover and mutation operations of the genetic algorithm, schemes that meet the constraint conditions and perform well in the four optimization objectives are selected. If a non-dominated sorting genetic algorithm is used, all candidate schemes are hierarchically divided through non-dominated sorting, and the schemes at the highest level (i.e. there is no other scheme that is better than this scheme in all four optimization objectives) are selected to obtain the Pareto optimal solution set.

[0236] S3.4: Perform the generation and output operation of the final index strategy, first obtain the pre-defined business rule library, which is constructed based on the business priority and technical requirements of the target business scenario, and contains rule entries in different business scenarios (such as the core business query scenario preferentially selecting the solution with the highest query performance improvement degree, the non-core business scenario preferentially selecting the solution with the lowest storage cost and maintenance cost, and the system stable operation preferentially selecting the solution with the lowest change compatibility cost); based on the rule entries in the business rule library, each solution in the Pareto optimal solution set obtained in S3.3 is matched, and the solution most suitable for the current business scenario priority is selected as the final index strategy; according to the specific requirements of the final index strategy (such as adding an index, adjusting an existing index, rebuilding an index, and deleting a useless index), the corresponding database index operation statement is generated, the index operation statement includes one or more of CREATE INDEX (used for adding an index), ALTER INDEX (used for adjusting the attributes or structure of an existing index), REINDEX (used for rebuilding an index to optimize index performance), and DROP INDEX (used for deleting an index that is no longer used), and the syntax of the operation statement conforms to the specification requirements of the target database management system, ensuring that it can be directly executed in the database.

[0237] The embodiment realizes intelligent optimization and dynamic adjustment of database index by constructing a business form query configuration tree and a minimum effective field adjustment tree, and brings many significant beneficial effects. The query performance is greatly improved. Based on the multi-level association of the field driven node layer and the target index layer and the historical query frequency weighting, the system can accurately generate an index strategy that is highly matched with the business query mode, so that the high-frequency query condition can directly hit the efficient index, reduce the full table scan, significantly shorten the query response time and enhance the system concurrent support capability. The system maintenance cost is significantly reduced. Through automatic analysis of form unit data, adaptive construction of query configuration tree and minimum change adjustment mechanism, the tedious workload of traditional manual index optimization is avoided, the operation and maintenance efficiency is improved, and the accuracy and safety of index adjustment are guaranteed, without manual intervention to avoid index redundancy and conflict. The business adaptability is also enhanced. Relying on cross-layer driven connection and dynamic correlation calculation, the system can real-time perceive business changes and quickly generate a minimum adjustment scheme through Bayesian network and optimization algorithm, so that the index strategy always keeps high consistency with business demand, and the business query abnormal rate is greatly reduced. The resource utilization rate is optimized. Through the multi-objective cost model and the Pareto optimal solution selection, the storage overhead, write cost and change compatibility are balanced while improving the query performance, reducing the overall system resource consumption, and realizing the unification of high performance and low cost. These effects are directly derived from the core technical mechanism of the embodiment. The structured modeling based on text analysis and graph neural network ensures the deep fit of index strategy and business logic; the cross-layer driven connection and dynamic weight distribution realize the accuracy and adaptability of index generation; the minimum adjustment tree and multi-objective optimization guarantee the efficiency and system stability of change. Overall, the scheme not only solves the problems of experience dependence, response lag and error-prone in traditional database index management, but also provides a new paradigm of systematic, adaptive and continuous optimization of intelligent index management, which has high engineering application value and promotion prospect.

[0238] Embodiment 2

[0239] Please refer to Figure 2 The application provides another embodiment of a business form field driven dynamic index generation system, which comprises a configuration tree module, an analysis module, an index construction module and an exception adjustment module.

[0240] The configuration tree module obtains a business form query configuration tree based on the query configuration items of the form fields in the business form configuration subsystem and a tree database through graph algorithm.

[0241] The parsing module is used for obtaining a real-time configuration change operation of a query configuration item of a form field in a business form configuration subsystem in combination with an optimal traversal strategy, obtaining a minimum effective field adjustment tree with minimum change constraint and maximum matching degree as targets, and the minimum effective field adjustment tree is obtained by traversing a preset target function constructed by a minimum change operation range and a maximum matching degree through a Bayesian algorithm and an optimal traversal algorithm based on a change operation complexity of a query field to be changed and a corresponding associated adjustment query field, a query permission of a query operator corresponding to a single or composite query field, a query granularity index, and a parent-child hierarchical relationship of the business form query configuration tree extracted from the business form query configuration tree; wherein the matching degree is represented by a query exception probability detected and obtained by the Bayesian algorithm in a query process of the changed business form query configuration tree, and is used for associatively representing effectiveness of the changed business form query configuration tree; the minimum change operation range is obtained based on the hierarchical association relationship of the business form query configuration tree, and the minimum change operation range is obtained by taking the field to be changed as an initial parent field, traversing each associated child field corresponding to the initial parent field, and calculating a conditional probability of each associated child field needing to be adjusted with a corresponding parent field by using the Bayesian algorithm with the query permission, the query granularity index, and the dependence degree of the parent field and the associated child field as conditions; the associated child field with a conditional probability lower than a preset threshold is excluded, and a set of the remaining fields is taken as the minimum change operation range of the current change operation; and the query granularity index is constructed according to a selection degree of a query result of a single or composite query operator corresponding to each single or composite query field, and is used for optimizing precision of a query index.

[0242] The index construction module obtains a database index creation or adjustment statement through an index strategy engine based on the minimum effective field adjustment tree.

[0243] The abnormal adjustment module is used for automatically adjusting the business form query configuration tree by the database index creation or adjustment statement, and responding to real-time query requirements of a new business form, positioning the business form real-time query requirement and the corresponding updated query index when any query requirement is abnormal, and feeding back the business form real-time query requirement and the corresponding updated query index to the optimal traversal strategy for real-time updating until all query requirements are met.

[0244] The embodiments of the application are described above with reference to the drawings, but the application is not limited to the specific embodiments described above, and the specific embodiments described above are only illustrative rather than limiting, and a person of ordinary skill in the art can make changes, modifications, replacements, and variations to the above-described embodiments without departing from the purpose of the application and the scope protected by the claims under the inspiration of the application, and these are all within the protection of the application.

Claims

1. A dynamic index generation method driven by business form fields, characterized in that, include: Based on the query configuration items of form fields in the business form configuration subsystem, a graph algorithm and a tree-structured database are used to obtain the business form query configuration tree; By combining real-time configuration change operations of query configuration items for form fields in the business form configuration subsystem with an optimal traversal strategy, the minimum effective field adjustment tree is obtained with the goal of minimizing change constraints and maximizing fit. Based on the minimum effective field adjustment tree, the database index creation or adjustment statement is obtained through the index strategy engine; The system automatically executes the database index creation or adjustment statements to adjust the business form query configuration tree and responds to the real-time query requirements of newly added business forms. When any query requirement is abnormal, the system locates the real-time query requirement of the business form and the corresponding updated query index. The real-time query requirements of the business forms and the corresponding updated query index are fed back to the optimal traversal strategy for real-time updates until all query requirements are met.

2. The dynamic index generation method driven by business form fields as described in claim 1, characterized in that, The minimum effective field adjustment tree is derived from the business form query configuration tree by extracting the query fields to be changed and the complexity of the change operations of the corresponding associated query fields, the query permissions of the query operators corresponding to single or compound query fields, the fine-grained query indicators, and the parent-child hierarchical relationship. It is obtained by traversing a preset objective function constructed from the minimum change operation range and the maximum fit, using a combination of Bayesian algorithm and optimization traversal algorithm. The fit is represented by the query anomaly probability obtained by Bayesian algorithm during the query process of the modified business form query configuration tree for the business form, and is used to associate and represent the effectiveness of the modified business form query configuration tree. The process of obtaining the minimum scope of change operation is as follows: based on the parent-child hierarchical relationship of the business form query configuration tree, taking the field to be changed as the initial parent field, traversing each associated child field corresponding to the initial parent field, and using the Bayesian algorithm, with the query permissions, query fine-grained indicators and dependency degree corresponding to the parent field and the associated child field as conditions, calculating the conditional probability that each associated child field needs to be adjusted with the corresponding parent field. Related subfields with conditional probabilities below a preset threshold are excluded, and the set of remaining fields is used as the minimum scope of the current change operation. The fine-grained query index is constructed based on the selectivity of the query results of the single or compound query operators corresponding to each single or compound query field, and is used to optimize the accuracy of the query index.

3. The dynamic index generation method driven by business form fields as described in claim 2, characterized in that, The process of obtaining the business form query configuration tree includes: Based on the business form text information and text parsing algorithm, a form metadata field table is obtained; the form metadata field table includes at least the form ID, field and corresponding ID set, field association pointer, field association logical relationship operator set and field query combination dependency relationship, field query validity status label, field data type and constraint attribute, query operator mapping set, historical query frequency, and form business affiliation label. The root node is constructed using the form ID and the form business belonging label; the field-driven node layer is constructed using the fields and their corresponding ID sets, field data types and constraint attributes and field association pointers; the query configuration node layer is constructed using the set of field association logical relationship operators; and the target index layer is constructed using the business form information data. Based on the root node, the connection relationships between each field node in the field-driven node layer, the connection relationships between query operation nodes in the query configuration node layer, the connection relationships between target index character nodes in the target index layer, and the form ID and field ID, a first cross-layer driven connection is constructed. A second cross-layer driven combined connection is constructed based on the set of field association logic relationship operators and the field query combination dependency relationship. A third cross-layer driven index connection is constructed based on the set of query operator mappings and the historical query frequencies corresponding to the corresponding fields and query operators. Finally, a business form query configuration tree is constructed using a graph neural network and a tree-structured database.

4. The dynamic index generation method driven by business form fields as described in claim 3, characterized in that, The process of obtaining the first cross-layer driver connection includes: Based on the form ID, fields and corresponding ID sets, construct the first one-to-many connection sequence between the root node and the field-driven node layer; Based on the business form attribution tags and field attribute features, text feature extraction algorithms are used to obtain business domain classification features, field-related business indicator type features, periodic association matching features, and scenario constraint matching features. Based on business domain classification features, field-related business indicator type features, periodic association matching features, and scenario constraint matching features, the query-driven association degree between the business form and each field is obtained through association analysis algorithms. Based on the field query validity status label, obtain the corresponding binary label, the binary label includes 1 and 0, 1 corresponds to the field being valid, and 0 corresponds to the field being disabled; The binary label and the query-driven correlation are embedded into the first connection sequence to construct the first cross-layer driving connection.

5. The dynamic index generation method driven by business form fields as described in claim 4, characterized in that, The process of obtaining the field-driven node layer includes: Based on the binary labels, fields and corresponding ID sets, field data types and constraint attributes, and field association pointers in the first cross-layer driving connection, valid fields are filtered through availability verification rules to obtain a basic list of valid fields; Based on the effective field base list, extract the parent-child field hierarchical relationship features from the field association pointers to construct a vertical query dependency chain, and at the same time extract the horizontal causal features of the corresponding same-level associated query fields for each field to construct a horizontal query causal chain. A field-driven node layer is constructed by combining the vertical query dependency chain and the horizontal query causal chain corresponding to all fields with a graph neural network.

6. The dynamic index generation method driven by business form fields as described in claim 5, characterized in that, The process of constructing the horizontal query causal chain includes: obtaining the correlation degree between any two fields of the same level based on the fixedness of the logical operators in the combined query, the intersection ratio of the query result sets of the two fields, the mutual constraint degree of the field query conditions, and the business semantic association score combined with the association analysis algorithm, as the horizontal connection between the corresponding two fields; and obtaining the horizontal query causal chain based on the horizontal connection between the two fields of the same level and the field nodes constructed by the corresponding two fields. The process of constructing the vertical query dependency chain is as follows: extract the query enable control degree of the parent field to its child field, the constraint degree of the child field's value range on the parent field, the reference depth of the child field's query logic to the parent field, the passive adjustment rate of the child field when the parent field changes, and the data lineage relationship between the parent and child fields, combined with the infectious disease model, to construct the query dependency degree between any parent and child fields, which serves as the vertical connection between the parent and child nodes constructed for the corresponding parent and child fields; based on all parent and child nodes and their corresponding vertical connections, the vertical query dependency chain is constructed.

7. The dynamic index generation method driven by business form fields as described in claim 6, characterized in that, The construction process of the second cross-layer driver composite connection includes: Based on the set of field association logical relationship operators, combined with the entity-relationship extraction algorithm, we obtain the operator-combination-field node information set; Based on the operator-combination-field node information set, the association matching algorithm is used to match the associated field IDs of the field association logical relationship operator and the list of associated field IDs of the field query combination dependency relationship with the IDs of the valid field nodes in the field-driven node layer, and the valid field nodes corresponding to the operator and combination dependency are filtered out to obtain the operator-combination-valid field node matching set.

8. The dynamic index generation method driven by business form fields as described in claim 7, characterized in that, The construction process of the second cross-layer driver composite connection also includes: Based on the matching set of operator-combination-effective field nodes, the fitness algorithm is used to evaluate the fitness of each operator-combination with the corresponding effective field node, taking the fitness of the operator type and the data type of the effective field node, and the consistency of the combinational logic structure with the vertical query dependency chain and the horizontal query causal chain as evaluation targets. The fitness of each operator-combination with the corresponding effective field node is calculated, and the operator-combination-effective field node combination with the fitness greater than or equal to the preset fitness threshold is retained to obtain the qualified operator-combination-field node set. Based on the set of qualified operators, combinations, and field nodes, and according to the combination logic structure of field query combination dependencies, the corresponding valid field nodes are connected through the associated field association logic operators to obtain a combination connection containing valid field nodes, association operators, and combination logic. At the same time, the vertical dependency identifier or horizontal causal identifier corresponding to each combination connection is marked to obtain the second cross-layer driven combination connection.

9. The dynamic index generation method driven by business form fields as described in claim 8, characterized in that, The construction process of the third cross-layer driven index join includes: Based on the target index layer, query operator mapping set, and field-driven node layer, we extract the index type, index-related field identifier, operator type, operator-field mapping relationship, effective field node identifier, and historical query frequency to obtain the index-operator-field hierarchical information set. Based on the index-operator-field hierarchical information set, the operator-associated field identifier is matched with the valid field node identifier through the field identifier matching algorithm, and the operator type is matched with the index-adapted operation type through the matching algorithm to obtain the index-operator-valid field matching set; Based on the index-operator-valid field matching set, the matching degree between the operator and the field data type, the matching degree between the operator and the index type, and the priority score based on the historical query frequency are calculated by a multi-dimensional matching degree evaluation algorithm. The combination that reaches the preset threshold of comprehensive matching degree is retained to obtain the qualified index-operator-field set. Based on the qualified index-operator-field set, the connection unit construction algorithm establishes the association connection between the effective field node, the query operator and the target index node according to the mapping relationship between the operator and the field, and obtains the basic index connection unit set marked with historical query frequency weight and adaptation status identifier; Based on the basic index connection unit set, the structural compatibility between the connection unit and the field-driven node layer and the target index layer is verified by a hierarchical compatibility verification algorithm. Connection units that have logical conflicts or destroy the hierarchical structure are eliminated to obtain a compatible index connection unit set. Based on the set of compatible index connection units, they are sorted according to the product of historical query frequency weight and fit score. Based on the sorted set of compatible index connection units, the query configuration node layer and the target index layer are bound according to the mapping relationship between operators and fields, thus completing the third cross-layer driven index connection.

10. A dynamic index generation system driven by business form fields, used to implement the dynamic index generation method driven by business form fields as described in any one of claims 1-9, characterized in that, include: Configuration tree module, parsing module, index building module, and exception handling module; The configuration tree module obtains the business form query configuration tree based on the query configuration items of form fields in the business form configuration subsystem, combined with graph algorithms and tree databases. The parsing module is used to obtain the real-time configuration change operations of the query configuration items of the form fields in the business form configuration subsystem. Combined with the optimal traversal strategy, with the goal of minimizing change constraints and maximizing fit, it obtains the minimum effective field adjustment tree. The index building module adjusts the tree based on the minimum effective fields and obtains the database index creation or adjustment statement through the index strategy engine; The exception adjustment module is used to automatically execute the database index creation or adjustment statements to adjust the business form query configuration tree and respond to the real-time query requirements of newly added business forms. When any query requirement is abnormal, it locates the real-time query requirement of the business form and the corresponding updated query index; it feeds the real-time query requirement of the business form and the corresponding updated query index back to the optimization traversal strategy for real-time updates until all query requirements are met.

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