A method and system for constructing a database of structural components manufactured by machine tools

Abstract

The application discloses a kind of structural member database construction method and system of machine tool manufacturing, it is related to machine tool manufacturing and database technical field, the application is by obtaining the multidimensional data of machine tool structural member, and carries out data cleaning, missing completion and normalization processing;Build spatial correlation topological network, establish parameter node directed connection relationship, give connection weight, divide strong connection edge and weak connection edge, and calculate node topological hierarchy;Generate dynamic constraint logic, set parameter and boundary condition, form constraint rule script;Using data hierarchical storage and data sharding management, build consistent hash index and hierarchical multidimensional index structure;Carry out consistency check, determine parameter modification validity based on constraint deviation degree, adaptively update correlation topological network and connection weight;Effectively solve the problem that structural member database cannot effectively establish and maintain the correlation constraint between the multidimensional parameters of structural member;Significantly improve the query efficiency and data accuracy of database.

Description

Technical Field

[0001] This invention relates to the fields of machine tool manufacturing and database technology, specifically to a method and system for constructing a database of structural components for machine tool manufacturing. Background Technology

[0002] In the field of machine tool manufacturing, structural components are the foundation for ensuring the accuracy and stability of machine tools. Their design and manufacturing involve multi-dimensional parameters such as geometric features, material properties, and working environment, and there are complex physical coupling relationships between these parameters. Building a high-quality structural component database is a key link in realizing digital design and intelligent manufacturing of machine tools.

[0003] Existing technologies typically use traditional relational databases or product data management systems to store and manage structural component data, focusing on static archiving and querying of data. They treat multidimensional parameters as isolated data items or only establish simple table relationships, making it difficult to reflect the deep physical dependencies and logical constraints such as the influence of temperature field changes on geometric thermal deformation and material property changes on mechanical transmission. Such architectures cannot establish cross-dimensional, non-linear correlation constraint mechanisms, and it is difficult to construct dynamic topological networks between parameters to quantify the mutual influence weights.

[0004] Furthermore, in the entire lifecycle maintenance, when a key parameter changes, the existing technology lacks dynamic constraint logic support, cannot automatically propagate the impact of the change to related parameters, and is also difficult to perform consistency verification in a timely manner, which leads to the imbalance of multi-dimensional parameters, resulting in data silos or contradictory data that violate physical laws, seriously affecting the efficiency of design reuse and manufacturing accuracy.

[0005] Therefore, there is an urgent need for a database construction method that can effectively establish and maintain multi-dimensional parameter association constraints in order to solve the problems of weak data association and poor consistency in existing technologies.

[0006] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0007] The purpose of this invention is to solve the problem that structural component databases cannot effectively establish and maintain the correlation constraints between multi-dimensional parameters of structural components, and to propose a method and system for constructing a structural component database for machine tool manufacturing.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] A method for constructing a database of structural components for machine tool manufacturing includes:

[0010] S1. Multidimensional parameter acquisition: Collect geometric features of machine tool structural components, physical properties of materials, and working environment data, and perform data cleaning, missing data completion and normalization to form a standard parameter sequence for the machine tool.

[0011] S2. Construction of related topology: Parameter nodes are constructed using the machine tool standard parameter sequence, directed connection edges are established based on physical mapping rules, connection weights are assigned, strong connection edges and weak connection edges are divided to form a spatial related topology network.

[0012] S3. Constraint Logic Generation: Based on the spatially related topological network, select strongly connected parameter node pairs, calculate coupling coefficients, set constraint boundary conditions, encapsulate constraint rule scripts, and establish mapping indexes.

[0013] S4. Database storage and indexing: Encapsulate and store machine tool structural component data and corresponding associated topology networks and dynamic constraint logic, and construct a multi-dimensional index structure;

[0014] S5. Consistency Verification and Maintenance: Respond to data modification operation commands, perform consistency verification based on dynamic constraint logic, and update the associated topology network and connection weights.

[0015] Furthermore, the specific operation steps of S2 are as follows:

[0016] Each independent parameter in the standard parameter sequence of the machine tool is defined as a parameter node in the spatial association topology network. Combining the assembly and matching relationship of machine tool structural parts, positioning constraint relationship, and force transmission path, the interaction logic between parameter nodes is analyzed through preset physical mapping rules, the direction of parameter change transmission and the dependency order are determined, and a directed connection edge is established from the active driving parameter node to the passive response parameter node.

[0017] Calculate the weight value of the directed connection edge, and mark the directed connection edge as a strong connection edge or a weak connection edge based on the comparison result of the weight value and the preset threshold.

[0018] An adjacency matrix is ​​constructed based on directed edges. Starting from the root node with no incoming edges, a level traversal algorithm is used to obtain the topological level of each parameter node.

[0019] A spatially related topological network is formed based on the node topological hierarchy. The spatially related topological network records the connection direction, influence strength, and dependency depth between parameter nodes.

[0020] Furthermore, the specific operation steps of S3 include:

[0021] Traverse the parameter nodes within the spatially associated topology network, filter the parameter node pairs marked as strong connections, and set the strong connection parameter node pairs as the basic functional units of the dynamic constraint logic; strong connection parameter node pairs include geometry-thermal coupling pairs, stiffness-deformation coupling pairs, and load-response coupling pairs.

[0022] For different types of strongly connected parameter node pairs, define the coupling coefficients for different types of coupling pairs.

[0023] Based on the coupling coefficient, and combined with the design specifications and safety margins of machine tool structural components, three types of constraint boundary conditions are set for each group of strongly connected parameter nodes: the allowable variation range of the driving parameter nodes, the allowable variation range of the response parameter nodes, and the effective interval of the coupling coefficient.

[0024] Perform constraint boundary judgment on the parameter node change value. After the constraint boundary verification is passed, calculate the controlled response value of the associated parameter.

[0025] Furthermore, the specific operation steps of S3 also include:

[0026] The controlled response value calculation logic, coupling coefficient calculation method, and constraint boundary conditions are encapsulated into an executable constraint rule script. The constraint rule script is written using the database's built-in stored procedure language. The constraint rule script includes fields for rule identifier, driving parameter identifier, response parameter identifier, coupling coefficient type, boundary conditions, and response calculation formula.

[0027] All constraint rule scripts are stored in the database rule layer, and a mapping index is established between the constraint rule scripts and the corresponding strong connection edges in the spatially associated topological network, forming a callable and associative parameter constraint rule system.

[0028] Furthermore, the specific operation steps of S4 are as follows:

[0029] The original parameter data of machine tool structural components, spatial association topology network, and dynamic constraint logic are encapsulated into a unified data object. A hierarchical storage architecture of data layer, topology layer, and rule layer is adopted, and each layer is associated through node identifiers.

[0030] Data is divided into segments based on the functional attributes of machine tool structural components. Segment identifiers are assigned to data segments and their physical storage locations are recorded. Feature vectors of structural component parameters within data segments are extracted. A consistent hashing algorithm is used to perform hash operations on the feature vectors, mapping hash key values ​​to corresponding positions on the hash ring. A mapping relationship between hash key values ​​and recorded physical addresses is established, forming a parameter hash index table.

[0031] Calculate index priority, adjust the storage medium location of data shards based on index priority, load a preset number of high-priority data shards into the memory buffer, store the remaining data shards on disk and sort them by priority, periodically adjust the data shards in the memory buffer, and build a hierarchical multidimensional index structure.

[0032] After completing the hierarchical multidimensional index structure, for user query requests, the query conditions are parsed and the corresponding data shards are located. Based on the query type, index queries or topology traversal queries are executed.

[0033] Furthermore, the specific operation steps of S5 are as follows:

[0034] When the database receives a modification instruction for the target parameter node, it triggers the constraint logic engine to traverse the first-level associated nodes directly connected to the target parameter node. The first-level associated nodes include successor nodes with the target parameter node as the driving parameter and predecessor nodes with the target parameter node as the response parameter.

[0035] Retrieve the corresponding constraint rule scripts from the database rule layer, calculate the theoretical expected value of the first-level associated nodes, and perform constraint deviation calculation based on the theoretical expected value and the actual input value of each first-level associated node;

[0036] When the constraint deviation is less than the preset threshold, update the database data layer parameters and the connection weights of the topology network nodes associated with the topology layer.

[0037] When the constraint deviation is greater than or equal to the preset threshold, a conflict report is generated and the target parameter node modification operation is rolled back.

[0038] Real-time recording of parameter modification logs and data query logs; statistical analysis of parameter node call dependencies based on log data; correction of directed connection edge weights; adjustment of connection edge types; recalculation of parameter node topology levels; and updating of topology layer adjacency matrix and node connection relationships.

[0039] A second aspect of the present invention provides a database construction system for structural components in machine tool manufacturing, comprising:

[0040] Multi-dimensional parameter acquisition module: It acquires the geometric features, material physical properties and working environment data of machine tool structural parts through the data acquisition interface, and preprocesses the acquired data to generate a standard parameter sequence for the machine tool;

[0041] The associated topology construction module defines parameter nodes based on the standard parameter sequence of machine tools, establishes directed connection edges through physical mapping rules, calculates connection weights, marks strong and weak connection edges, calculates the node topology hierarchy, and forms a spatial associated topology network.

[0042] Constraint logic generation module: Based on the spatially related topology network, extract the coupling features between parameter nodes to generate dynamic constraint logic;

[0043] Database storage and indexing module: Stores data, topology, and rules in layers, divides data into shards according to functional attributes, and builds consistent hash indexes and hierarchical multidimensional indexes;

[0044] Consistency verification and maintenance module: Traverses the first-level associated nodes of the node whose target parameters are modified, calculates the constraint deviation, performs parameter updates or operation rollbacks, corrects connection weights, and updates and optimizes the associated topology network.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] This invention achieves standardized processing of geometric features, material physical properties, and operating environment data of machine tool structural components through multi-dimensional parameter acquisition. It establishes directed connections between parameter nodes through topology construction, distinguishing connection strength and topology hierarchy. It generates coupled constraint rules and boundary conditions through constraint logic. It implements hierarchical and fragmented data management and consistent hash index construction through database storage and indexing. Finally, it completes parameter modification verification and adaptive topology network updates through consistency verification and maintenance. This significantly improves the integration capability of multi-source heterogeneous data for machine tool structural components, the ability to express parameter relationships, the execution capability of constraint rules, the operational efficiency of data storage and querying, and the stability of database operation. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example:

[0050] like Figure 1 As shown, a method for constructing a structural component database for machine tool manufacturing includes multi-dimensional parameter acquisition, association topology construction, constraint logic generation, database storage and indexing, and consistency verification and maintenance.

[0051] S1. Multidimensional parameter acquisition:

[0052] Through the data acquisition interfaces pre-installed on the machine tool design terminal, testing equipment, and operating condition monitoring sensors, three types of core data are collected simultaneously: geometric feature data of machine tool structural components, material physical property data, and operating environment data. Among them, the geometric feature data comes from the structural component model file exported by 3D modeling software and the actual machining dimension data output by online testing equipment; the material physical property data is retrieved from the standard material performance database; and the operating environment data includes temperature, vibration, and load data during the machining process.

[0053] The collected multi-source heterogeneous data is preprocessed in a unified manner to obtain the three-dimensional model file of the machine tool structural component. The model is then meshed and feature-recognized using a geometric analytical algorithm to extract the key dimensional parameters of the structural component, including length, width, height, hole diameter, and hole position coordinates, forming the original geometric parameter sequence. At the same time, the material density, elastic modulus, coefficient of thermal expansion, and thermal conductivity of the corresponding structural component are matched and retrieved from the material property database to construct a set of physical properties.

[0054] Based on the preprocessing, the size deviation factor is used. The accuracy of the geometric parameters is evaluated using the following formula: ,in, These represent the actual measured values ​​of the structural component in the length, width, and height directions at the current sampling time. These represent the design reference values ​​corresponding to the length, width, and height of the structural component, respectively.

[0055] Based on the size deviation factor and combined with the numerical distribution range of each parameter in the material physical property set, a corresponding data anomaly detection threshold is set.

[0056] Based on preset data cleaning rules, discrete outliers in the collected data that exceed the anomaly detection threshold are identified and removed, and linear interpolation is used to complete the values ​​of missing parameters.

[0057] After cleaning and completion, the geometric parameters, material physical property parameters, and working environment parameters are uniformly mapped to the preset standard parameter coordinate system. The parameters of different dimension types are normalized to achieve coordinate alignment and numerical standardization of multi-source parameters, forming a machine tool standard parameter sequence.

[0058] S2, Association Topology Construction:

[0059] Based on the standard parameter sequence of machine tools, each independent parameter in the sequence is defined as a parameter node in a spatially related topology network, including geometric dimension nodes, material physical property nodes, and working environment nodes. Combining the actual assembly and fit relationships, positioning constraints, and force transmission paths of machine tool structural components, the interaction logic between different types of parameter nodes is analyzed based on preset physical mapping rules. These physical mapping rules include:

[0060] Dimension chain transfer rules: For example, when the diameter of a hole increases, the allowable deviation range of the diameter of the mating shaft is adjusted accordingly, and this is transferred through the limit deviation formula;

[0061] Thermo-mechanical coupling deformation rule: For example, temperature changes cause thermal expansion of materials, resulting in changes in geometric dimensions;

[0062] Control command constraint rules; for example, there is an empirical relationship between the feed rate and cutting load of CNC equipment;

[0063] Based on the physical mapping rules, the direction of parameter change propagation and the order of dependency are determined, thereby establishing directional directed connections between nodes. The direction of propagation is used to characterize the causal order of the impact of a parameter change on related parameters, and the order of dependency is used to characterize the hierarchical relationship based on the determination of the values ​​of related parameters. The directed connection points from the active driving parameter node to the passive response parameter node, which is used to characterize the driving and influence relationship of one parameter node on another parameter node, intuitively reflecting the propagation driving relationship and the direction of influence transmission between parameters.

[0064] After establishing directed edges, each directed edge is assigned a quantized weight to characterize the driving strength:

[0065] For the node Pointing to node The directed connecting edges, through the formula Calculations are performed to obtain nodes Pointing to node Weight values ​​of directed connection edges ,in, Represents a node For nodes The partial derivatives, This indicates that in the machine tool standard parameter sequence, the node... The standard deviation of all sampled values In the standard parameter sequence of machine tools, nodes are represented. Standard deviation of all sampled values;

[0066] Based on weight values Construct an adjacency matrix, where the matrix elements are... This indicates the existence of a slave node. Pointing to node A directed connection edge, otherwise ;

[0067] If the weight value If the value is greater than or equal to a preset threshold, the current directed connection edge is marked as a strong connection edge.

[0068] If the weight value If the value is less than a preset threshold but greater than zero, the current directed connection edge is marked as a weak connection edge.

[0069] Based on the directed edges in the adjacency matrix, starting from the root node with no incoming edges (i.e., an independent parameter not driven by any other parameters), a level-order traversal algorithm is used to obtain the topological level of each parameter node: node Topology hierarchy ,in, Represents a node The topological hierarchy value of the predecessor node. This indicates the operation of retrieving the maximum value;

[0070] The numerical value of the topology level of a parameter node directly represents the quantified value of the node's dependency depth in the network; the root node has the smallest topology level value, indicating that the root node does not depend on any other parameters; the larger the topology level value, the deeper the parameter node is in the network, and the more upstream parameters drive its value.

[0071] The computation based on the node topology hierarchy forms a spatially related topological network with a clear hierarchical structure; the spatially related topological network also records the connection direction, influence strength and dependency depth between parameter nodes.

[0072] S3, Constraint Logic Generation:

[0073] Based on the spatially related topology network constructed by S2, the parameter nodes within the network are traversed and filtered to identify all parameter node pairs marked as strongly connected edges. Each strongly connected parameter node pair is defined as the basic functional unit of the dynamic constraint logic. The strongly connected parameter node pairs include geometric-thermal coupling pairs between geometric dimension nodes and thermally related nodes, stiffness-deformation coupling pairs between material physical property nodes and geometric dimension nodes, and load-response coupling pairs between working condition nodes and geometric dimension nodes.

[0074] Define corresponding coupling coefficients for different types of strongly connected parameter node pairs. The specific calculation method is as follows:

[0075] When the parameter node pair is a geometry-thermal coupling pair, it is obtained through the formula The geometry-thermal coupling coefficient is calculated. ,in, Indicates the relative rate of change of geometric dimensions. This represents the change in geometric dimensions, expressed as the absolute value of the difference between the current geometric parameter value and the corresponding design reference value. Indicates the maximum permissible geometric deformation. Indicates the relative rate of change of temperature. This represents the change in temperature, expressed as the absolute value of the difference between the current operating temperature and the standard ambient temperature. Indicates the extreme temperature difference under operating conditions. These are the weighting factors for the relative rates of change of geometric dimensions and the relative rates of change of temperature, respectively, satisfying... ;

[0076] When the parameter node pair is a stiffness-deformation coupled pair, it is obtained through the formula The stiffness-deformation coupling coefficient is calculated. ,in, Indicates the elastic modulus. This represents the actual strain, that is, the relative deformation of a material under stress. This indicates the maximum stress a material is allowed to withstand under safe design conditions;

[0077] When the parameter node pair is a load-response coupling pair, it is obtained through the formula The load-response coupling coefficient is calculated. ,in, Indicates the actual load. This indicates the maximum load value that the structural component design or materials are allowed to withstand. This represents the actual deformation of the structural component under actual load. Indicates the maximum allowable deformation;

[0078] Based on the coupling coefficient, and combined with the design specifications and safety margins of the machine tool structural components, constraint boundary conditions are set for each set of strongly connected parameter nodes with respect to changes in the set parameters. These constraint boundary conditions include:

[0079] Drive parameter node Permissible range of variation ;

[0080] Response parameter node Permissible range of variation ;

[0081] The effective range of the coupling coefficient is limited to: ,in, ;

[0082] in, Indicates the driving parameter node The lower limit of physically permissible values and upper limit The closed interval formed is pre-set based on the design specifications of machine tool structural components, material limits, safety margins, or machining process constraints; Represents response parameter nodes The lower limit of physically permissible values and upper limit The closed interval formed is pre-set based on the design specifications, assembly requirements or performance indicators of the machine tool structural components; This represents the upper limit threshold of the coupling coefficient, i.e., the maximum allowable proportion of coupling transmission;

[0083] When any parameter node changes, the changed value of the parameter node is constrained: if it exceeds its own constraint boundary, the modification is rejected and an out-of-bounds report is generated; if it does not exceed its own constraint boundary, the boundary check is considered passed.

[0084] For parameter changes that pass the boundary constraint test, calculate the controlled response value of the corresponding associated parameter using the following formula: ,in, This indicates the response parameter nodes after calculation through coupling constraints. Theoretically controlled update value, This indicates a response parameter node that is strongly correlated with the driving parameters. The original value, Indicates the driving parameter node In the modified update values Indicates the driving parameter node The original values ​​before modification This represents the coupling coefficient, which is selected based on the type of coupling pair: geometric-thermal coupling coefficient, stiffness-deformation coupling coefficient, or load-response coupling coefficient.

[0085] The calculation logic of the controlled response value, the calculation formula of the coupling coefficient, and the constraint boundary conditions are encapsulated into an executable constraint rule script. The constraint rule script is written in the database's built-in stored procedure language. Each rule includes the following fields: rule identifier, driving parameter identifier, response parameter identifier, coupling coefficient type, boundary conditions, and response calculation formula.

[0086] All constraint rule scripts are stored in the rule layer of the database, and a mapping index is established between the rules and the corresponding strong connection edges in the spatial association topology network in step S2, forming a callable and associative parameter constraint rule system.

[0087] S4. Database Storage and Indexing:

[0088] Data encapsulation and hierarchical storage:

[0089] The original parameter data, spatial correlation topology network, and dynamic constraint logic of machine tool structural components are encapsulated into a unified data object; the data object adopts a hierarchical storage architecture.

[0090] Data layer: Stores standard parameter sequences of geometric feature parameters, material physical property parameters, and working environment parameters of structural components. Each record is accompanied by a timestamp and version number.

[0091] Topology layer: Stores the adjacency matrix generated by S2, the node topology hierarchy, and the strength of each connecting edge;

[0092] Rule layer: Stores the constraint rule scripts generated by S3 and their mapping indexes to the strongly connected edges in the topology layer;

[0093] Each layer is associated with another layer through node identifiers to ensure consistency of data, topology, and constraint logic;

[0094] Data sharding:

[0095] Based on the functional attributes of machine tool structural components, the database is divided into several data shards; each shard corresponds to an index node, and the shard stores complete data objects of all structural components under the same functional category; the data sharding rules are based on the functional classification standards in the machine tool design specifications, which makes the structural component parameters within the same shard have strong cohesion and different shards have weak coupling; each data shard is assigned a unique shard identifier, and the physical location of each data shard is recorded.

[0096] Construction of the parameter hash index table:

[0097] For each data segment, feature vectors of all structural component parameters within the data segment are extracted. The feature vectors are composed of key parameter combinations, including structural component type code, geometric dimensions, material category code, and typical working condition identifier.

[0098] Based on feature vectors, a parameter hash index table is constructed using the consistent hashing algorithm, as follows:

[0099] Perform a hash operation on the feature vector of each structural component to obtain a hash key value;

[0100] Map hash key values ​​to corresponding positions on the hash ring to establish a direct mapping from hash key values ​​to the physical addresses of records within data fragments;

[0101] The hash index table is stored in memory to support single-point queries with constant time complexity;

[0102] Index priority calculation and dynamic adjustment:

[0103] For each data partition, the formula is used. Calculate the index priority ,in, This indicates the historical access frequency of the current data shard. This indicates the data storage capacity of the current data shard. This represents the total storage capacity of the database. Indicates storage utilization. These represent the weighting coefficients for historical access frequency and storage utilization, respectively.

[0104] Based on index priority, the physical location of data shards in the storage medium is dynamically adjusted to construct a hierarchical multidimensional index structure, as follows:

[0105] Prioritize the highest priority Each data slice is loaded into the memory buffer and kept in memory as hot data;

[0106] The remaining data is stored in disk fragments, arranged in descending order of priority, with higher priority nodes stored in the fast access area of ​​the disk;

[0107] By setting a fixed interval, the access frequency and index priority of each shard are recalculated, and the shard set in the memory buffer is adjusted based on the calculation results to achieve automatic migration of hot and cold data.

[0108] Execution path of multidimensional queries:

[0109] After completing the hierarchical multidimensional index structure construction, user query requests are executed according to the following path:

[0110] a. Analyze the query conditions, extract the functional attribute categories involved in the query, and locate the corresponding data slices;

[0111] b. If the query contains exact parameter matches, the record address is obtained directly through the parameter hash index table;

[0112] c. If the query involves parameter range or topological association, load the spatial association topology network corresponding to the data shard, and perform graph traversal based on the adjacency matrix and topological hierarchy;

[0113] d. If the query requires obtaining constraint validation results, the corresponding constraint rule script in the rule layer is called.

[0114] S5. Consistency Verification and Maintenance:

[0115] Using the dynamic constraint logic generated by S3 as the judgment basis and the spatial association topology network constructed by S2 as the basis for association retrieval, when the database receives a request for any target parameter node... When a modification instruction is executed, the constraint logic engine is triggered to perform a consistency check:

[0116] Access the spatial relational topology network constructed by S2 through the constraint logic engine, and traverse the nodes related to the target parameters. All directly connected first-level related nodes; first-level related nodes include:

[0117] by For all successor nodes of the driving parameter, i.e., there exists a directed connection edge. parameter nodes ;

[0118] by For all predecessor nodes of the response parameters, i.e., there exist directed connection edges. parameter nodes ;

[0119] For each first-level associated node, the corresponding constraint rule script is retrieved from the database rule layer to calculate the theoretical expected value of each associated node. The specific calculation method is based on the topological relationship between the associated node and the target parameter node and is divided into the following two cases:

[0120] For any associated parameter node ,like For target parameter nodes The subsequent nodes directly call the constraint rule script, and obtain the controlled response value based on the formula. Theoretical correction value;

[0121] For any associated parameter node ,like For target parameter nodes The predecessor node then applies dynamic constraint logic in reverse, using the target parameter node. Using the new value as a known quantity, derive... The allowed range of values ​​is defined, and the median value of that allowed range is taken as the median value. Theoretical compatibility value;

[0122] The dynamic constraint logic consists of the calculation logic for the controlled response value, the calculation formula for the coupling coefficient, and the constraint boundary conditions;

[0123] By using the specific calculation method of theoretical expected value, the theoretical expected value of each first-level associated node under the constraint logic is obtained respectively;

[0124] Based on the theoretical expected value and the actual input values ​​of each first-level related node, the formula is used. The constraint deviation is calculated, where, This represents the total number of first-level related nodes. Indicates the first The actual input value of each first-level related node is used. If the user does not input the actual value of the first-level related node, the current stored value of the current parameter node in the database is used. The first term obtained based on dynamic constraint logic calculation is represented as... The theoretical expected value of each first-level related node, This indicates a very small positive number that prevents the denominator from being zero;

[0125] If the constraint deviation is less than the preset threshold, the target parameter modification operation is deemed valid, and the target parameter and the parameter values ​​of each associated node in the database data layer are updated synchronously. At the same time, the node connection weights of the associated topology network in the topology layer are updated.

[0126] If the constraint deviation is greater than or equal to the preset threshold, it is determined that there is a parameter conflict in this modification operation. A conflict report is immediately generated, recording the related parameter links of the conflict, the constraint deviation value, and the deviation between the theoretical and actual values. At the same time, the target parameter modification operation is rolled back to restore the parameter state before the modification.

[0127] During the daily operation of the database, the dynamic updates and maintenance of the associated topology network are performed synchronously, and all parameter modification logs and data query logs are recorded in real time. Every preset statistical period, the actual call dependencies between each parameter node are statistically analyzed based on the log data.

[0128] Based on the statistical analysis results, using the formula The weights of directed connections are dynamically adjusted, whereby... This indicates the parameter node after correction. Pointer to parameter node The weight values ​​of the directed connection edges. Indicates the parameter node before correction. Pointer to parameter node The weight values ​​of the directed connection edges. This represents the preset learning rate factor. This indicates the parameter nodes within the current statistical period. With parameter nodes The number of times it is accessed simultaneously. This indicates the total number of accesses to all parameter nodes in the database during the current statistical period.

[0129] If the corrected weight value is less than the preset threshold and the current connection edge is a weak connection edge, then the current directed connection edge is removed from the adjacency matrix and the corresponding parameter node connection is broken.

[0130] If the corrected weight value is greater than or equal to the preset threshold, and the current connection edge is a weak connection edge, then the current directed connection edge will be upgraded to a strong connection edge, and the S3 process will be triggered to generate the corresponding constraint rule script.

[0131] Simultaneously, based on all the corrected weight values, the hierarchical position of each parameter node in the associated topology network is recalculated, the adjacency matrix and connection relationships in the topology layer are updated, and adaptive optimization of the associated topology network is achieved.

[0132] A database construction system for structural components in machine tool manufacturing, comprising:

[0133] Multi-dimensional parameter acquisition module: By collecting geometric feature data of machine tool structural components, material physical property data, and working environment data, the module performs preprocessing, abnormal data removal, missing data completion, and dimension normalization on the collected data to form a standard parameter sequence for the machine tool.

[0134] The associated topology construction module defines independent parameters in the machine tool standard parameter sequence as parameter nodes. Combining the assembly and mating relationships of machine tool structural components, positioning constraints, and force transmission paths, it establishes directed connection edges through physical mapping rules, calculates the weights of directed connection edges, marks strong and weak connection edges, and uses a hierarchical traversal algorithm to obtain the topology hierarchy of parameter nodes, forming a spatial associated topology network.

[0135] The constraint logic generation module traverses the parameter nodes in the spatially associated topology network, filters strongly connected parameter node pairs, constructs coupling constraint relationships, sets the allowable range of change for driving parameter nodes, the allowable range of change for response parameter nodes, and the effective range of coupling coefficients, encapsulates constraint rule scripts, and establishes a mapping index between constraint rule scripts and strongly connected edges.

[0136] Database storage and indexing module: Data, topology and rules are stored in layers. Data is divided into data shards based on the functional attributes of machine tool structural components. A consistent hashing algorithm is used to build a parameter hash index table, calculate the index priority, adjust the storage location of data shards, and build a hierarchical multidimensional index structure.

[0137] Consistency verification and maintenance module: Receives parameter modification instructions, traverses first-level associated nodes, calculates constraint deviation, performs parameter updates or modification rollbacks based on constraint deviation, corrects directed connection edge weights based on runtime logs, adjusts connection edge types, and updates the topology layer adjacency matrix and node connection relationships.

[0138] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for constructing a database of structural components for machine tool manufacturing, characterized in that, include: S1. Multidimensional parameter acquisition: Collect geometric features of machine tool structural components, physical properties of materials, and working environment data, and perform data cleaning, missing data completion and normalization to form a standard parameter sequence for the machine tool. S2. Construction of related topology: Parameter nodes are constructed using the machine tool standard parameter sequence, directed connection edges are established based on physical mapping rules, connection weights are assigned, strong connection edges and weak connection edges are divided to form a spatial related topology network. S3. Constraint Logic Generation: Based on the spatially related topological network, select strongly connected parameter node pairs, calculate coupling coefficients, set constraint boundary conditions, encapsulate constraint rule scripts, and establish mapping indexes. S4. Database storage and indexing: Encapsulate and store machine tool structural component data and corresponding associated topology networks and dynamic constraint logic, and construct a multi-dimensional index structure; S5. Consistency Verification and Maintenance: Respond to data modification operation commands, perform consistency verification based on dynamic constraint logic, and update the associated topology network and connection weights.

2. The method for constructing a structural component database for machine tool manufacturing according to claim 1, characterized in that, The specific operation steps of S2 are as follows: Each independent parameter in the standard parameter sequence of the machine tool is defined as a parameter node in the spatial association topology network. Combining the assembly and matching relationship of machine tool structural parts, positioning constraint relationship, and force transmission path, the interaction logic between parameter nodes is analyzed through preset physical mapping rules, the direction of parameter change transmission and the dependency order are determined, and a directed connection edge is established from the active driving parameter node to the passive response parameter node. Calculate the weight value of the directed connection edge, and mark the directed connection edge as a strong connection edge or a weak connection edge based on the comparison result of the weight value and the preset threshold. An adjacency matrix is ​​constructed based on directed edges. Starting from the root node with no incoming edges, a level traversal algorithm is used to obtain the topological level of each parameter node. A spatially related topological network is formed based on the node topological hierarchy. The spatially related topological network records the connection direction, influence strength, and dependency depth between parameter nodes.

3. The method for constructing a structural component database for machine tool manufacturing according to claim 1, characterized in that, The specific operation steps of S3 include: Traverse the parameter nodes within the spatially associated topology network, filter the parameter node pairs marked as strong connections, and set the strong connection parameter node pairs as the basic functional units of the dynamic constraint logic; strong connection parameter node pairs include geometry-thermal coupling pairs, stiffness-deformation coupling pairs, and load-response coupling pairs. For different types of strongly connected parameter node pairs, define the coupling coefficients for different types of coupling pairs; Based on the coupling coefficient, and combined with the design specifications and safety margins of machine tool structural components, three types of constraint boundary conditions are set for each group of strongly connected parameter nodes: the allowable variation range of the driving parameter nodes, the allowable variation range of the response parameter nodes, and the effective interval of the coupling coefficient. Perform constraint boundary judgment on the parameter node change value. After the constraint boundary verification is passed, calculate the controlled response value of the associated parameter.

4. The method for constructing a structural component database for machine tool manufacturing according to claim 3, characterized in that, The specific operation steps of S3 also include: The controlled response value calculation logic, coupling coefficient calculation method, and constraint boundary conditions are encapsulated into an executable constraint rule script. The constraint rule script is written using the database's built-in stored procedure language. The constraint rule script includes fields for rule identifier, driving parameter identifier, response parameter identifier, coupling coefficient type, boundary conditions, and response calculation formula. All constraint rule scripts are stored in the database rule layer, and a mapping index is established between the constraint rule scripts and the corresponding strong connection edges in the spatially associated topological network, forming a callable and associative parameter constraint rule system.

5. The method for constructing a structural component database for machine tool manufacturing according to claim 1, characterized in that, The specific operation steps of S4 are as follows: The original parameter data of machine tool structural components, spatial association topology network, and dynamic constraint logic are encapsulated into a unified data object. A hierarchical storage architecture of data layer, topology layer, and rule layer is adopted, and each layer is associated through node identifiers. Data is divided into segments based on the functional attributes of machine tool structural components. Segment identifiers are assigned to data segments and their physical storage locations are recorded. Feature vectors of structural component parameters within data segments are extracted. A consistent hashing algorithm is used to perform hash operations on the feature vectors, mapping hash key values ​​to corresponding positions on the hash ring. A mapping relationship between hash key values ​​and recorded physical addresses is established, forming a parameter hash index table. Calculate index priority, adjust the storage medium location of data shards based on index priority, load a preset number of high-priority data shards into the memory buffer, store the remaining data shards on disk and sort them by priority, periodically adjust the data shards in the memory buffer, and build a hierarchical multidimensional index structure. After completing the hierarchical multidimensional index structure, for user query requests, the query conditions are parsed and the corresponding data shards are located. Based on the query type, index queries or topology traversal queries are executed.

6. The method for constructing a structural component database for machine tool manufacturing according to claim 1, characterized in that, The specific operation steps of S5 are as follows: When the database receives a modification instruction for the target parameter node, it triggers the constraint logic engine to traverse the first-level associated nodes directly connected to the target parameter node. The first-level associated nodes include successor nodes with the target parameter node as the driving parameter and predecessor nodes with the target parameter node as the response parameter. Retrieve the corresponding constraint rule scripts from the database rule layer, calculate the theoretical expected value of the first-level associated nodes, and perform constraint deviation calculation based on the theoretical expected value and the actual input value of each first-level associated node; When the constraint deviation is less than the preset threshold, update the database data layer parameters and the connection weights of the topology network nodes associated with the topology layer. When the constraint deviation is greater than or equal to the preset threshold, a conflict report is generated and the target parameter node modification operation is rolled back. Real-time recording of parameter modification logs and data query logs; statistical analysis of parameter node call dependencies based on log data; correction of directed connection edge weights; adjustment of connection edge types; recalculation of parameter node topology levels; and updating of topology layer adjacency matrix and node connection relationships.

7. A system applied to the method for constructing a structural component database for machine tool manufacturing as described in any one of claims 1-6, comprising: Multi-dimensional parameter acquisition module: It acquires the geometric features, material physical properties and working environment data of machine tool structural parts through the data acquisition interface, and preprocesses the acquired data to generate a standard parameter sequence for the machine tool; The associated topology construction module defines parameter nodes based on the standard parameter sequence of machine tools, establishes directed connection edges through physical mapping rules, calculates connection weights, marks strong and weak connection edges, calculates the node topology hierarchy, and forms a spatial associated topology network. Constraint logic generation module: Based on the spatially related topology network, extract the coupling features between parameter nodes to generate dynamic constraint logic; Database storage and indexing module: Stores data, topology, and rules in layers, divides data into shards according to functional attributes, and builds consistent hash indexes and hierarchical multidimensional indexes; Consistency verification and maintenance module: Traverses the first-level associated nodes of the node whose target parameters are modified, calculates the constraint deviation, performs parameter updates or operation rollbacks, corrects connection weights, and updates and optimizes the associated topology network.

Images