Design method and device for dynamic and static rings of coal mill, electronic equipment and storage medium

Abstract

The invention provides a coal mill dynamic and static ring design method and device, electronic equipment and a storage medium, and relates to the technical field of coal mill dynamic and static ring design. Obtaining a design scheme identifier of the current design; selecting and loading a rule set corresponding to the design scheme from a preset data mapping rule base; if the application condition of the rule is met, executing a conversion function defined in the rule, converting the data format of the design platform into a format required by a performance verification tool, and mapping the converted data to a corresponding field of the data format of the performance verification tool; and after conversion of all applicable rules is completed, data obtained through final conversion are automatically sent to a performance verification tool. The design method and device for the dynamic and static rings of the coal mill, the electronic equipment and the storage medium have the advantages that design efficiency is improved, human errors are reduced, and the product development period is shortened.

Description

Technical Field

[0001] This application relates to the technical field of the design of the dynamic and static rings of coal mills. Specifically, it relates to a design method, device, electronic device, and storage medium for the dynamic and static rings of coal mills. Background Art

[0002] In the field of large-scale industrial equipment manufacturing, design and development platforms are widely used in the design of complex components such as the dynamic and static rings of coal mills. To shorten the R & D cycle, reduce R & D costs, and improve design efficiency, enterprises usually build a unified design and development platform and tend to reuse existing mature design solutions. For the development of new-generation coal mill products, enterprises plan to follow the existing design solutions for the dynamic and static rings of multiple series of coal mills and make adjustments and optimizations according to the subtle differences in the performance parameters of new products. However, after designers adjust the parameters of the dynamic and static rings on the design platform, they need to use a performance verification tool to evaluate the performance of the adjusted design solution.

[0003] Currently, there is a problem of data interface incompatibility between the performance verification tools established by enterprises in the early stage and the new design and development platform. There is a barrier between the design data format output by the design platform and the data format that the performance verification tool can accept. This incompatibility has led to a series of serious problems:

[0004] First of all, for performance verification, designers have to manually export the design platform data and spend a lot of time converting it into a format recognizable by the performance verification tool. This manual data conversion process is not only cumbersome and time-consuming but also extremely prone to human errors. Especially when dealing with complex dynamic and static ring design data, the risk of misoperation increases significantly, which may lead to the incorrect transmission of key parameters, thus affecting the subsequent performance evaluation results.

[0005] Secondly, to avoid the risks of manual conversion, some enterprises choose to use multiple sets of verification tools for repeated analysis. However, this method is also inefficient, not only increasing software licensing and maintenance costs but also extending the design cycle. The use of multiple sets of tools also increases the difficulty of data consistency management, which may lead to inconsistent results between different tools and increase the complexity of decision-making.

[0006] Furthermore, the problem of data format incompatibility seriously restricts design efficiency and increases the risk of design errors. Especially when enterprises need to frequently reuse and adjust existing design solutions to adapt to new model products, this problem becomes more prominent. Designers need to repeatedly perform data conversion or multi-tool verification, which not only delays the product development progress but also may miss market opportunities.

[0007] In addition, the incompatibility of data formats also restricts the ability of enterprises to make full use of historical design data. Since the design data of the old platform cannot be directly imported into the new verification tool, it is difficult for enterprises to quickly evaluate and optimize historical design solutions, which reduces the reuse efficiency of design assets and increases unnecessary repetitive work.

[0008] In view of the above problems, the existing technologies urgently need to be improved. Summary of the Invention

[0009] The purpose of this application is to provide a design method, device, electronic device and storage medium for the dynamic and static rings of a coal mill, which have the advantages of improving design efficiency, reducing human errors, and shortening the product development cycle.

[0010] In the first aspect, this application provides a design method for the dynamic and static rings of a coal mill, and the technical solution is as follows:

[0011] It is used for converting the design data of the dynamic and static rings of the coal mill between the design and development platform and the performance verification tool. The method includes: receiving the design data of the dynamic and static rings output by the design platform; obtaining the design scheme identifier of the current design according to the received design data of the dynamic and static rings output by the design platform; selecting and loading the rule set corresponding to the design scheme from the preset data mapping rule library according to the obtained design scheme identifier; traversing each rule in the loaded rule set and checking whether the application condition of the current rule is satisfied; if the application condition of the rule is satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding field of the performance verification tool data format; after completing the conversion of all applicable rules, automatically send the finally converted data to the performance verification tool.

[0012] Furthermore, this application also proposes that the step of if the application condition of the rule is satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding field of the performance verification tool data format includes: if the application condition of the rule is satisfied, analyze the data field type and value range of the data to be converted; establish the mapping relationship between the data field and the conversion function, and determine the conversion function priority; match the data field and the conversion function according to the data field type, value range, and conversion function priority; execute the matched conversion function to convert the data format of the design platform into the performance verification tool format, and map the converted data to the performance verification tool field.

[0013] Furthermore, the present application also proposes that the steps of establishing the mapping relationship between the data field and the conversion function and determining the priority of the conversion function include: obtaining the error rate records of each conversion function for the data field during the historical conversion process; obtaining the version identifier of the current performance verification tool and matching the conversion function compatibility constraint conditions corresponding to the version identifier; determining the error transfer weights of each conversion function for the data field according to the error rate records and the compatibility constraint conditions; generating a dynamic priority ranking based on the error transfer weights, the compatibility constraint conditions, and the error rate records; and updating the dynamic priority ranking to the corresponding rule set in the data mapping rule library.

[0014] Furthermore, the present application also proposes that the steps of generating a dynamic priority ranking based on the error transfer weights, the compatibility constraint conditions, and the error rate records include: analyzing the parameter sensitivity distribution in the historical conversion scenario according to the error rate records; obtaining the constraint type corresponding to the version identifier of the performance verification tool and establishing an associated mapping between the compatibility constraint conditions and the parameter sensitivity; generating a multi-dimensional weight distribution coefficient according to the parameter sensitivity distribution, the associated mapping, and the error transfer weights; performing a normalized weighted calculation on the error transfer weights, the compatibility constraint conditions, and the error rate records by applying the weight distribution coefficient to generate a dynamic priority ranking; and associating the dynamic priority ranking with the conversion function identifier in the data mapping rule library.

[0015] Furthermore, the present application also proposes that the steps of mapping the converted data to the fields of the performance verification tool include: analyzing the data to be mapped, identifying the missing fields and redundant fields; establishing a field mapping rule, defining a data completion strategy and a redundancy elimination strategy; according to the field mapping rule, for the identified missing fields, using a preset default value or a calculation result based on relevant fields to complete the data and eliminate the redundant fields; and mapping the data after the completion and elimination processing to the corresponding fields of the performance verification tool.

[0016] Furthermore, the present application also proposes that the steps of obtaining the design scheme identifier of the current design according to the static and dynamic ring design data output by the received design platform include: parsing the static and dynamic ring design data output by the design platform and extracting the metadata field containing the design scheme category information; establishing a design scheme identifier priority judgment rule, including at least one weight factor such as the newness and oldness of the version and the parameter coverage range; screening out the most prioritized valid identifier from the extracted metadata fields according to the priority judgment rule; screening out the relevant rule subset from the preset data mapping rule library according to the most prioritized valid identifier; constructing a key parameter feature vector, including the feature of the static and dynamic ring geometric parameter range; calculating the similarity between the feature vector and the preset identifier definition in the rule subset; and based on the similarity calculation result, selecting the most matching design scheme identifier as the unique identifier of the current design.

[0017] Furthermore, the present application also proposes that the step of constructing the key parameter feature vector, which includes the feature of the geometric parameter range of the dynamic and static rings, comprises: obtaining the current working condition parameters, the material properties of the dynamic and static rings, and the data of the coal mill model; determining the sensitivity of each geometric parameter of the dynamic and static rings to the design scheme identifier according to the obtained working condition parameters, the material properties of the dynamic and static rings, and the data of the coal mill model; selecting a number of geometric parameters of the dynamic and static rings with the highest sensitivity based on the determined sensitivity; and constructing a feature vector including the range of the selected geometric parameters of the dynamic and static rings.

[0018] Furthermore, the present application also proposes a design device for the dynamic and static rings of a coal mill, which is used to convert the design data of the dynamic and static rings of the coal mill between a design and development platform and a performance verification tool. The device includes: a data receiving module, which is used to receive the design data of the dynamic and static rings output by the design platform; an identifier obtaining module, which is used to obtain the design scheme identifier of the current design according to the design data of the dynamic and static rings output by the received design platform; a rule set selection module, which is used to select and load a rule set corresponding to the design scheme from a preset data mapping rule library according to the obtained design scheme identifier; a rule traversing module, which is used to traverse each rule in the loaded rule set and check whether the application condition of the current rule is satisfied; a data conversion module, which is used to execute the conversion function defined in the rule to convert the data format of the design platform into the format required by the performance verification tool and map the converted data to the corresponding fields of the data format of the performance verification tool if the application condition of the rule is satisfied; and a data sending module, which is used to automatically send the finally converted data to the performance verification tool after the conversion of all applicable rules is completed.

[0019] Furthermore, the present application also proposes an electronic device, which includes a processor and a memory. The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are run.

[0020] Furthermore, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are run.

[0021] As can be seen from the above, a design method, device, electronic device and storage medium for the dynamic and static rings of a coal mill provided by the present application are used to convert the design data of the dynamic and static rings of the coal mill between a design and development platform and a performance verification tool. The method includes receiving the design data of the dynamic and static rings output by the design platform, obtaining the design scheme identifier of the current design, selecting and loading the corresponding rule set, traversing the rules and executing the conversion function that meets the conditions, converting the data format of the design platform into the format required by the performance verification tool, and automatically sending the converted data to the performance verification tool. Through the automated data conversion process, manual operations are reduced, the design efficiency is improved, human errors are reduced, and thus the product development cycle is shortened. Brief Description of the Drawings

[0022] Figure 1 It is a schematic flowchart of a design method for the dynamic and static rings of a coal mill provided by this application.

[0023] Figure 2 It is a schematic structural diagram of a design device for the dynamic and static rings of a coal mill provided by this application.

[0024] In the figure: 210, data reception module; 220, identification acquisition module; 230, dynamic and static ring design data; 240, rule set selection module; 250, rule traversal module; 260, data conversion module; 270, data sending module. Detailed Description of the Embodiments

[0025] Next, the technical solutions in this application will be clearly and completely described in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Usually, the components of this application described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application that is required to be protected, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0027] In the field of large-scale industrial equipment manufacturing, design and development platforms are widely used in the design of complex components such as the dynamic and static rings of coal mills. In order to shorten the R & D cycle, reduce the R & D cost, and improve the design efficiency, enterprises usually build a unified design and development platform and tend to reuse existing mature design solutions. However, there is a problem of incompatible data interfaces between the performance verification tools established by enterprises in the early stage and the new design and development platform. There is a barrier between the design data format output by the design platform and the data format that the performance verification tool can accept. This leads to designers having to manually export the design platform data and spend a lot of time converting it into a format recognizable by the performance verification tool, or using multiple sets of verification tools for repeated analysis. In this case, how to convert the design data of the dynamic and static rings of a coal mill between the design and development platform and the performance verification tool has become an urgent technical problem to be solved.

[0028] This incompatibility issue of data formats seriously affects the efficiency of design iteration. During a design cycle, engineers may need to make dozens of design adjustments and performance verifications. If each verification requires hours of data conversion, the cumulative effect may extend the entire project cycle by weeks or even months. Even more seriously, human errors are likely to be introduced during the manual data conversion process. For example, in an important design verification, due to a unit conversion error, the CFD analysis results deviated significantly from the actual situation, resulting in major adjustments to the design scheme, directly affecting the project schedule and cost. In addition, this inefficient data conversion process also limits engineers' attempts to make more innovative designs because each minor parameter adjustment requires cumbersome data processing for verification. This not only reduces the flexibility of the design but also may lead to missed opportunities for potential performance optimization.

[0029] In response, referring to Figure 1 , this application proposes a design method for the dynamic and static rings of a coal mill, which is used for data conversion of the dynamic and static ring design data of a coal mill between a design and development platform and a performance verification tool. The method includes:

[0030] S110. Receive the dynamic and static ring design data output by the design platform;

[0031] S120. According to the received dynamic and static ring design data output by the design platform, obtain the design scheme identifier of the current design;

[0032] S130. According to the obtained design scheme identifier, select and load the rule set corresponding to the design scheme from a preset data mapping rule library;

[0033] S140. Traverse each rule in the loaded rule set and check whether the application condition of the current rule is satisfied;

[0034] S150. If the application condition of the rule is satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format;

[0035] S160. After completing the conversion of all applicable rules, automatically send the finally converted data to the performance verification tool.

[0036] Among them, the design platform refers to the software tool used for the design of the dynamic and static rings of a coal mill, which can specifically be implemented using a CAD system or a dedicated parametric design software, and can generate and output design data such as the geometric parameters and material properties of the dynamic and static rings. In this application, the design platform serves as the data source and provides the original design information that needs to be converted.

[0037] Among them, the performance verification tool refers to the analysis software used to evaluate the performance of the dynamic and static rings of the coal mill. Specifically, it can be implemented using CFD analysis tools or finite element analysis software, and requires input data in a specific format for performance simulation and evaluation. In this application, the performance verification tool is the target of data conversion and has specific requirements for the data format.

[0038] Among them, the design scheme identifier refers to the identifier used to uniquely identify the current design scheme. Specifically, it can be implemented using version numbers, project codes, or combinations of characteristic parameters, and it is used to determine the applicable data conversion rules. In this application, the design scheme identifier is the key basis for selecting the correct conversion rules, ensuring the pertinence of data conversion.

[0039] Among them, the data mapping rule library refers to the database that stores the data conversion rules corresponding to various design schemes. Specifically, it can be implemented using relational databases or structured files, and it contains the data format conversion rules for different design schemes. In this application, the data mapping rule library provides a flexible and extensible set of conversion rules.

[0040] Among them, the conversion function refers to the program module that performs specific data format conversion operations. Specifically, it can be implemented using scripting languages or programming languages, and is responsible for converting the data format of the design platform into the format required by the performance verification tool. In this application, the conversion function is the key component that actually performs data conversion, ensuring the correct conversion of the data format.

[0041] The core innovation of this application lies in proposing a mechanism to establish a data mapping rule library and dynamically select applicable rules, realizing the automatic conversion of data formats between the design platform and the performance verification tool. This method not only solves the problem of data format incompatibility, but also has high flexibility and scalability, and can adapt to different design schemes and verification tool requirements.

[0042] The working principle of this application can be described in detail as follows:

[0043] First, receive the dynamic and static ring design data from the design platform. These data usually contain information such as geometric parameters and material properties.

[0044] Next, analyze the received data and extract key features to identify the current design scheme. This involves checking specific metadata fields, analyzing parameter combinations, or using predefined identification algorithms. The identification process takes into account version information, project codes, and key parameter values to accurately determine the design scheme identifier.

[0045] Then, according to the identified design scheme identifier, select the corresponding rule set from the pre-established data mapping rule library. The rule library is a structured data storage that contains conversion rules for different design schemes. Each rule set targets a specific combination of design scheme and data format, ensuring the pertinence of the conversion process.

[0046] Subsequently, traverse the selected rule set and check the application conditions of each rule one by one. These conditions may include the existence of data fields, value ranges, or specific logical relationships. Only the rules that meet the application conditions will be executed, which ensures the accuracy and applicability of the conversion process.

[0047] For the rules that meet the conditions, execute the corresponding conversion functions. These functions can perform operations such as unit conversion, data structure reorganization, parameter calculation, or format conversion. The conversion process takes into account the data format differences between the design platform and the performance verification tool, ensuring that the converted data meets the requirements of the verification tool.

[0048] Finally, integrate all the converted data and automatically send it to the performance verification tool to ensure that the data can be correctly received and recognized by the verification tool.

[0049] As a preferred implementation:

[0050] In a certain coal mill design department, engineers use the latest CAD system to design the dynamic and static ring components. The data output by this CAD system is in XML format, containing parameters such as the inner diameter of the dynamic ring, the outer diameter of the static ring, and the blade angle, with units of millimeters and degrees. However, the company's CFD analysis software requires CSV format input, with parameter units of inches and radians.

[0051] The system of this application first receives the XML file output by the CAD system through the network interface. Then, the system parses the XML file, extracts the project code "MWGP-2023A" and the version number "V2.1", and accordingly identifies the current design scheme identifier as "MWGP2023A-V2.1".

[0052] The system then loads the rule set corresponding to "MWGP2023A-V2.1" from the data mapping rule library. This rule set contains data conversion rules for this specific design scheme, such as unit conversion rules and parameter mapping rules.

[0053] Traverse each rule in the rule set. For example, for the inner diameter conversion rule of the dynamic ring, the system checks whether the "inner_diameter" field exists in the XML and verifies whether its value is within the valid range (such as 500 - 2000 mm). If the conditions are met, the system executes the corresponding conversion function: convert the unit from millimeters to inches (divide by 25.4) and save the result to a new data structure.

[0054] Similarly, the system processes other parameters such as the outer diameter of the stationary ring, blade angles, etc. For blade angles, the system not only performs unit conversion (degrees to radians), but also adjusts the parameter representation according to the requirements of the CFD software (such as converting from absolute angle to relative angle).

[0055] Finally, the system organizes all the converted parameters into a CSV format and automatically sends them to the CFD analysis software through a predefined interface. The entire process is fully automated, greatly improving the design efficiency and eliminating the risk of human errors at the same time.

[0056] In some of the above embodiments of the present application, it is proposed that if the application conditions of the rule are satisfied, the conversion function defined in the rule is executed to convert the data format of the design platform into the format required by the performance verification tool, and the converted data is mapped to the corresponding fields of the performance verification tool data format to achieve the conversion of the design platform data to the performance verification tool data. However, in this process, since different data fields may require different conversion methods, and the conversion process may involve complex data processing and mapping relationships, without a systematic method to handle these conversions and mappings, it may lead to low efficiency and high error rate in the conversion process, or even the data conversion cannot be completed correctly.

[0057] In response to this, the present application further proposes that if the application conditions of the rule are satisfied, analyze the data field type and value range of the data to be converted; establish the mapping relationship between the data field and the conversion function, and determine the conversion function priority; according to the data field type, value range, and conversion function priority, match the data field with the conversion function; execute the matched conversion function to convert the data format of the design platform into the performance verification tool format, and map the converted data to the performance verification tool field.

[0058] This technical solution realizes the systematization and optimization of the data conversion and mapping process through a series of steps. First, by analyzing the field type and value range of the data to be converted, it provides a basis for the subsequent selection of the conversion function. Specifically, data type recognition algorithms such as regular expression matching or machine learning classification models can be used to automatically identify the type of data fields, for example, classifying the fields as numeric, string, date, etc. At the same time, for numeric data, statistical methods can be used to calculate its range, such as minimum value, maximum value, mean, and standard deviation, etc.

[0059] Furthermore, establish the mapping relationship between data fields and conversion functions and determine the priorities, which helps to select the most suitable one among multiple possible conversion functions. The mapping relationship can be maintained through a configuration file or a database, which contains a list of conversion functions that may be applicable to each data field. The determination of priorities can be based on multiple factors, such as the historical success rate of the conversion function, the computational complexity, and the compatibility with the target format, etc. For example, a weight value can be assigned to each conversion function, and the higher the weight value, the higher the priority.

[0060] Next, match the data fields and conversion functions according to the information obtained previously (field type, value range, function priority), which ensures that the most suitable conversion method can be found for each field. The matching process can be implemented using a decision tree or a rule engine, taking multiple factors into comprehensive consideration. For example, for a numerical field representing an angle, if its range is between 0 and 360 degrees, a function specifically for angle conversion may be preferred over a general numerical conversion function.

[0061] Finally, execute the matched conversion function and map the result to the target field to complete the entire conversion process. When executing the conversion function, parallel processing technology can be adopted to process multiple data fields simultaneously, improving the conversion efficiency. The converted data will be accurately placed into the corresponding fields of the performance verification tool according to the predefined mapping rules.

[0062] This method effectively solves the complexity and diversity problems that may be encountered in data conversion through a systematic analysis and matching process. It can select the most suitable conversion method according to the specific characteristics of the data, improving the accuracy and efficiency of the conversion. At the same time, by establishing a mapping relationship and a priority mechanism, this method also provides a certain degree of flexibility and can adapt to different types of data conversion requirements.

[0063] As a preferred implementation manner, the technical solution of the present application can be applied to the conversion process of the design data of the dynamic and static rings of a coal mill. For example, assume that the design data of the dynamic and static rings output by the design platform includes the following fields: inner diameter of the static ring (mm), outer diameter of the dynamic ring (mm), clearance between the dynamic and static rings (mm), rotational speed of the dynamic ring (rpm), etc.

[0064] First, the system analyzes the types and ranges of these data fields. For example, both the inner diameter of the static ring and the outer diameter of the dynamic ring are numerical data, and their ranges may be between 500 mm and 2000 mm; the clearance between the dynamic and static rings is numerical data, and the range may be between 0.1 mm and 10 mm; the rotational speed of the dynamic ring is also numerical data, and the range may be between 100 rpm and 1000 rpm.

[0065] Next, the system will establish the mapping relationship between these data fields and available conversion functions. For example, for the inner diameter of the stationary ring and the outer diameter of the rotating ring, there may be unit conversion functions (such as millimeters to inches) and numerical rounding functions; for the clearance between the stationary and rotating rings, there may be precision adjustment functions; for the rotational speed of the rotating ring, there may be unit conversion functions (such as rpm to rad / s). The system will determine the priority of these functions based on factors such as historical conversion success rate and computational complexity.

[0066] Then, the system will perform matching based on the specific characteristics of the data fields and the priority of the conversion functions. For example, if the performance verification tool requires the use of imperial units, the system will select the millimeter-to-inch conversion function for the inner diameter of the stationary ring and the outer diameter of the rotating ring; if the performance verification tool has a high precision requirement for the clearance between the stationary and rotating rings, the system will select a precision adjustment function that retains more decimal places.

[0067] Finally, the system executes the matching conversion function to convert the data of the design platform into the format required by the performance verification tool. For example, the inner diameter of the stationary ring of 1000 mm may be converted to 39.37 inches, the clearance between the stationary and rotating rings of 2.5 mm may be converted to 0.0984 inches (retaining 4 decimal places), and the rotational speed of the rotating ring of 500 rpm may be converted to 52.36 rad / s. The converted data will be accurately mapped to the corresponding fields of the performance verification tool.

[0068] In this way, the technical solution of the present application can efficiently and accurately complete the conversion of the design data of the stationary and rotating rings of the coal mill from the design platform to the performance verification tool, greatly improving the design efficiency and reducing the risk of data conversion errors.

[0069] In some of the above embodiments of the present application, a mapping relationship between data fields and conversion functions is established, and the priority of the conversion functions is determined to achieve the conversion of the data format of the design platform to the format of the performance verification tool. However, in this process, due to the possible differences in the processing effects of different conversion functions on data fields, and the compatibility issues that may be caused by the version update of the performance verification tool, how to dynamically optimize the selection and application order of conversion functions has become an urgent problem to be solved. This directly affects the accuracy and efficiency of data conversion, and further affects the quality and efficiency of the entire design process of the stationary and rotating rings of the coal mill.

[0070] In response to this, the present application further proposes to obtain the error rate records of each conversion function for data fields during the historical conversion process; obtain the version identifier of the current performance verification tool, and match the conversion function compatibility constraint conditions corresponding to the version identifier; determine the error transfer weight of each conversion function for data fields according to the error rate records and the compatibility constraint conditions; generate a dynamic priority ranking based on the error transfer weight, compatibility constraint conditions, and error rate records; and update the dynamic priority ranking to the corresponding rule set in the data mapping rule library.

[0071] This application realizes the dynamic adjustment of the priority of the conversion function by introducing multi-dimensional factors such as historical error rate records, version compatibility constraints, and error transfer weights. Specifically, first, obtain the error rate records of each conversion function for data fields during the historical conversion process. This step can be achieved by establishing an error rate database to record the performance of each conversion function during each conversion process. The error rate can be calculated by comparing the data differences before and after the conversion, such as using metrics like root mean square error or mean absolute error.

[0072] Secondly, obtain the version identifier of the current performance verification tool and match the conversion function compatibility constraint conditions corresponding to the version identifier. The version identifier can be a version number or a specific code, and the compatibility constraint conditions may include the availability of specific functions, parameter requirements, or output formats, etc. This step can be realized by maintaining a version-compatibility mapping table to ensure that the selected conversion function can work properly with the current tool version.

[0073] Furthermore, according to the error rate records and compatibility constraint conditions, determine the error transfer weights of each conversion function for data fields. The error transfer weight reflects the influence degree of different conversion functions on the overall conversion result. The calculation method can consider the magnitude of the error rate, the importance of the data fields, and the strictness of the compatibility constraints. For example, a weighted average method can be used to multiply the error rate, field importance coefficient, and compatibility coefficient to obtain the comprehensive weight.

[0074] Based on the above factors, generate a dynamic priority ranking. This step can adopt multi-objective optimization algorithms such as genetic algorithms or particle swarm optimization algorithms, comprehensively consider the error transfer weights, compatibility constraint conditions, and error rate records, and obtain the optimal application order of the conversion functions. The optimization goal can be set to minimize the overall error rate while meeting the compatibility constraints.

[0075] Finally, update the dynamic priority ranking to the corresponding rule set in the data mapping rule library. This ensures that the subsequent conversion process can use the latest optimization results and realizes the continuous improvement of the conversion strategy.

[0076] Through this method, this application realizes the intelligence and self-adaptability of the conversion function selection. Dynamically adjusting the application order of the conversion functions effectively improves the accuracy and efficiency of data conversion. It can adapt to the changing requirements of different version tools and continuously optimize itself based on historical performance, thus significantly improving the conversion quality of the dynamic and static ring design data of the coal mill between different platforms.

[0077] When implementing specifically, the following steps can be adopted:

[0078] Build an error rate database: For each conversion function, record its historical error rates on different data fields. For example, a two-dimensional matrix can be used, where the rows represent the conversion functions, the columns represent the data fields, and the matrix elements are the corresponding error rates.

[0079] Version compatibility mapping: Establish a version compatibility table to record the compatibility of each version of the performance verification tool with the conversion functions. Boolean values can be used to represent compatibility (1) or incompatibility (0).

[0080] Error transfer weight calculation: For each conversion function f and data field d, calculate the weight W(f, d) = E(f, d) * I(d) * C(f, v), where E(f, d) is the error rate, I(d) is the field importance coefficient, and C(f, v) is the compatibility coefficient of function f with the current version v.

[0081] Dynamic priority sorting: Use a genetic algorithm to generate an optimized sequence of conversion functions. The chromosome encoding can represent the application order of the conversion functions, and the fitness function is the reciprocal of the overall error rate.

[0082] Rule set update: Convert the optimized sequence of conversion functions into a rule set format and update it to the data mapping rule library.

[0083] Through this implementation method, the present application can dynamically adjust the conversion strategy in actual applications to adapt to different data characteristics and tool versions, thereby improving the accuracy and efficiency of the dynamic and static ring design data conversion of the coal mill. The advantage of this method is that it can automatically adapt to changes in data and tools, reducing the need for manual intervention. For example, there are three conversion functions: F1: millimeter to inch, F2: degree to radian, F3: unit rounding; through the analysis of historical data, it is found that: the error rate of F1 when processing size data is only 0.1%, the error rate of F2 when processing angle data is 0.5%, and the error rate of F3 fluctuates greatly on various types of data (1% - 5%); at the same time, the current version 3.2 of the CFD tool requires extremely high precision for angle data.

[0084] The system will automatically adjust to give F1 priority to processing size data, F2 priority to processing angle data (and may give higher precision settings), while F3 may be given a lower priority or only used for less important fields.

[0085] The next time an engineer needs to convert new coal mill design data, this optimized conversion strategy will be automatically applied to improve the conversion accuracy and efficiency.

[0086] In some of the above embodiments of the present application, steps for generating a dynamic priority ranking based on error transfer weights, compatibility constraint conditions, and error rate records are proposed to determine the priority of the conversion function. However, in this process, solely relying on these factors may not fully consider the sensitivity differences of different parameters in different scenarios, as well as the new constraint conditions brought about by the version update of the performance verification tool. This may lead to an inaccurate and inflexible generated priority ranking, unable to adapt to complex and ever-changing design environments and verification requirements.

[0087] To address this, the present application further proposes analyzing the parameter sensitivity distribution in historical conversion scenarios based on the error rate record; obtaining the constraint types corresponding to the version identifier of the performance verification tool, and establishing an association mapping between the compatibility constraint conditions and parameter sensitivity; generating a multi-dimensional weight distribution coefficient according to the parameter sensitivity distribution, association mapping, and error transfer weight; applying the weight distribution coefficient to perform a normalized weighted calculation on the error transfer weight, compatibility constraint conditions, and error rate record to generate a dynamic priority ranking; and associating the dynamic priority ranking with the conversion function identifier in the data mapping rule library.

[0088] The present application significantly improves the accuracy and adaptability of the dynamic priority ranking by introducing parameter sensitivity analysis and multi-dimensional weight distribution. Specifically, first, analyze the parameter sensitivity distribution in historical conversion scenarios based on the error rate record. This step can be achieved by statistically analyzing the influence degree of each parameter on the conversion result in historical data. For example, statistical methods such as analysis of variance or sensitivity index can be used to calculate the contribution degree of each parameter to the conversion result in different scenarios.

[0089] Furthermore, obtain the constraint types corresponding to the version identifier of the performance verification tool, and establish an association mapping between the compatibility constraint conditions and parameter sensitivity. This step can be achieved by establishing a mapping table or matrix, which lists the constraint types of different versions of the tool and associates them with the parameter sensitivity obtained from the previous analysis. For example, for a specific version of the verification tool, there may be more stringent accuracy requirements for certain geometric parameters, and in this case, it is necessary to increase the weight of these parameters in the sensitivity distribution.

[0090] Generate a multi-dimensional weight distribution coefficient according to the parameter sensitivity distribution, association mapping, and error transfer weight. This step can be achieved by using weighted average or multi-objective optimization algorithms. Specifically, the parameter sensitivity, the strictness of the constraint conditions, and the historical error transfer weight can be used as inputs in different dimensions, and by setting appropriate weight coefficients, a comprehensive weight distribution coefficient can be calculated.

[0091] Apply the weight distribution coefficient to perform normalized weighted calculations on the error transfer weight, compatibility constraint conditions, and error rate records to generate a dynamic priority ranking. This step can use normalization methods such as min-max normalization or z-score standardization to transform indicators of different scales onto the same dimension. Then, apply the weight distribution coefficient obtained in the previous step to perform weighted summation on these normalized indicators to obtain the final priority score of each transfer function.

[0092] Finally, associate the dynamic priority ranking with the transfer function identifier in the data mapping rule library. This step can be achieved by updating the priority field in the database to ensure that the latest priority ranking can be used for each data conversion.

[0093] Through the above steps, the technical solution of this application can generate a dynamic priority ranking more precisely and flexibly. Due to considering the parameter sensitivity distribution, this method can identify which parameters have a greater impact on the results under different design conditions, thus providing an important basis for subsequent weight distribution. At the same time, by establishing an associated mapping between the compatibility constraint conditions and parameter sensitivity, it ensures that the generated priority ranking can adapt to the specific requirements of different versions of the tool, improving the compatibility and scalability of the system.

[0094] The introduction of the multi-dimensional weight distribution coefficient makes the final priority ranking more reasonable and accurate. This method can consider various influencing factors more comprehensively, including the performance of parameters in historical scenarios, the special requirements of the current tool version, and the historical data of error transfer. Through normalized weighted calculations, not only the influence of different factors is balanced, but also the comparability and consistency of the calculation results are ensured.

[0095] Associating the dynamic priority ranking with the transfer function identifier in the data mapping rule library realizes the effective application and continuous update of the priority ranking result. This means that for each data conversion, the system can use the latest and most suitable priority ranking for the current scenario, thereby improving the accuracy and efficiency of the conversion.

[0096] As a preferred implementation, the analysis of the parameter sensitivity distribution can be carried out in the following steps: First, extract the value range of each parameter and the corresponding conversion results from the historical data. Then, use the Monte Carlo simulation method to randomly generate a large number of sample points within the value range of each parameter. Next, perform conversions on each sample point and record the changes in the conversion results. Finally, calculate the contribution degree of each parameter to the change in the conversion results to obtain the parameter sensitivity distribution.

[0097] For example, assume that when analyzing the geometric parameters of the stationary and rotating rings, it is found that the inner diameter of the stationary ring has the highest sensitivity to the conversion result, followed by the outer diameter of the rotating ring. Then, when generating the multi-dimensional weight distribution coefficients, these two parameters will be assigned higher weights. Specifically, if the sensitivity of the inner diameter of the stationary ring is 0.8 and the sensitivity of the outer diameter of the rotating ring is 0.6, and the sensitivities of other parameters are all lower than 0.5, then when allocating weights, the weight coefficients of the inner diameter of the stationary ring and the outer diameter of the rotating ring may be set to 1.5 and 1.2, respectively, while the weight coefficients of other parameters may remain around 1.0.

[0098] When establishing the association mapping between compatibility constraints and parameter sensitivities, a matrix form can be adopted. For example, create a two-dimensional matrix where the rows represent different parameters and the columns represent different versions of the performance verification tool. Each element of the matrix represents the constraint strength of a specific parameter under a specific tool version. This mapping method allows the system to quickly search for and update constraints, improving the efficiency of data processing.

[0099] Through this method, the technical solution of this application can adaptively adjust the priority of the conversion function according to the characteristics of different design scenarios and verification tool versions. This not only solves the problem of inaccurate and inflexible priority sorting in the original method but also provides strong support for the efficient conversion and verification of the design data of the dynamic and static rings of the coal mill. This solution can better adapt to the complex and changeable design environment, improve the accuracy of data conversion, reduce the need for human intervention, thereby significantly enhancing the design efficiency and reducing the risk of design errors.

[0100] This application further proposes that the steps for generating multi-dimensional weight distribution coefficients based on parameter sensitivity distribution, association mapping, and error transfer weights include:

[0101] Define the parameter sensitivity distribution vector S = (s1, s2,..., sn), where si represents the sensitivity of the i-th parameter, and n is the total number of parameters. This vector reflects the degree of influence of each parameter on the design result. At the same time, define the association mapping matrix M, where Mij represents the degree of association between the i-th parameter and the j-th constraint condition. This matrix takes into account the complex relationship between parameters and constraint conditions. In addition, define the error transfer weight vector W = (w1, w2,..., wn), where wi represents the error transfer weight of the i-th parameter. This vector emphasizes the importance of error transfer in weight allocation.

[0102] To meet the requirements of different versions, this application introduces the version constraint factor V = (v1, v2,..., vn), where vi represents the applicability of the i-th parameter to the current version. This factor ensures that the generated weight distribution coefficients are compatible with the current version.

[0103] To more accurately capture the complex interactions between various factors, this application defines three functions: f(x) = 1 / (1 + e^(-kx)), g(x) = ln(1 + x), h(x) = x^p, where k and p are adjustable parameters used to control the degree of non-linearity. These non-linear functions are respectively used to process parameter sensitivity, correlation mapping, and error transfer weight.

[0104] Based on the above definitions, this application calculates the multi-dimensional weight distribution coefficient vector C = (c1, c2,..., cn), where ci = vi * [α * f(si) + β * g(Σj Mij) + γ * h(wi)]. α, β, and γ are adjustment coefficients, and α + β + γ = 1. This formula comprehensively considers multiple factors such as parameter sensitivity, correlation mapping, error transfer weight, and version constraints, and can comprehensively reflect the complex relationships in the design process of the dynamic and static rings of the coal mill.

[0105] This application calculates the sensitivity distribution of each parameter using historical data, where Yt represents the output result in the t-th historical record, Xi,t represents the value of the i-th parameter in the t-th historical record, and T is the total number of historical records. This method can more accurately reflect the actual impact of parameters on the design result.

[0106] To ensure the rationality of the calculation results, this application sets a series of constraint conditions: 0 ≤ si, wi, ci, vi ≤ 1 (i = 1, 2,..., n), 0 ≤ Mij ≤ 1 (i = 1, 2,..., n; j = 1, 2,..., m), 0 ≤ α, β, γ ≤ 1, α + β + γ = 1, k > 0, p > 0. These constraint conditions ensure that each parameter and coefficient are within a reasonable range.

[0107] In practical applications, the method of this application can be implemented through the following steps:

[0108] First, by analyzing historical data, calculate the sensitivity distribution of each parameter. For example, for the design of the dynamic and static rings of the coal mill, the design records of the past 100 times can be analyzed to calculate the influence degree of each geometric parameter (such as ring diameter, groove depth, etc.) on the final performance indicators (such as coal grinding efficiency, energy consumption, etc.).

[0109] Then, establish a correlation mapping matrix. By analyzing design specifications and expert experience, determine the correlation degree between each parameter and constraint conditions (such as strength requirements, machining accuracy, etc.). For example, the ring diameter may be highly correlated with strength requirements, while the groove depth may be more correlated with coal grinding efficiency.

[0110] Next, based on the error data in historical designs, determine the error transfer weight of each parameter. For example, if it is found that a small error in the ring diameter will cause a large performance fluctuation, then the error transfer weight of the ring diameter should be relatively high.

[0111] Considering that performance verification tools of different versions may have different requirements, this application introduces a version constraint factor. For example, if a new version of the verification tool pays more attention to certain specific parameters, then the version constraint factors of these parameters should be higher.

[0112] After determining all necessary vectors and matrices, this application uses the defined non-linear function and multi-dimensional weight distribution coefficient formula for calculation. For example, assuming that for the ring diameter parameter, the calculated sensitivity is 0.8, the sum of associated mappings is 2.5, the error transfer weight is 0.6, and the version constraint factor is 0.9, then its weight distribution coefficient may be: 0.9 * [0.4 * f(0.8) + 0.3 * g(2.5) + 0.3 * h(0.6)].

[0113] Through this method, this application can generate a weight distribution coefficient vector that comprehensively considers multiple factors. This vector can be used for subsequent dynamic priority sorting, thereby optimizing the design process of the dynamic and static rings of the coal mill.

[0114] The technical solution of this application can more accurately reflect the complex relationships in the design process of the dynamic and static rings of the coal mill by comprehensively considering multiple factors and introducing non-linear functions. Compared with traditional linear weighting methods, the method of this application can better capture the non-linear interactions between parameters, thereby generating more accurate multi-dimensional weight distribution coefficients.

[0115] By introducing non-linear functions and multiple influencing factors, the method of this application can more accurately reflect the importance of each parameter in the actual design. By introducing the version constraint factor, the method of this application can better meet the requirements of performance verification tools of different versions, improving the versatility and scalability of the method. Through more accurate weight distribution, designers can more specifically optimize key parameters, thereby improving the design efficiency. By considering the error transfer weight, the method of this application can better control design errors and reduce design risks. The multi-dimensional weight distribution coefficient provides more comprehensive information support for design decisions, helping designers make more reasonable decisions.

[0116] In some of the above embodiments of this application, it is proposed to map the converted data to the fields of the performance verification tool to map the converted data to the corresponding fields of the performance verification tool data format. However, in this process, there may be problems of missing or redundant data fields, which may cause the performance verification tool to be unable to correctly read or process the converted data, thus affecting the accuracy of the verification results. In addition, direct mapping may not be able to handle the differences in field definitions between the design platform and the performance verification tool, and a more flexible and intelligent mapping method is needed.

[0117] In response to this, the present application further proposes that the steps of mapping the data after transformation to the fields of the performance verification tool include: analyzing the data to be mapped, identifying missing fields and redundant fields; establishing field mapping rules, defining data completion strategies and redundant elimination strategies; according to the field mapping rules, for the identified missing fields, using preset default values or calculation results based on relevant fields to complete the data, and eliminating redundant fields; mapping the data after completion and elimination processing to the corresponding fields of the performance verification tool.

[0118] The technical solution proposed by the present application solves the problems of missing and redundant fields that may occur in the data mapping process through a series of steps. First, by analyzing the data to be mapped to identify missing and redundant fields, this lays the foundation for subsequent processing. Specifically, a method of data structure comparison can be used to compare the fields of the data to be mapped with the fields required by the performance verification tool, so as to identify which fields are missing and which fields are redundant.

[0119] Among them, the identification of missing fields can be achieved by establishing a complete list of fields required by the performance verification tool, and then checking one by one whether these fields exist in the data to be mapped. If a certain field does not exist in the data to be mapped, it is marked as a missing field. For example, if the performance verification tool requires the field of "dynamic ring inner diameter" but it is not found in the data to be mapped, then "dynamic ring inner diameter" is marked as a missing field.

[0120] The identification of redundant fields can be carried out by reverse checking, that is, checking whether each field in the data to be mapped exists in the list of fields required by the performance verification tool. If a certain field does not exist in the list of fields required by the performance verification tool, it is marked as a redundant field. For example, if the data to be mapped contains the field of "designer's name" but the performance verification tool does not require this information, then "designer's name" is marked as a redundant field.

[0121] Then, establish field mapping rules, including data completion and redundant elimination strategies, which provide a flexible framework to handle different types of data inconsistency problems. The field mapping rules can be in the form of key-value pairs, where the key is the field name of the design platform and the value is the corresponding field name of the performance verification tool. The data completion strategy can include methods such as using default values, calculating based on other fields, or inferring from historical data. The redundant elimination strategy can simply exclude the fields marked as redundant from the mapping process.

[0122] Furthermore, perform actual operations according to the established rules: for missing fields, use preset default values or calculation results based on related fields to complete them, ensuring data integrity. For example, if the missing field is the "dynamic ring material density" field, the preset material density value can be found according to the "dynamic ring material type" field for completion. Or, if the missing field is the "dynamic ring volume" field, it can be calculated based on the existing dynamic ring size data (such as inner diameter, outer diameter, height).

[0123] At the same time, eliminate redundant fields, improving data accuracy and efficiency. The process of eliminating redundant fields can be achieved by simply not including these fields in the final mapping result. This ensures that only the data truly required by the performance verification tool is transmitted, reducing the burden of data transmission and processing.

[0124] Finally, map the processed data to the corresponding fields of the performance verification tool, completing the entire mapping process. This step can be achieved by traversing the processed data and assigning the value of each field to the corresponding field of the performance verification tool according to the previously established field mapping rules.

[0125] Thus, the technical solution of this application effectively solves the problems of missing and redundant fields in the data mapping process through intelligent and adaptive methods. By identifying and handling data inconsistency problems, this method can effectively bridge the data format differences between the design platform and the performance verification tool, greatly improving the accuracy and efficiency of data conversion.

[0126] As a preferred implementation, the technical solution of this application can be applied to a specific dynamic and static ring design scenario of a coal mill. Assume that the dynamic and static ring design data output by the design platform includes the following fields: dynamic ring inner diameter, dynamic ring outer diameter, dynamic ring height, static ring inner diameter, static ring outer diameter, static ring height, dynamic ring material type, static ring material type, designer's name, design date. And the fields required by the performance verification tool include: dynamic ring inner diameter, dynamic ring outer diameter, dynamic ring height, static ring inner diameter, static ring outer diameter, static ring height, dynamic ring material density, static ring material density, dynamic ring surface roughness, static ring surface roughness.

[0127] In this scenario, the system will first identify the missing fields (dynamic ring material density, static ring material density, dynamic ring surface roughness, static ring surface roughness) and redundant fields (designer's name, design date). Then, the system will establish field mapping rules, for example:

[0128] For the dynamic ring material density and static ring material density, find the preset density values according to the material type.

[0129] For the dynamic ring surface roughness and static ring surface roughness, use the default value of 3.2μm.

[0130] Directly remove the fields of designer's name and design date.

[0131] Next, the system will execute these rules. Assuming that the dynamic ring material type is "high chromium cast iron" and the static ring material type is "wear-resistant steel", the system will look up the preset material density values. For example, the density of high chromium cast iron is 7.2 g / cm 3 , and the density of wear-resistant steel is 7.8 g / cm 3 . The system will use these values to complete the missing density fields and use the default value of 3.2 μm to complete the surface roughness field. At the same time, the system will remove the two redundant fields of designer's name and design date.

[0132] Finally, the system will map the processed data to the corresponding fields of the performance verification tool to generate a complete and compatible data set, including all the fields required by the performance verification tool, and each field has an appropriate value.

[0133] In this way, the technical solution of the present application can effectively solve the problems of missing and redundant fields in the data mapping process, improve the accuracy and efficiency of data conversion. This not only reduces the possibility of human errors, but also greatly improves the automation degree of the design verification process. Since the integrity and accuracy of the data are ensured, this method can also improve the reliability of the performance verification results, providing a more reliable basis for the design optimization of the dynamic and static rings of the coal mill. In addition, the flexibility of this method enables it to adapt to different types of data structure differences, enhancing the versatility and scalability of the system, and being able to better handle new data formats or field requirements that may appear in the future.

[0134] In some of the above embodiments of the present application, it is proposed to obtain the design scheme identifier of the current design according to the dynamic and static ring design data output by the received design platform, so as to select and load the rule set corresponding to the design scheme from the preset data mapping rule library. However, in this process, since the dynamic and static ring design data output by the design platform may contain multiple versions or various types of design scheme information, it may be difficult to directly obtain a unique design scheme identifier from it. In addition, different design schemes may have similar characteristics, resulting in difficulties in ensuring the accuracy and uniqueness of the identifier. These problems may affect the subsequent selection and loading of the rule set, and thus affect the accuracy and efficiency of the entire data conversion process.

[0135] In response, the present application further proposes to analyze the stationary and rotating ring design data output by the design platform, extract metadata fields containing design scheme category information; establish a judgment rule for the priority of design scheme identifiers, including at least one weight factor such as the degree of newness or oldness of the version and the parameter coverage range; according to the priority judgment rule, screen out the most prioritized valid identifier from the extracted metadata fields; according to the most prioritized valid identifier, screen out the relevant rule subset from the preset data mapping rule library; construct a key parameter feature vector, including the geometric parameter range features of the stationary and rotating rings; calculate the similarity between the feature vector and the preset identifier definition in the rule subset; based on the similarity calculation result, select the most matching design scheme identifier as the unique identifier of the current design.

[0136] This technical solution solves the problem of obtaining the design scheme identifier through multi-level screening and precise matching. First, analyze the design data and extract the metadata fields to ensure the acquisition of key information from the source data. This step can be achieved by using predefined parsing algorithms. For example, regular expression matching or structured data parsing techniques can be used to extract the metadata fields containing design scheme category information.

[0137] Furthermore, establish a priority judgment rule, considering the newness or oldness of the version and the parameter coverage range, to improve the accuracy of identifier selection. The priority judgment rule can adopt a weighted scoring system, where the degree of newness or oldness of the version can be scored according to the timestamp or version number, and the parameter coverage range can be scored according to the number and importance of the included parameters. For example, the weight of the degree of newness or oldness of the version can be set to 0.6, and the weight of the parameter coverage range can be set to 0.4, and then each candidate identifier can be scored according to the specific situation.

[0138] Use the priority rule to screen out the valid identifier to ensure the selection of the most suitable candidate identifier. This step can be achieved by comparing the weighted scores of each candidate identifier and selecting the identifier with the highest score as the most prioritized valid identifier.

[0139] Thus, screen out the relevant rule subset from the rule library to narrow the scope of subsequent matching. This can be achieved by using the most prioritized valid identifier as the keyword to perform rapid indexing and screening in the preset data mapping rule library. For example, a hash table or index tree structure can be used to improve the screening efficiency.

[0140] Specifically, construct a feature vector, including key geometric parameter features, to provide a basis for precise matching. The feature vector can include key geometric parameters such as the inner diameter, outer diameter, height, and angle of the stationary and rotating rings. These parameters can be normalized to ensure the effective comparison of parameters with different dimensions.

[0141] Calculate the similarity between the eigenvector and the preset identifier to achieve accurate identifier matching. Methods such as cosine similarity or Euclidean distance can be used for similarity calculation. For example, when using cosine similarity, the dot product operation can be performed on the eigenvector and the vector defined by the preset identifier, and then divided by the product of the norms of the two vectors to obtain the similarity value.

[0142] Finally, select the most matching identifier based on the similarity result to ensure uniqueness and accuracy. A similarity threshold can be set, and only the matching results that exceed this threshold are considered valid. If there are multiple matching results that exceed the threshold, the one with the highest similarity is selected as the identifier of the final design solution.

[0143] This method effectively solves the problems of accuracy and uniqueness in obtaining the design solution identifier through multi-level screening and accurate matching. It not only considers the version and coverage of the design data, but also uses geometric parameter features for accurate matching, greatly improving the accuracy of identifier selection. The innovation of this method lies in the combination of priority judgment and feature similarity calculation, which not only ensures efficiency but also accuracy.

[0144] In this way, subsequent data conversion errors caused by improper identifier selection can be effectively avoided, and the reliability and efficiency of the entire design and verification process are improved. For example, when processing the design data of static and dynamic rings in multiple versions, this method can accurately identify the latest and most relevant design solution, avoiding the use of outdated or inapplicable data for subsequent conversion. At the same time, through precise feature matching, it is possible to distinguish design solutions that seem similar but actually have subtle differences, ensuring that the selected identifier truly represents the current design intention.

[0145] As a preferred implementation, the method of this application can establish an intelligent intermediate layer between the design platform and the performance verification tool. This intermediate layer not only executes the above-mentioned identifier acquisition and data conversion processes, but also can maintain a dynamically updated design solution library and rule set. Each successful identifier matching and data conversion can be recorded and analyzed for optimizing future matching processes. For example, the weight factors of the priority judgment rules can be automatically adjusted according to the matching success rate, or the dimension of the eigenvector can be dynamically expanded according to newly emerging design features.

[0146] Specifically, a feedback mechanism can be set up to allow designers to evaluate the results after each data conversion. If there are cases of incorrect matching or inaccurate conversion, the system can automatically analyze the reasons for the errors and adjust the identifier acquisition strategy accordingly. For example, if it is found that certain geometric parameters are more distinguishable in a specific type of design, the weights of these parameters in the eigenvector can be dynamically increased.

[0147] In addition, this method can also be combined with machine learning algorithms to continuously optimize the accuracy of logo matching by analyzing a large amount of historical design data and conversion records. For example, algorithms such as support vector machine (SVM) or random forest can be used to train a classification model based on historical data to predict the design scheme logo most likely corresponding to the given design data. This way can further improve the intelligence and adaptability of logo acquisition.

[0148] By implementing the technical solution of this application, designers can significantly reduce the time and effort spent on data format conversion. For example, in a typical dynamic and static ring design project of a coal mill, the data conversion work that originally took several hours to handle manually can now be automatically completed within a few minutes. This not only improves work efficiency but also greatly reduces the risk of human errors. At the same time, due to the improved accuracy of the logo acquisition process, subsequent performance verification has become more reliable, reducing the number of repeated verifications and design modifications caused by data mismatch.

[0149] In some of the above embodiments of this application, a key parameter feature vector is proposed to include the range feature of the geometric parameters of the dynamic and static rings. However, there are some problems in this process. First, how to determine which geometric parameters of the dynamic and static rings are highly sensitive to the design scheme logo and thus should be included in the feature vector. Second, how to effectively use relevant data such as current working condition parameters, material properties of the dynamic and static rings, and coal mill models to guide the construction of the feature vector. Finally, how to ensure that the constructed feature vector can accurately reflect the key features of the design scheme for subsequent similarity calculation and logo matching.

[0150] In response to this, this application further proposes that the steps of constructing a key parameter feature vector including the range feature of the geometric parameters of the dynamic and static rings are as follows: obtaining current working condition parameters, material properties of the dynamic and static rings, and coal mill model data; determining the sensitivity of each geometric parameter of the dynamic and static rings to the design scheme logo according to the obtained working condition parameters, material properties of the dynamic and static rings, and coal mill model data; based on the determined sensitivity, selecting several geometric parameters of the dynamic and static rings with the highest sensitivity; constructing a feature vector including the ranges of the selected geometric parameters of the dynamic and static rings.

[0151] This application constructs a key parameter feature vector containing the range characteristics of the geometric parameters of the stationary and rotating rings through a series of steps. First, the current operating condition parameters, the material properties of the stationary and rotating rings, and the data of the coal mill model are obtained, which provide the basis for subsequent analysis. Then, these data are used to determine the sensitivity of each geometric parameter of the stationary and rotating rings to the design scheme identification. This step can identify which parameters have the greatest impact on the design scheme. Next, based on the determined sensitivity, several geometric parameters of the stationary and rotating rings with the highest sensitivity are selected, which ensures that the feature vector contains the most critical parameters. Finally, a feature vector containing the range of the selected geometric parameters of the stationary and rotating rings is constructed, and this feature vector can accurately reflect the key features of the design scheme.

[0152] When obtaining the current operating condition parameters, the material properties of the stationary and rotating rings, and the data of the coal mill model, it can be achieved through various methods. For example, these information can be directly extracted from the database of the design platform, or obtained in real-time from the coal mill operating system through a dedicated data acquisition interface. The operating condition parameters may include the rotational speed, load, feed rate, etc. of the coal mill; the material properties of the stationary and rotating rings may include the hardness, wear resistance, thermal expansion coefficient, etc. of the material; and the data of the coal mill model may include basic information such as the size, capacity, power, etc. of the coal mill.

[0153] Determining the sensitivity of each geometric parameter of the stationary and rotating rings to the design scheme identification is a key step. This can be achieved by establishing a mathematical model or using machine learning algorithms. Specifically, the partial differential analysis method can be adopted, and the sensitivity is quantified by calculating the influence degree of the small change of each geometric parameter on the performance of the overall design scheme.

[0154] When selecting parameters based on the determined sensitivity, a sensitivity threshold can be set. For example, select the parameters with the top 30% sensitivity ranking, or select the parameters with a sensitivity value greater than a specific value (such as 0.5). This selection process can be dynamic and adjusted according to different design requirements and operating conditions. For example, in some special operating conditions, more parameters may need to be selected to ensure the comprehensiveness of the feature vector.

[0155] When constructing the feature vector, not only the specific values of the selected parameters should be included, but also the range of variation of these parameters should be considered. For example, for the parameter of the inner diameter of the rotating ring, the feature vector may include its nominal value and the allowable upper and lower floating ranges. This approach can better capture the flexibility and adaptability of the design scheme.

[0156] The technical solution of this application provides a systematic solution to the problem of how to construct a feature vector of key parameters. By obtaining comprehensive relevant data, this solution can analyze parameter sensitivity based on actual working conditions and material properties, which is more accurate and flexible than simply relying on experience or fixed rules. The introduction of sensitivity analysis makes parameter selection have a scientific basis and avoids the deviation that may be brought by subjective judgment.

[0157] Select the parameters with the highest sensitivity to construct the feature vector, ensuring that the information contained in the vector is the most critical and representative. This not only improves the accuracy of subsequent similarity calculations but also reduces the computational complexity because irrelevant or low-sensitivity parameters are excluded. At the same time, by including parameter ranges rather than just fixed values, the feature vector can better express the variability and adaptability of the design scheme, which is particularly important for dealing with different working conditions and changing requirements.

[0158] An important advantage of this method is its self-adaptability. As new data accumulates and working conditions change, the sensitivity analysis can be updated regularly, thereby dynamically adjusting the composition of the feature vector. This enables the system to continuously optimize and adapt to new design requirements and technological developments.

[0159] As a preferred implementation method, this application can be implemented as follows: First, obtain the working condition data of the past week from the coal mill operation system, including the average rotational speed (e.g., 980 rpm), average load (e.g., 85%), and average feed rate (e.g., 50 tons / hour). At the same time, extract the key properties of the dynamic and static ring materials from the material database, such as hardness (e.g., HRC60), wear resistance coefficient (e.g., 0.95), and thermal expansion coefficient (e.g., 11.5×10^-6 / ℃). The coal mill model data includes its rated power (e.g., 500 kW) and maximum processing capacity (e.g., 80 tons / hour).

[0160] Next, using these data, calculate the sensitivity of each dynamic and static ring geometric parameter through the sensitivity analysis method. The analysis results show that the sensitivities of the inner diameter of the dynamic ring, the outer diameter of the static ring, and the clearance between the dynamic and static rings are 0.85, 0.78, and 0.72 respectively, which are much higher than other parameters.

[0161] Based on this result, select these three parameters with the highest sensitivity to construct the feature vector. The form of the feature vector may be as follows: [Inner diameter of the dynamic ring: 1200 mm ± 5 mm, Outer diameter of the static ring: 1500 mm ± 8 mm, Clearance between the dynamic and static rings: 2.5 mm ± 0.2 mm].

[0162] This method not only considers the specific values of the parameters but also includes the allowable range of variation, thus more comprehensively reflecting the characteristics and flexibility of the design scheme. The feature vector constructed in this way can accurately capture the key features of the design scheme, providing a reliable basis for subsequent similarity calculation and identification matching, thereby improving the efficiency and accuracy of the entire dynamic and static ring design process of the coal mill.

[0163] In a second aspect, referring to Figure 2 , the present application also proposes a design device for the dynamic and static rings of a coal mill, which is used to perform data conversion of the dynamic and static ring design of the coal mill between a design and development platform and a performance verification tool. The device includes:

[0164] A data receiving module 210, configured to receive the dynamic and static ring design data output by the design platform;

[0165] An identification obtaining module 220, configured to obtain the design scheme identification of the current design according to the received dynamic and static ring design data output by the design platform;

[0166] A rule set selection module 230, configured to select and load a rule set corresponding to the design scheme from a preset data mapping rule library according to the obtained design scheme identification;

[0167] A rule traversing module 240, configured to traverse each rule in the loaded rule set and check whether the application conditions of the current rule are satisfied;

[0168] A data conversion module 250, configured to, if the application conditions of the rule are satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format;

[0169] A data sending module 260, configured to automatically send the finally converted data to the performance verification tool after completing the conversion of all applicable rules.

[0170] Through the automated data conversion process, manual operations are reduced, design efficiency is improved, and human errors are reduced, thereby shortening the product development cycle.

[0171] In addition, in some preferred embodiments, the design device for the dynamic and static rings of the coal mill proposed by the present application can execute any one of the steps in the above method.

[0172] In a third aspect, the present application also provides an electronic device, including a processor and a memory. The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are run.

[0173] Through the above technical solution, the processor and the memory are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanisms (not shown). The memory stores computer-readable instructions executable by the processor. When the electronic device runs, the processor executes the computer-readable instructions to execute the method in any optional implementation manner of the above embodiments to achieve the following functions: receiving the static and dynamic ring design data output by the design platform; obtaining the design scheme identifier of the current design according to the received static and dynamic ring design data output by the design platform; selecting and loading the rule set corresponding to the design scheme from the preset data mapping rule library according to the obtained design scheme identifier; traversing each rule in the loaded rule set and checking whether the application condition of the current rule is satisfied; if the application condition of the rule is satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format; after completing the conversion of all applicable rules, automatically send the finally converted data to the performance verification tool.

[0174] In a fourth aspect, the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are run.

[0175] Through the above technical solution, when the computer program is executed by the processor, the method in any optional implementation manner of the above embodiments is executed to achieve the following functions: receiving the static and dynamic ring design data output by the design platform; obtaining the design scheme identifier of the current design according to the received static and dynamic ring design data output by the design platform; selecting and loading the rule set corresponding to the design scheme from the preset data mapping rule library according to the obtained design scheme identifier; traversing each rule in the loaded rule set and checking whether the application condition of the current rule is satisfied; if the application condition of the rule is satisfied, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format; after completing the conversion of all applicable rules, automatically send the finally converted data to the performance verification tool.

[0176] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0177] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0178] In addition, the units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] Furthermore, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0180] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A design method for the dynamic and static rings of a coal mill, which is used to convert the design data of the dynamic and static rings of the coal mill between a design and development platform and a performance verification tool. The method includes: Receiving the design data of the dynamic and static rings output by the design platform; Obtaining the design scheme identifier of the current design according to the received design data of the dynamic and static rings output by the design platform; Selecting and loading the rule set corresponding to the design scheme from a preset data mapping rule library according to the obtained design scheme identifier; Traversing each rule in the loaded rule set and checking whether the application conditions of the current rule are met; If the application conditions of the rule are met, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format; After completing the conversion of all applicable rules, automatically send the finally converted data to the performance verification tool.

2. The design method for the dynamic and static rings of a coal mill according to claim 1, wherein the step of, if the application conditions of the rule are met, executing the conversion function defined in the rule, converting the data format of the design platform into the format required by the performance verification tool, and mapping the converted data to the corresponding fields of the performance verification tool data format includes: If the application conditions of the rule are met, analyze the data field type and value range of the data to be converted; Establish a mapping relationship between the data fields and the conversion functions, and determine the conversion function priority; Match the data fields with the conversion functions according to the data field type, value range, and conversion function priority; Execute the matched conversion function, convert the data format of the design platform into the performance verification tool format, and map the converted data to the performance verification tool fields.

3. The design method for the dynamic and static rings of a coal mill according to claim 2, wherein the step of establishing a mapping relationship between the data fields and the conversion functions and determining the conversion function priority includes: Obtaining the error rate records of each conversion function for the data fields during the historical conversion process; Obtaining the version identifier of the current performance verification tool and matching the conversion function compatibility constraint conditions corresponding to the version identifier; Determining the error transfer weights of each conversion function for the data fields according to the error rate records and the compatibility constraint conditions; Generating a dynamic priority sorting based on the error transfer weights, compatibility constraint conditions, and error rate records; Updating the dynamic priority sorting to the corresponding rule set in the data mapping rule library.

4. The design method for the dynamic and static rings of a coal mill according to claim 3, wherein the step of generating a dynamic priority sorting based on the error transfer weights, compatibility constraint conditions, and error rate records includes: Analyzing the parameter sensitivity distribution in the historical conversion scenario according to the error rate records; Obtaining the constraint type corresponding to the version identifier of the performance verification tool, and establishing an associated mapping between the compatibility constraint conditions and the parameter sensitivity; Generating a multi-dimensional weight distribution coefficient according to the parameter sensitivity distribution, associated mapping, and error transfer weights; Applying the weight distribution coefficient to perform a normalized weighted calculation on the error transfer weights, compatibility constraint conditions, and error rate records to generate a dynamic priority sorting; Associating the dynamic priority sorting with the conversion function identifier in the data mapping rule library.

5. The design method of the dynamic and static rings of a coal mill according to claim 2, the step of mapping the converted data to the fields of the performance verification tool includes: Analyze the data to be mapped, and identify the missing fields and redundant fields; Establish field mapping rules, and define data completion strategies and redundant elimination strategies; According to the field mapping rules, for the identified missing fields, use the preset default values or calculation results based on relevant fields to complete the data, and eliminate the redundant fields; Map the data after completion and elimination processing to the corresponding fields of the performance verification tool.

6. The design method of the dynamic and static rings of a coal mill according to claim 1, the step of obtaining the design scheme identifier of the current design according to the dynamic and static ring design data output by the received design platform includes: Analyze the dynamic and static ring design data output by the design platform, and extract the metadata fields containing the design scheme category information; Establish a design scheme identifier priority judgment rule, including at least one weight factor such as the degree of newness and oldness of the version and the parameter coverage range; According to the priority judgment rule, screen out the most prioritized valid identifier from the extracted metadata fields; According to the most prioritized valid identifier, screen out the relevant rule subset from the preset data mapping rule library; Construct a key parameter feature vector, including the dynamic and static ring geometric parameter range features; Calculate the similarity between the feature vector and the preset identifier definition in the rule subset; Based on the similarity calculation result, select the most matching design scheme identifier as the unique identifier of the current design.

7. The design method of the dynamic and static rings of a coal mill according to claim 6, the step of constructing a key parameter feature vector, including the dynamic and static ring geometric parameter range features, includes: Obtain the current working condition parameters, the material properties of the dynamic and static rings, and the coal mill model data; According to the obtained working condition parameters, the material properties of the dynamic and static rings, and the coal mill model data, determine the sensitivity of each dynamic and static ring geometric parameter to the design scheme identifier; Based on the determined sensitivity, select several dynamic and static ring geometric parameters with the highest sensitivity; Construct a feature vector including the ranges of the selected dynamic and static ring geometric parameters.

8. A design device for the dynamic and static rings of a coal mill, which is used for converting the design data of the dynamic and static rings of the coal mill between a design and development platform and a performance verification tool, is characterized in that, The device includes: A data receiving module, configured to receive the dynamic and static ring design data output by the design platform; An identifier obtaining module, configured to obtain the design scheme identifier of the current design according to the dynamic and static ring design data output by the received design platform; A rule set selection module, configured to select and load the rule set corresponding to the design scheme from the preset data mapping rule library according to the obtained design scheme identifier; A rule traversal module, configured to traverse each rule in the loaded rule set and check whether the application conditions of the current rule are met; A data conversion module, configured to, if the application conditions of the rule are met, execute the conversion function defined in the rule, convert the data format of the design platform into the format required by the performance verification tool, and map the converted data to the corresponding fields of the performance verification tool data format; A data sending module, configured to automatically send the finally converted data to the performance verification tool after completing the conversion of all applicable rules.

9. An electronic device, characterized in that, It includes a processor and a memory, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1-7 are run.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps in the method according to any one of claims 1-7 are run.

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