Method and system for evaluating manufacturing elements in a digital transformation of a ship assembly construction

Abstract

The application discloses a kind of ship assembly construction digital transformation in the evaluation method and system of manufacturing element, which comprises the following steps: constructing four-layer index system for manufacturing element, covering five first-level indexes of digital design, digital supply chain, digital production, digital operation and digital service, and being provided with 14 second-level indexes and 35 third-level indexes;Obtain the original production data of each third-level index;Data is corrected by segmented construction progress deviation;34 ratio type indexes are calculated by percentage, and the pass rate of one threshold type index is calculated based on the current threshold value, and the threshold value is updated adaptively with process level;Standardize the score to the [0,1] interval using a segmented mapping function;Based on the standardized score, generate the digital maturity index of manufacturing element.The application introduces segmented construction progress deviation correction model, segmented mapping function and threshold adaptive algorithm, solves the technical problem that the digital level of manufacturing ring in the ship assembly construction process is difficult to quantify and evaluate, and significantly improves the industry adaptability and accuracy of evaluation.

Description

Technical Field

[0001] This invention belongs to the field of shipbuilding and industrial digitalization assessment, specifically relating to an assessment method and system for manufacturing elements in the digital transformation of ship assembly and construction. Background Technology

[0002] Shipbuilding is a complex production process characterized by multiple stages, multi-disciplinary collaboration, and long cycles. Unlike the assembly-line discrete manufacturing industries such as automobiles and electronics, shipbuilding has the following significant characteristics: parallel construction of sections, simultaneous design and production, high precision control requirements, difficulties in supply chain coordination, complex operation and management, and long service cycles.

[0003] With the rapid development of intelligent manufacturing and industrial internet technologies, shipbuilding companies have gradually introduced digital tools such as 3D design systems (e.g., CAD / CAE / CAM), Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Product Lifecycle Management (PLM). However, existing digital transformation evaluation systems are mostly geared towards general manufacturing designs and have the following technical shortcomings:

[0004] (1) The indicator system lacks adaptability to the shipbuilding industry and has unclear levels: First, existing enterprise digital transformation assessment systems are mostly designed for general manufacturing industries, such as the "Intelligent Manufacturing Capability Maturity Model" (GB / T 39116-2020) and the "Data Management Capability Maturity Assessment Model" (GB / T 36073-2018), whose indicator design does not fully consider the industry characteristics of ship assembly and construction. For example, in the general manufacturing assessment system, production operations are generally divided into two aspects: production planning and production execution, while the actual production operations in shipbuilding need to cover more than 10 stages, including steel yard, pretreatment, material cutting and processing, assembly and fabrication, outfitting and fabrication, pre-outfitting, painting, final assembly and installation, commissioning, and sea trials. Second, existing assessment systems mostly adopt a two-level structure of "first-level indicator - second-level indicator" or "dimension - indicator", which cannot accurately depict the digitalization level of each business link in the shipbuilding industry.

[0005] (2) Failure to consider the progress deviation of segment construction: In shipbuilding, the delay of segment construction is a common problem. Existing assessment methods directly use the original index values ​​without correcting for progress deviations, which leads to distorted assessment results (for example, companies with delayed progress may receive inflated scores because they have completed a small number of high-quality segments).

[0006] (3) Lack of adaptive threshold mechanism: Precision management is a key link in shipbuilding, and its qualified threshold (such as the allowable range of accuracy deviation) needs to be dynamically adjusted as the level of technology improves. Existing methods rely on manual experience to set fixed thresholds, which cannot be updated adaptively.

[0007] (4) The standardization method is too simple: the traditional linear normalization method cannot distinguish the non-linear difference between "below the industry average" and "above the industry average". The same mapping rule is used for positive and negative indicators, and the score gap between excellent companies and ordinary companies in the industry is compressed.

[0008] Therefore, there is an urgent need for a digital evaluation method tailored to the characteristics of the ship assembly and manufacturing process. Summary of the Invention

[0009] The purpose of this invention is to provide a method and system for evaluating manufacturing elements in the digital transformation of ship assembly and construction, which solves the technical problem that it is difficult to quantify and evaluate the level of digitalization in the manufacturing process during ship assembly and construction, and significantly improves the industry adaptability and accuracy of the evaluation.

[0010] The technical solution to achieve the purpose of this invention is as follows:

[0011] A method for evaluating manufacturing elements in the digital transformation of ship assembly and construction, comprising:

[0012] Step 1: Construct a four-level indicator system for manufacturing elements. The indicator system includes evaluation elements, primary indicators, secondary indicators, and tertiary indicators. The evaluation elements are manufacturing elements, and their primary indicators include five dimensions: digital design, digital supply chain, digital production, digital operation, and digital service. Each primary indicator has several secondary indicators, and each secondary indicator has several tertiary indicators.

[0013] Step 2: Obtain the original production data of each of the three-level indicators of the ship assembly and construction enterprise to be evaluated;

[0014] Step 3: Correct the segmented construction progress deviations of the original production data;

[0015] Step 4: Calculate the original score of each tertiary indicator based on the correction value, according to the type of each tertiary indicator.

[0016] Step 5: Use a piecewise mapping function to standardize the original scores of each third-level indicator to the [0,1] interval;

[0017] Step 6: Determine the weights of indicators at each level;

[0018] Step 7: Determine the digital maturity index of the evaluation elements based on the standardized values ​​of the indicator weights and original scores at each level.

[0019] Furthermore, the secondary indicators under the primary indicator of digital design include production design;

[0020] The secondary indicators under the primary indicators of the digital supply chain include procurement and logistics;

[0021] The secondary indicators under the primary indicator of digital production include seven secondary indicators: production management, production operations, safety and environmental protection, quality management, precision management, production support, and warehousing and distribution.

[0022] The secondary indicators under the primary indicators of digital operations include marketing and finance;

[0023] The secondary indicators under the primary indicator of digital services include customer service and product services.

[0024] Furthermore, the tertiary indicators under the secondary indicators of production design include hull design, outfitting design, painting design, and process design;

[0025] The procurement of tertiary indicators under the secondary procurement indicators;

[0026] The logistics secondary indicator is the tertiary indicator under the logistics secondary indicator;

[0027] The tertiary indicators under the secondary indicators of production management include production planning management, material quantity and time management, production preparation management, production operation scheduling, and labor management.

[0028] The tertiary indicators under the secondary indicators of the production operation include steel stockpiling, steel pretreatment, material cutting and processing, assembly and fabrication, outfitting and fabrication, pre-outfitting, painting, final assembly and installation, commissioning and trial sea trials;

[0029] The tertiary indicators under the secondary safety and environmental protection indicators include safety management and environmental protection management;

[0030] The tertiary indicators under the secondary quality management indicators include quality planning and quality inspection;

[0031] The tertiary indicators under the secondary indicators of accuracy management include accuracy planning and accuracy detection;

[0032] The tertiary indicators under the secondary production assurance indicators include energy management and equipment management;

[0033] The tertiary indicators under the secondary indicators of warehousing and distribution include warehousing management and distribution management.

[0034] The marketing tertiary indicators under the marketing secondary indicators;

[0035] The financial secondary indicators are the tertiary indicators under the financial secondary indicators;

[0036] The customer service secondary indicator is the tertiary indicator under the customer service secondary indicator;

[0037] The product service is a tertiary indicator under the secondary indicator of the product service.

[0038] Furthermore, the model for correcting the phased construction schedule deviation in step 3 is as follows:

[0039] ;

[0040] in, This represents the original value of the kth tertiary indicator under the jth secondary indicator of the i-th primary indicator; This refers to the actual construction progress; For the planned construction schedule; This is a positive number set to prevent division by zero errors.

[0041] Furthermore, among the three-level indicators, accuracy detection is a threshold-type indicator, and the original score value is calculated based on the current threshold. The other indicators are ratio-type indicators, and the original score value is calculated based on the completed ratio.

[0042] Furthermore, the original score is calculated based on the current threshold:

[0043] ;

[0044] ;

[0045] in, This is the original score value for accuracy testing. To account for deviations in accuracy detection, The threshold for passing the accuracy test is denoted as , and m is the number of segments involved in this evaluation. This is the pass / fail judgment value.

[0046] Furthermore, the threshold is adaptively updated with the process level.

[0047] ;

[0048] in, This is the accuracy detection pass threshold for the k-th evaluation. denoted as the actual accuracy deviation of the p-th segment; m represents the number of segments involved in this evaluation. This is the learning rate.

[0049] Furthermore, the piecewise mapping function is:

[0050] ;

[0051] in, Let be the original score value of the kth tertiary indicator under the jth secondary indicator of the i-th primary indicator. Baseline value, This is the industry average. As a benchmark value, , is a nonlinear adjustment coefficient. for The standardized value.

[0052] Furthermore, the weights of the indicators at each level are determined using a combination of stratified analysis and entropy weighting:

[0053] ;

[0054] in, The weights were determined using the stratified analysis method. The weights determined by the entropy weight method, This represents the number of secondary indicators under the i-th primary indicator. This represents the number of tertiary indicators under the j-th secondary indicator under the i-th primary indicator.

[0055] An evaluation system for manufacturing elements in the digital transformation of ship assembly and construction, comprising:

[0056] The data acquisition module is used to acquire the original production data of each of the three-level indicators of the ship assembly and construction enterprise to be evaluated;

[0057] The schedule correction module is used to correct deviations in the construction schedule of each segment.

[0058] The scoring calculation module is used to calculate the corrected original score value based on the type of each tertiary indicator.

[0059] The standardization module is used to standardize the scores of each tertiary indicator to the [0,1] interval using a piecewise mapping function;

[0060] The weight determination module is used to determine the weights of indicators at each level;

[0061] The index generation module is used to obtain a digital maturity index of manufacturing factors through step-by-step weighted aggregation. Compared with the prior art, the present invention has the following advantages:

[0062] (1) A four-layer indicator system for ship assembly and construction has been constructed, significantly improving the industry adaptability and granularity of the assessment. This invention, tailored to the characteristics of shipbuilding, pioneered a four-layer structure of "evaluation elements—primary indicators—secondary indicators—tertiary indicators," setting 5 primary indicators, 14 secondary indicators, and 35 tertiary indicators. The secondary indicators precisely correspond to ship-specific business processes, such as production operations being subdivided into more than 10 specific stages from steel yard to sea trials, with core businesses such as precision management and warehousing and distribution all included in the assessment. Compared to existing general assessment systems (typically only 2 layers and 10-20 indicators), this invention offers finer granularity and stronger industry adaptability, accurately identifying the shortcomings in an enterprise's digital transformation.

[0063] (2) By introducing a segmented construction schedule deviation correction model and an adaptive algorithm for accuracy management thresholds, the technical challenges of schedule interference and threshold fixation are solved. On the one hand, this invention proposes a correction model. The indicator values ​​are dynamically adjusted downwards based on the deviation between the actual progress and the planned progress, effectively eliminating the interference of progress lag on the evaluation results. On the other hand, this invention designs a threshold adaptive update algorithm. This allows the pass threshold for precision management to automatically tighten as the process level improves, achieving dynamic optimization of the judgment criteria without frequent manual intervention.

[0064] (3) A piecewise mapping function and a combined weighting method are proposed to achieve more scientific scoring standardization and weight determination. Regarding standardization, this invention uses industry averages... Set a nonlinear adjustment coefficient as the dividing point. (Positive indicators are set at 0.5, and negative indicators at 0.3), making the scoring results more consistent with business understanding—those who score above the average are rewarded, and those who score below the average are penalized, which is superior to the traditional linear normalization method. In terms of weighting, this invention uses a combination of the analytic hierarchy process (AHP) and entropy weighting, multiplying subjective and objective weights and then normalizing the result. This approach takes into account both expert experience and data characteristics, resulting in a more scientific and reasonable determination of weights.

[0065] (4) Achieving hierarchical weighted aggregation and full value chain coverage, forming a logically consistent and complete closed-loop evaluation system. This invention adopts a three-level weighted aggregation model—the three-level indicators are aggregated into the secondary indicator scores, the secondary indicators are aggregated into the primary indicator scores, and the primary indicators are aggregated into the manufacturing factor maturity index, ensuring the consistency of evaluation results at different granular levels. At the same time, 35 tertiary indicators comprehensively cover the entire value chain of shipbuilding, including digital design, digital supply chain, digital production, digital operation, and digital services, forming a complete evaluation closed loop from design to delivery and from procurement to service, supporting hierarchical drill-down analysis from macro indices to micro indicators.

[0066] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0067] Figure 1 This is an overall flowchart of the manufacturing element evaluation method in the digital transformation of ship assembly and construction provided in this embodiment of the invention.

[0068] Figure 2 This is a schematic diagram of the four-layer index system architecture provided in an embodiment of the present invention.

[0069] Figure 3 This is a schematic diagram of the piecewise mapping function curve provided in an embodiment of the present invention.

[0070] Figure 4This is a flowchart of the accuracy management threshold adaptive update process provided in the embodiments of the present invention.

[0071] Figure 5 This is a schematic diagram of the weighted aggregation provided in the embodiment of the present invention.

[0072] Figure 6 This is a schematic diagram of the evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0073] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0074] Combination Figure 1 This embodiment provides a method for evaluating manufacturing elements in the digital transformation of ship assembly and construction, including the following steps:

[0075] Step 1: Construct a four-level indicator system

[0076] This invention constructs a four-layer indicator system oriented towards manufacturing factors. This embodiment takes a large ship assembly and manufacturing enterprise as the evaluation object, and adopts a four-layer structure of "evaluation factor - primary indicator - secondary indicator - tertiary indicator" to construct the manufacturing factor evaluation framework. Combined with... Figure 2 Specifically:

[0077] First layer: Evaluation elements (1), namely: (1) Manufacturing.

[0078] Second level: Primary indicators (5 in total), including:

[0079] (1) Digital design

[0080] (2) Digital supply chain

[0081] (3) Digital production

[0082] (4) Digital Operations

[0083] (5) Digital services

[0084] The third level: secondary indicators (14 in total), including:

[0085] (1) Digital design: production design

[0086] (2) Digital supply chain: procurement and logistics

[0087] (3) Digitalized production: production management, production operations, quality management, precision management, production support, warehousing and distribution, safety and environmental protection.

[0088] (4) Digital Operations: Marketing, Finance

[0089] (5) Digital services: customer service, product services

[0090] Fourth level: Level 3 indicators (35 in total), including:

[0091] (1) Production design includes: hull design (digitalization rate of hull design), outfitting design (digitalization rate of outfitting design), painting design (digitalization rate of painting design), and process design (digitalization rate of process design).

[0092] (2) Procurement includes: procurement (electronic order rate)

[0093] (3) Logistics includes: logistics (logistics in transit visibility rate)

[0094] (4) Production management includes: production planning management (production plan achievement rate), material quantity and time management (digitalization rate of material quantity and time management), production preparation management (production preparation completion rate), production operation scheduling (production scheduling instruction execution rate), and labor management (digitalization rate of labor management).

[0095] (5) Production operations include: steel stockpile (digitalization rate of steel stockpile management), steel pretreatment (digitalization rate of steel pretreatment), material cutting and processing (digitalization rate of material cutting and processing), assembly and fabrication (digitalization rate of assembly and fabrication), outfitting fabrication (digitalization rate of outfitting fabrication), pre-outfitting (pre-outfitting completion rate), painting (digitalization rate of painting operations), final assembly and mounting (accuracy compliance rate of final assembly and mounting), installation and commissioning (completion rate of installation and commissioning), and sea trial (first-pass rate of sea trial).

[0096] (6) Quality management includes: quality planning (quality planning coverage) and quality inspection (quality inspection coverage).

[0097] (7) Precision management includes: precision planning (precision planning coverage) and precision testing (precision testing pass rate).

[0098] (8) Production support includes: energy management (energy management digitization rate) and equipment management (equipment comprehensive utilization rate).

[0099] (9) Warehousing and distribution includes: warehouse management (digitalization rate of warehouse management) and distribution management (pallet integrity delivery rate).

[0100] (10) Safety and environmental protection include: safety management (rectification rate of safety hazards) and environmental management (coverage rate of environmental monitoring).

[0101] (11) Marketing includes: marketing (marketing digitization rate)

[0102] (12) Finance includes: financial (financial digitization rate)

[0103] (13) Customer service includes: customer service (timeliness of customer service response)

[0104] (14) Product services include: product service (completion rate from a digital perspective)

[0105] Step 2, Obtain raw data

[0106] Obtain raw production data for 35 tertiary indicators of the ship assembly and construction company to be evaluated. Data sources include:

[0107] 1) Automatic extraction from enterprise information systems (MES, ERP, etc.);

[0108] 2) Manual data collection and entry;

[0109] 3) Expert scoring;

[0110] Step 3, Correction of Segmented Construction Schedule Deviation

[0111] The original production data is used to correct for segmented construction schedule deviations. The segmented construction schedule deviation correction model is as follows:

[0112]

[0113] in, This represents the original value of the j-th secondary indicator and the k-th tertiary indicator under the i-th primary indicator; This refers to the actual construction progress; For the planned construction schedule; It is a very small positive number, used to prevent division by zero errors.

[0114] For example, a shipbuilding company is building a container ship with 10 sections. As of the assessment date, the planned construction progress was 65%, and the actual construction progress was 55%. The original data collected for the "Production Planning Management" Level 3 indicator was 88%.

[0115] The revised calculation is as follows:

[0116]

[0117] The revised indicator value is 74.47%, lower than the original value of 88%. This indicates that although the production plan for the completed segments is being executed well, the overall construction progress is lagging behind, and production management capabilities should be appropriately reduced.

[0118] Step 4, calculate the original score.

[0119] Different scoring strategies are used depending on the type of the tertiary indicator. In this invention, 34 tertiary indicators are ratio-based, and 1 tertiary indicator (accuracy detection) is threshold-based.

[0120] (1) Ratio-type indicators (34)

[0121] The original rating is a percentage value. The calculation formula is:

[0122] ;

[0123] in, This represents the actual number completed. This represents the total number.

[0124] Typical examples are shown in Table 1.

[0125] Table 1. Examples of Ratio Indicator Calculations

[0126]

[0127] For example, if a ship's hull structure design involves 120 items, and 96 of these items utilize digital design techniques such as 3D digital modeling and drawing, then the original score for the third-level indicator "Hull Design Digitalization Rate" is:

[0128]

[0129] (2) Threshold-type indicators (1)

[0130] The accuracy detection level 3 indicator is a threshold-type indicator and needs to be compared with the dynamic pass threshold:

[0131] Step 41: Collect the actual accuracy deviation of each segment. , This represents the absolute value of the deviation, calculated as the actual value minus the absolute value of the standard value.

[0132] Step 42: Based on the current threshold Determining compliance:

[0133] ;

[0134] Step 43: Calculate the pass rate:

[0135] ;

[0136] For example, to calculate the raw score of the "accuracy detection" level 3 indicator, the current threshold is required. The actual accuracy deviation values ​​for the 10 segments collected are:

[0137]

[0138] According to the qualification judgment ( If the deviation values ​​are within acceptable limits, then 8 segment deviation values ​​are acceptable, and 2 segment deviation values ​​are unacceptable. The pass rate is calculated to be 80%.

[0139] Step 5, Segmented Mapping Standardization

[0140] The scores of each tertiary indicator are standardized to the [0,1] interval using a piecewise mapping function, such as... Figure 3 As shown, the piecewise mapping function is:

[0141]

[0142] in, Let be the original score value of the kth tertiary indicator under the jth secondary indicator of the i-th primary indicator. for The standardized value, Baseline value, This is the industry average. As a benchmark value, This is a non-linear adjustment coefficient (0.5 for positive indices and 0.3 for negative indices).

[0143] Taking "digitalization rate of hull design" as an example, a positive indicator The parameters are set as follows: Original score value baseline value industry average Benchmark value Judgment interval: Use the following formula:

[0144]

[0145] The traditional formula for linear normalization is:

[0146]

[0147] The traditional linear normalization result is 0.486, and the piecewise mapping score is 0.55, reflecting the reward for the "above the industry average" indicator.

[0148] Step 6, Accuracy Management Threshold Adaptive Update

[0149] For the third-level accuracy inspection indicator under the second-level accuracy management indicator, its pass threshold needs to be dynamically adjusted as the process level improves. An adaptive threshold update algorithm is adopted:

[0150]

[0151] in, This is the accuracy detection pass threshold for the k-th evaluation. denoted as the actual accuracy deviation of the p-th segment; m represents the number of segments involved in this evaluation. This is the learning rate.

[0152] Combination Figure 4 The algorithm flow is as follows:

[0153] Step 61: Initialize the threshold (Based on industry standards or historical data);

[0154] Step 62: During the Kth evaluation, collect the actual accuracy deviation of m segments. ;

[0155] Step 63: Calculate the mean deviation for this test:

[0156]

[0157] Step 64: Update the threshold:

[0158]

[0159] Step 65: Boundary Constraints

[0160]

[0161] Assuming the current threshold This assessment collected accuracy deviation data for 8 segments: Set the learning rate ; Calculate the mean deviation: Update threshold: Due to the actual accuracy deviation average Higher than the current threshold The threshold was raised to This indicates that the level of precision management needs to be improved, and the standards should be appropriately relaxed.

[0162] Step 7: Determine the weights by combining weights

[0163] The weights for each level are determined by a combination of Analytic Hierarchy Process (AHP) and entropy weighting:

[0164] AHP method: Constructing a judgment matrix based on expert experience and calculating subjective weights. ;

[0165] Entropy weight method: Calculating objective weights based on the dispersion of actual data. ;

[0166] Combined weighting method:

[0167]

[0168] Taking the "precision management" secondary indicator as an example, its weight values ​​are shown in Table 2.

[0169] Table 2, Data Table for Accuracy Management Scoring

[0170]

[0171] Similarly, the scores for the other 6 secondary indicators are calculated, as shown in Table 3.

[0172] Table 3, Data Table of Scores for the Other 6 Secondary Indicators

[0173]

[0174] Taking the seven secondary indicators under the primary indicator of digital production as an example:

[0175] AHP method for determining subjective weights:

[0176]

[0177] Entropy weight method for determining objective weights:

[0178]

[0179] Combination weighting:

[0180]

[0181] The combined weight of precision management is the highest (0.5391), reflecting the dual characteristics of this indicator: large dispersion in actual data and high expert importance evaluation.

[0182] Step 8, weighted summarization at each level

[0183] like Figure 5 A three-level weighted aggregation method is adopted, aggregating data from the three-level teacher index upwards to the manufacturing factor maturity index:

[0184] The third-level indicators are summarized into the second-level indicator scores: ;

[0185] Secondary indicators are summarized into primary indicator scores: ;

[0186] The primary indicators are summarized as the manufacturing factor index: .

[0187] The secondary indicators are summarized into the primary indicator scores, as shown in Table 4.

[0188] Table 4 summarizes the secondary indicators into primary indicator scores.

[0189]

[0190] The primary indicators are summarized as the Manufacturing Factor Maturity Index, as shown in Table 5.

[0191] Table 5 summarizes the primary indicators as the Manufacturing Factor Maturity Index.

[0192]

[0193] Step 9, Output the evaluation results: Output the digital maturity index of manufacturing factors. And generate scoring reports for each level of indicators.

[0194] like Figure 6 As shown, this embodiment also provides an evaluation system for manufacturing elements in the digital transformation of ship assembly and construction, including:

[0195] The data acquisition module is used to acquire the original production data of each tertiary indicator of the ship assembly and construction enterprise to be evaluated; it connects to the enterprise's MES, ERP and other systems through the API interface to automatically collect the original production data of 35 tertiary indicators, and supports batch import (Excel / CSV format).

[0196] The schedule correction module is used to perform segmented construction schedule deviation correction; it obtains the planned and actual progress of segmented construction and performs segmented construction schedule deviation correction calculation;

[0197] The scoring calculation module is used to calculate the corrected original score value based on the type of each tertiary indicator.

[0198] The standardization module is used to standardize the scores of each tertiary indicator to the [0,1] interval using a piecewise mapping function;

[0199] The weight determination module is used to determine the weights of indicators at each level;

[0200] The index generation module is used to obtain a digital maturity index of manufacturing factors through step-by-step weighted aggregation.

[0201] This invention solves the technical problem of difficulty in quantifying and assessing the digitalization level of the manufacturing process in the ship assembly and construction process by introducing a segmented construction progress deviation correction model, segment mapping function and threshold adaptive algorithm, which significantly improves the industry adaptability and accuracy of the assessment.

[0202] The embodiments described above are merely one implementation method of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for evaluating manufacturing elements in the digital transformation of ship assembly and construction, characterized in that, include: Step 1: Construct a four-level indicator system for manufacturing elements. The indicator system includes evaluation elements, primary indicators, secondary indicators, and tertiary indicators. The evaluation elements are manufacturing elements, and their primary indicators include five dimensions: digital design, digital supply chain, digital production, digital operation, and digital service. Each primary indicator has several secondary indicators, and each secondary indicator has several tertiary indicators. Step 2: Obtain the original production data of each of the three-level indicators of the ship assembly and construction enterprise to be evaluated; Step 3: Correct the segmented construction progress deviations of the original production data; Step 4: Calculate the original score of each tertiary indicator based on the correction value, according to the type of each tertiary indicator. Step 5: Use a piecewise mapping function to standardize the original scores of each third-level indicator to the [0,1] interval; Step 6: Determine the weights of indicators at each level; Step 7: Determine the digital maturity index of the evaluation elements based on the standardized values ​​of the indicator weights and original scores at each level.

2. The evaluation method according to claim 1, characterized in that: The secondary indicators under the primary indicator of digital design include production design; The secondary indicators under the primary indicators of the digital supply chain include procurement and logistics; The secondary indicators under the primary indicator of digital production include seven secondary indicators: production management, production operations, safety and environmental protection, quality management, precision management, production support, and warehousing and distribution. The secondary indicators under the primary indicators of digital operations include marketing and finance; The secondary indicators under the primary indicator of digital services include customer service and product services.

3. The evaluation method according to claim 2, characterized in that: The tertiary indicators under the secondary indicators of production design include hull design, outfitting design, painting design, and process and method design. The procurement of tertiary indicators under the secondary procurement indicators; The logistics secondary indicator is the tertiary indicator under the logistics secondary indicator; The tertiary indicators under the secondary indicators of production management include production planning management, material quantity and time management, production preparation management, production operation scheduling, and labor management. The tertiary indicators under the secondary indicators of the production operation include steel stockpiling, steel pretreatment, material cutting and processing, assembly and fabrication, outfitting and fabrication, pre-outfitting, painting, final assembly and installation, commissioning and trial sea trials; The tertiary indicators under the secondary safety and environmental protection indicators include safety management and environmental protection management; The tertiary indicators under the secondary quality management indicators include quality planning and quality inspection; The tertiary indicators under the secondary indicators of accuracy management include accuracy planning and accuracy detection; The tertiary indicators under the secondary production assurance indicators include energy management and equipment management; The tertiary indicators under the secondary indicators of warehousing and distribution include warehousing management and distribution management. The marketing tertiary indicators under the marketing secondary indicators; The financial secondary indicators are the tertiary indicators under the financial secondary indicators; The customer service secondary indicator is the tertiary indicator under the customer service secondary indicator; The product service is a tertiary indicator under the secondary indicator of the product service.

4. The evaluation method according to claim 2, characterized in that: The model for correcting the phased construction schedule deviation in step 3 is as follows: ； in, This represents the original value of the kth tertiary indicator under the jth secondary indicator of the i-th primary indicator; This refers to the actual construction progress; For the planned construction schedule; This is a positive number set to prevent division by zero errors.

5. The evaluation method according to claim 3, characterized in that: Among the three-level indicators, accuracy detection is a threshold-based indicator, and the original score is calculated based on the current threshold. The other indicators are ratio-based indicators, and the original score is calculated based on the completed ratio.

6. The evaluation method according to claim 5, characterized in that: The original score is calculated based on the current threshold: ; ; in, This is the original score value for accuracy testing. To account for deviations in accuracy detection, The threshold for passing the accuracy test is denoted as , and m is the number of segments involved in this evaluation. This is the pass / fail judgment value.

7. The evaluation method according to claim 6, characterized in that: The threshold is adaptively updated with process level. ； in, This is the accuracy detection pass threshold for the k-th evaluation. denoted as the actual accuracy deviation of the p-th segment; m represents the number of segments involved in this evaluation. This is the learning rate.

8. The evaluation method according to claim 1, characterized in that: The piecewise mapping function is: ； in, Let be the original score value of the kth tertiary indicator under the jth secondary indicator of the i-th primary indicator. Baseline value, This is the industry average. As a benchmark value, , is a nonlinear adjustment coefficient. for The standardized value.

9. The evaluation method according to claim 2, characterized in that: The weights of the indicators at each level are determined using a combination of stratified analysis and entropy weighting: ； in, The weights were determined using the stratified analysis method. The weights determined by the entropy weight method, This represents the number of secondary indicators under the i-th primary indicator. This represents the number of tertiary indicators under the j-th secondary indicator under the i-th primary indicator.

10. An evaluation system for manufacturing elements in the digital transformation of ship assembly and construction, characterized in that, The evaluation method according to any one of claims 1-9 includes: The data acquisition module is used to acquire the original production data of each of the three-level indicators of the ship assembly and construction enterprise to be evaluated; The schedule correction module is used to correct deviations in the construction schedule of each segment. The scoring calculation module is used to calculate the corrected original score value based on the type of each tertiary indicator. The standardization module is used to standardize the scores of each tertiary indicator to the [0,1] interval using a piecewise mapping function; The weight determination module is used to determine the weights of indicators at each level; The index generation module is used to obtain a digital maturity index of manufacturing factors through step-by-step weighted aggregation.

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