A method and system for assessing the maturity of digital transformation of shipbuilding equipment

Abstract

This invention discloses a method and system for assessing the digital transformation maturity of shipbuilding equipment. The method includes constructing a digital transformation maturity assessment model based on the inherent characteristics of shipbuilding equipment; determining the weights of various components in the assessment model according to the role of the assessed equipment in the shipbuilding process; acquiring assessment data; verifying the data; calculating scores based on the assessment model; and determining the maturity assessment result. This method accurately matches the characteristics of shipbuilding equipment, solving the technical problem of insufficient adaptability of general assessment models. Furthermore, the weights are dynamically adjusted according to equipment category, making the classification assessment more scientific. It also innovatively introduces a "multimodal data acquisition + on-site full-link verification" mechanism, significantly improving the accuracy and reliability of the assessment results.

Description

Technical Field

[0001] This invention belongs to the field of digital transformation assessment, specifically relating to a method and system for assessing the maturity of digital transformation of ship manufacturing equipment. Background Technology

[0003] Shipbuilding equipment is characterized by its massive size and heavy load-bearing capacity, enabling it to handle the massive structural operations required for shipbuilding; its highly customized design and process adaptability to meet the requirements of different ship types and construction; and its need for stable operation in harsh conditions and environments, such as coastal high humidity, high salt spray, and open-air operations. Although China's shipbuilding industry ranks first globally in terms of total output, the digital foundation of its manufacturing equipment remains relatively weak, and the industry is at a critical stage of transitioning from "automation" to "digitalization." Currently, most shipyards' key process equipment is still primarily based on single-machine automation, resulting in a widespread "data silo" phenomenon: although some equipment has data acquisition capabilities, the lack of unified data standards and open interfaces makes it difficult to achieve interconnectivity between workshop management systems, process design systems, and enterprise-level platforms.

[0004] Existing methods for assessing the maturity of digital transformation are mostly based on general standards (such as GB / T 39116). Their indicators lack the granularity to accurately depict the digital requirements of ship equipment under extreme operating conditions. Furthermore, the assessment process relies on manual questionnaires and document review, lacking empirical verification of the data flow between systems. The assessment results are highly subjective, disconnected from reality, and unsuitable for effectively guiding enterprises to accurately diagnose shortcomings and plan upgrade paths. Therefore, there is an urgent need for a quantifiable method for assessing the maturity of digital transformation, tailored to the characteristics of ship manufacturing equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for assessing the maturity of digital transformation of ship manufacturing equipment, aiming to solve problems such as the disconnect between existing assessment models and the characteristics of ship equipment, strong subjectivity in assessment, and difficulty in quantifying results.

[0006] The specific technical solution for achieving the objective of this invention is as follows:

[0007] A method for assessing the maturity of digital transformation of shipbuilding equipment includes the following steps:

[0008] Step 1: Based on the analysis of the inherent characteristics of shipbuilding equipment, construct a maturity assessment model for the digital transformation of shipbuilding equipment.

[0009] Step 2: Determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process;

[0010] Step 3: Obtain evaluation data and verify it;

[0011] Step 4: Calculate the score based on the assessment model and determine the maturity assessment result.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0013] (1) The present invention constructs a "four-dimensional" maturity model for shipbuilding equipment when constructing the evaluation model. The model is highly specialized and accurately matches the characteristics of ship equipment. Existing evaluation methods are mostly based on general standards such as GB / T 39116. Their index granularity cannot cover the special requirements of ship equipment under extreme working conditions. The four dimensions (equipment basic digitalization, data intelligence application, business value realization, and sustainable development) in the maturity evaluation model constructed in the present invention accurately correspond to the four core characteristics of ship equipment—giant system and extreme working conditions, strong customization and process binding, data silos and system breakpoints, and long cycle and green requirements. Among them, dimension A is for the data collection and interconnection capabilities of equipment, dimension B focuses on the softwareization of process knowledge and adaptive optimization, dimension C evaluates multi-scenario adaptation and business effectiveness, and dimension D considers green and low-carbon and long-term sustainability, which comprehensively solves the technical problem of insufficient adaptability of general evaluation models.

[0014] (2) The weight evaluation method proposed in this invention is dynamically adjusted according to the equipment category, making the classification evaluation more scientific. Existing methods usually use fixed weights or simple averages, which are difficult to reflect the differentiated evaluation focus of different types of equipment. This invention divides the equipment into five categories according to its role in the shipbuilding process: core process equipment, heavy lifting and transportation equipment, auxiliary and testing equipment, production line workstation systems, and general basic equipment, and uses a weight allocation function. The weights of each dimension are dynamically determined, enabling adaptive matching between the evaluation focus and equipment characteristics;

[0015] (3) This invention adopts a field end-to-end verification method to ensure objective and truthful assessment. Traditional assessments rely on manual questionnaires and document review, which can lead to discrepancies between theory and practice. This invention innovatively introduces a "multimodal data acquisition + field end-to-end verification" mechanism: data is collected through automated, semi-automated, and manual modes, and a mandatory end-to-end data flow connectivity test is conducted to verify the entire process from the upper management system issuing instructions to the equipment execution feedback. Manual intervention points and data delays are recorded, and data quality evaluation rules are established. This quality control mechanism upgrades the assessment basis from subjective questionnaires to objective operational data and empirical evidence of system interoperability, significantly improving the accuracy and credibility of the assessment results.

[0016] The present invention will be further described below with reference to specific embodiments. Attached Figure Description

[0017] Figure 1This is a schematic diagram of the digital transformation maturity assessment method for ship manufacturing equipment according to the present invention. Detailed Implementation

[0018] Example

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0022] Combination Figure 1 A method for assessing the maturity of digital transformation of ship manufacturing equipment includes the following steps:

[0023] Step 1: Based on the analysis of the inherent characteristics of shipbuilding equipment, a digital transformation maturity assessment model for shipbuilding equipment is constructed. This solution deeply analyzes the industry essence of shipbuilding equipment and identifies four core characteristics: the coupling between mega-systems and extreme working conditions, the deep binding of strong customization and process knowledge, the prevalence of data silos and system breakpoints, and the urgent need for long-term and green sustainable development. Finally, the technical defects of existing assessment methods are identified: the model dimensions are missing and it is out of touch with industry characteristics; the assessment mechanism is subjective and lacks empirical verification; the weights are fixed and singular, ignoring equipment differences. Based on this, the model constructed in this embodiment includes multiple assessment dimensions, namely: equipment basic digitalization and system integration capabilities (dimensional A), data-driven and intelligent application capabilities (dimensional B), business scenario adaptation and value realization capabilities (dimensional C), and sustainable and compatible development capabilities (dimensional D). These four dimensions constitute a complete assessment closed loop from equipment basics to business value and long-term sustainability.

[0024] The specific indicators for the four dimensions will be explained in detail below:

[0025] (1) Equipment basic digitalization and system integration capabilities A, including:

[0026] A1 equipment data acquisition and numerical control ratio is used to evaluate the automatic acquisition coverage and data quality of key process parameters:

[0027]

[0028] in, To achieve the number of parameters automatically collected, The total number of preset key parameters;

[0029] The A2 standard protocol interface coverage is used to evaluate the equipment's compatibility with standard industrial protocols such as OPC UA and Modbus TCP, as well as its compliance with the shipyard's unified interface specifications.

[0030]

[0031] in, To support the number of interfaces for standard protocols, Total number of interfaces

[0032] A3 Security Policy Completeness Rate:

[0033]

[0034] in, The number of security policy items implemented. This represents the total number of preset security policy items;

[0035] A4 system inter-system data exchange success rate:

[0036]

[0037] in, The number of data interactions successfully completed within the specified time. The total number of data interactions initiated within a specified time period;

[0038] (2) Data-driven and intelligent application capabilities B, including:

[0039] The proportion of B1 process knowledge digitized in software is evaluated based on the completeness and ease of use of expert process parameter packages and typical process path databases.

[0040]

[0041] in, This represents the number of typical operating condition parameter combinations that have been implemented with software. This represents the total number of typical operating condition parameter combinations;

[0042] B2 Process parameter self-adjustment success rate, evaluating the process parameter self-adjustment capability and online quality judgment and early warning function based on real-time data:

[0043]

[0044] in, The number of times to successfully adapt and adjust. This represents the total number of disturbances.

[0045] B3 Predictive Maintenance and Health Management: Evaluate the accuracy of critical component life prediction and the decline in the rate of sudden failure. The prediction accuracy is:

[0046]

[0047] in, To accurately predict the number of failures, This represents the total number of predictions.

[0048] B4 Digital Twin Application Error Level: Evaluating the synchronization accuracy between the physical entity and the virtual entity of the equipment, as well as the richness of the simulation application scenarios.

[0049]

[0050] in, For the number of test time points, This represents the three-dimensional coordinate vector of the key points of the equipment in space. For the pose of a physical entity, The pose of the virtual model;

[0051] (4) Business scenario adaptation and value realization capabilities C, including:

[0052] C1 The business scenario coverage rate of flexible equipment is used to evaluate the equipment's adaptability to typical shipbuilding scenarios. For flexible equipment, scenario coverage rate is used:

[0053]

[0054] in, The number of business scenarios already covered by flexible equipment, This represents the total number of typical scenario-based services.

[0055] For specialized equipment, calculate the operational stability index of the scenario:

[0056]

[0057] in, This refers to the number of exceptions / interruptions that occurred during operation in a specific scenario. This refers to the total number of runs or the total number of job batches in this scenario. Total downtime due to equipment malfunction or abnormality; This represents the total planned runtime for this scenario.

[0058] C2 Process Adaptability: Evaluates the equipment's adaptability to different materials, bevels, and processes, as well as its switching efficiency. Process switching efficiency:

[0059]

[0060] in, For a successful switch, Total number of switching operations;

[0061] C3 Operational Efficiency Improvement Rate (Personnel Empowerment and Usability): Evaluates the availability of AR, VR, and visual interface auxiliary tools and the degree to which operator skill requirements are reduced.

[0062]

[0063] in, As the baseline operation time, Current operation time

[0064] C4 business performance is quantitatively improved by evaluating the quantifiable business results after equipment investment, such as shorter production cycles, higher first-pass yield, and lower costs, using the investment payback period as a metric.

[0065]

[0066] in, For equipment investment and operation and maintenance costs, This represents an increase in annualized business revenue.

[0067] (4) Sustainable and compatible development capability D, including:

[0068] D1 Green and Low-Carbon Level: Evaluates equipment energy efficiency rating, material environmental friendliness, and carbon emission intensity. Carbon emission intensity:

[0069]

[0070] in, Carbon emissions over the life cycle Output per unit;

[0071] D2 System Compatibility and Open Ecosystem: Evaluating the completeness of open APIs, third-party application integration capabilities, and ecosystem cooperation, using open interface completeness as a metric.

[0072]

[0073] in, To the number of standard APIs provided, Total number of standard APIs;

[0074] D3 Long-term Sustainability: Evaluates the foresight of the technological roadmap, the cost-effectiveness of upgrades, and the completeness of lifecycle management, characterized by the upgrade cost-effectiveness ratio.

[0075]

[0076] in, The increased benefits brought about by the upgrade Costs were incurred for the upgrade.

[0077] Step 2: Determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process:

[0078] Based on their role in the shipbuilding process, the assessed equipment is categorized into core working equipment, heavy lifting and transportation equipment, auxiliary and testing equipment, production line workstation systems, and general basic equipment, and then weighted according to a weighting function. The evaluation weights for each dimension are dynamically determined, among which... For equipment category identification, Weight assignment function;

[0079]

[0080] The equipment feature adjustment function fine-tunes the base weights based on the equipment feature P:

[0081] ,

[0082] Feature parameters Including but not limited to: equipment complexity, investment scale, frequency of use, safety level, and technological innovation.

[0083] The calibration function for evaluation dimension i is used to calibrate the weights in a specific evaluation scenario.

[0084]

[0085] For equipment category The corresponding base weights, Corresponding to different equipment categories The corresponding base weights, These correspond to multiple evaluation dimensions in the evaluation model; For equipment feature adjustment function, The number of feature parameters, Features Evaluation Dimensions The influence coefficient; Indicates the first The normalization function of each feature parameter limits the range of output values ​​to 1. between; This represents the calibration function for evaluating dimension i. The calibration coefficient representing assessment dimension i can be determined by a combination of the following factors: corporate strategic priorities, corporate policy orientation, and specific assessment objectives.

[0086] In this embodiment, Corresponding to different equipment categories The corresponding basic weight values ​​are as follows:

[0087]

[0088] The final weights are then normalized.

[0089] , .

[0090] Step 3: Obtain evaluation data and verify it;

[0091] When acquiring evaluation data, multiple modes of collection are used, including automated collection, semi-automated collection, and manual collection. Automated collection can automatically obtain real-time and historical data such as equipment operating status, process parameters, and alarm logs through device IoT gateways, OPC UA servers, or API interfaces. Semi-automated collection can use mobile terminal APPs to scan codes to enter inspection data and maintenance records, or use RPA tools to export reports from old systems. Manual collection is for information that cannot be automatically obtained. Structured questionnaires are designed and filled out through interviews, observations, document reviews, etc., and a double-check mechanism is implemented.

[0092] Next, end-to-end data flow connectivity testing is conducted to verify the entire process from the upper-level management system to the equipment execution and feedback. Manual intervention points and data latency are recorded. In actual operation, upper-level management systems such as MES, PLM, and WMS can be logged in to simulate typical tasks and verify data flow and business connectivity, including:

[0093] Task issuance verification: Test the automatic reception and parsing capability of production instructions issued from the upper management system to the target equipment;

[0094] Execution feedback verification: After the test equipment completes its operation, it automatically provides feedback to the management system on the completeness and real-time nature of the progress and results;

[0095] Anomaly handling verification: Test the data caching, retransmission and recovery mechanisms under abnormal conditions such as network interruption and equipment failure.

[0096] In addition, after collecting the evaluation data, it is also necessary to conduct a quality assessment of the data, that is, to verify the completeness, accuracy, and timeliness of the collected data:

[0097]

[0098] in, For data missing rate, For data error rate, For data latency, The corresponding weighting coefficients and satisfying ;

[0099] If the quality evaluation score If the data is below the set threshold, it cannot be used for evaluation and data needs to be collected again.

[0100] Step 4: Calculate the score based on the assessment model and determine the maturity assessment result:

[0101]

[0102] in, For the overall score, The dimension weights are satisfied. , For dimension The number of indicators below For the first Dimension 1 The score of each indicator item (in this embodiment, regardless of whether the indicators calculated above are decimals or percentages, they are all calculated in a unified manner).

[0103] Then, based on the evaluation model score, according to the quantization mapping relationship Determine the maturity level of equipment digital transformation:

[0104]

[0105] in, In this embodiment, the maturity levels of equipment digital transformation are defined as follows:

[0106] Basic Automation Level: Possesses stand-alone automation and basic data acquisition capabilities;

[0107] Digital connectivity level: Devices are connected to the network, data can be centrally monitored, and remote program distribution can be achieved;

[0108] System integration level: Integrates with systems such as MES, enabling data-driven production operations;

[0109] Intelligent optimization level: Based on data and models, it realizes self-optimization of process parameters, quality prediction, and preventive maintenance;

[0110] Ecosystem-leading level: Equipment becomes a node in networked collaborative manufacturing, supporting business model innovation.

[0111] This embodiment combines a maturity assessment of a portable welding robot for a shipyard, which is a core process equipment.

[0112] Scoring rules: Each indicator item is scored out of 5 (1-5 points), corresponding to a qualitative description of the maturity level. The average score of each dimension and the overall average score are calculated, and the overall level is determined based on the comprehensive scoring principle.

[0113] The scoring results of the portable welding robot were obtained through trial evaluation:

[0114]

[0115] Level determination analysis:

[0116] 1) Overall score: 3.14 points, between 3 and 4 points

[0117] 2) Final Level: L3 (System Integration Level)

[0118] The accuracy of the assessment results can be determined by the actual location of the equipment in the shipyard.

[0119] Analysis of the evaluation results in the table above leads to the following conclusion: Dimension B (data-driven and intelligent application capabilities) scored 2.8 points, significantly lower than other dimensions, representing a key weakness that drags down the overall maturity level. This result is highly consistent with the current state of the industry.

[0120] In contrast, many welding robots in domestic shipyards can only perform teach programming and simple data acquisition, lacking data-driven process self-optimization and predictive maintenance capabilities. This results in welding quality relying excessively on operator experience, and equipment failures being unpredictable. The assessment results accurately reflect this actual pain point.

[0121] Meanwhile, the assessment result was Level 3 (3.14 points), indicating that the equipment has achieved system integration and is among the top performers in the industry. However, it still lags significantly behind true intelligent optimization (Level 4). The company should focus its resources on intelligent transformation in the B dimension as the core task for the next stage.

[0122] In addition, this solution also provides a maturity assessment system for the digital transformation of shipbuilding equipment, including the following modules:

[0123] Modeling module: Used to build a maturity assessment model for the digital transformation of shipbuilding equipment based on the analysis of the inherent characteristics of shipbuilding equipment;

[0124] Weight determination module: used to determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process;

[0125] Data acquisition module: Used to acquire and verify evaluation data;

[0126] Assessment module: Used to calculate scores based on the assessment model and determine maturity assessment results.

[0127] In addition, this solution also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0128] Step 1: Based on the analysis of the inherent characteristics of shipbuilding equipment, construct a maturity assessment model for the digital transformation of shipbuilding equipment.

[0129] Step 2: Determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process;

[0130] Step 3: Obtain evaluation data and verify it;

[0131] Step 4: Calculate the score based on the assessment model and determine the maturity assessment result.

[0132] In addition, this solution also provides a computer-storable medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, performs the following steps:

[0133] Step 1: Based on the analysis of the inherent characteristics of shipbuilding equipment, construct a maturity assessment model for the digital transformation of shipbuilding equipment.

[0134] Step 2: Determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process;

[0135] Step 3: Obtain evaluation data and verify it;

[0136] Step 4: Calculate the score based on the assessment model and determine the maturity assessment result.

[0137] 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 assessing the maturity of digital transformation of shipbuilding equipment, characterized in that, Includes the following steps: Step 1: Based on the analysis of the inherent characteristics of shipbuilding equipment, construct a maturity assessment model for the digital transformation of shipbuilding equipment. Step 2: Determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process; Step 3: Obtain evaluation data and verify it; Step 4: Calculate the score based on the assessment model and determine the maturity assessment result.

2. The method for assessing the maturity of digital transformation of shipbuilding equipment according to claim 1, characterized in that, The maturity assessment model for the digital transformation of shipbuilding equipment in step 1 includes multiple assessment dimensions: (1) Basic digitalization and system integration capabilities of equipment, including: Equipment data acquisition and numerical control ratio: ； in, To achieve the number of parameters automatically collected, The total number of preset key parameters; Standard protocol interface coverage: ； in, To support the number of interfaces for standard protocols, Total number of interfaces Security policy completeness rate: ； in, The number of security policy items implemented. This represents the total number of preset security policy items; Inter-system data interaction success rate: ； in, The number of data interactions successfully completed within a specified time. The total number of data interactions initiated within a specified time period; (2) Data-driven and intelligent application capabilities, including: The proportion of process knowledge digitized in software: ； in, This represents the number of typical operating condition parameter combinations that have been implemented with software. This represents the total number of typical operating condition parameter combinations; Success rate of process parameter self-adjustment: ； in, The number of times to successfully adapt and adjust. This represents the total number of disturbances. Accuracy of critical component life prediction: ； in, To accurately predict the number of failures, This represents the total number of predictions. Errors in digital twin applications: ； in, For the number of test time points, This represents the three-dimensional coordinate vector of the key points of the equipment in space. For the pose of a physical entity, The pose of the virtual model; (4) Business scenario adaptation and value realization capabilities, including: Business scenario coverage of flexible equipment: ； in, The number of business scenarios already covered by flexible equipment, This represents the total number of typical scenario-based services. Stability index of specialized equipment in various operating scenarios: ； in, This refers to the number of exceptions / interruptions that occurred during operation in a specific scenario. This refers to the total number of runs or the total number of job batches in this scenario. Total downtime due to equipment malfunction or abnormality; This represents the total planned runtime for this scenario. Process adaptability: Evaluate the equipment's adaptability to different materials, bevels, and processes, as well as its switching efficiency. Process switching efficiency: ； in, For a successful switch, Total number of switching operations; Operational efficiency improvement rate: Evaluate the availability of AR, VR, and visual interface-assisted tools and the degree to which the skill requirements for operators are reduced. ； in, As the baseline operation time, Current operation time Quantitative improvement in business performance is represented by the investment payback period: ； in, For equipment investment and operation and maintenance costs, This represents an increase in annualized business revenue. (4) Sustainable and compatible development capabilities, including: Carbon emission intensity: ； in, Carbon emissions over the life cycle Output per unit; System compatibility and open ecosystem are characterized by the completeness of open interfaces: ； in, To the number of standard APIs provided, Total number of standard APIs; Upgrade cost-effectiveness ratio: ； in, The increased benefits brought about by the upgrade Costs were incurred for the upgrade.

3. The method for assessing the maturity of digital transformation of shipbuilding equipment according to claim 2, characterized in that, The determination of weights in step 2 is specifically as follows: Based on their role in the shipbuilding process, the assessed equipment is categorized into core working equipment, heavy lifting and transportation equipment, auxiliary and testing equipment, production line workstation systems, and general basic equipment, and then weighted according to a weighting function. The evaluation weights for each dimension are dynamically determined, among which... For equipment category identification, Weight assignment function; ； ， ； ； For equipment category The corresponding base weights, Corresponding to different equipment categories The corresponding base weights, These correspond to multiple evaluation dimensions in the evaluation model; For equipment feature adjustment function, The number of feature parameters, Features Evaluation Dimensions The influence coefficient; Indicates the first The normalization function of each feature parameter limits the range of output values ​​to 1. between; This represents the calibration function for evaluating dimension i. Represents the calibration coefficient for evaluation dimension i; The final weights are then normalized. ， 。 4. The method for assessing the maturity of digital transformation of shipbuilding equipment according to claim 2, characterized in that, In step 3, evaluation data is collected using multiple modes, and end-to-end data flow connectivity testing is performed to verify the entire process from the upper-level management system to equipment execution and feedback. Manual intervention points and data delays are recorded, including: Task issuance verification: Test the automatic reception and parsing capability of production instructions issued from the upper management system to the target equipment; Execution feedback verification: After the test equipment completes its operation, it automatically provides feedback to the management system on the completeness and real-time nature of the progress and results; Anomaly handling verification: Test the data caching, retransmission and recovery mechanisms under abnormal conditions such as network interruption and equipment failure.

5. The method for assessing the maturity of digital transformation of shipbuilding equipment according to claim 2, characterized in that, After collecting the evaluation data in step 3, the data quality is assessed: ； in, For data missing rate, For data error rate, For data latency, The corresponding weighting coefficients and satisfying ; If the quality evaluation score If the data is below the set threshold, it cannot be used for evaluation and data needs to be collected again.

6. The method for assessing the maturity of digital transformation of shipbuilding equipment according to claim 2, characterized in that, The calculation of the score based on the evaluation model in step 4 is specifically as follows: ； in, For the overall score, The dimension weights are satisfied. , For dimension The number of indicators below For the first Dimension 1 The score of each indicator item; Then, based on the evaluation model score, according to the quantization mapping relationship Determine the maturity level of equipment digital transformation: ； in, The maturity level of equipment digital transformation.

7. A digital transformation maturity assessment system for shipbuilding equipment, characterized in that, Includes the following modules: Modeling module: Used to build a maturity assessment model for the digital transformation of shipbuilding equipment based on the analysis of the inherent characteristics of shipbuilding equipment; Weight determination module: used to determine the weights of each item in the evaluation model based on the role of the equipment being evaluated in the shipbuilding process; Data acquisition module: Used to acquire and verify evaluation data; Assessment module: Used to calculate scores based on the assessment model and determine maturity assessment results.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-storable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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