Air conditioner heat exchanger multi-source corrosion data hierarchical management method

Abstract

The present application belongs to the technical field of material corrosion science and industrial big data management, and provides a multi-source corrosion data hierarchical management method for air conditioner heat exchangers, which specifically comprises the following steps: obtaining a multi-source corrosion data set; constructing a data hierarchical evaluation index system; establishing data hierarchical determination rules; performing data hierarchical division; establishing hierarchical data management strategies; establishing a hierarchical data calling mechanism; constructing a hierarchical data management system; and finally constructing a complete hierarchical data management system with engineering application capability. The present application has the beneficial effect of overcoming the drawbacks of relying on subjective experience in traditional methods, achieving precise configuration of data resources, significantly improving the reliability and engineering applicability of corrosion evaluation and prediction results, effectively improving the generalization ability and robustness of the model in complex environments, and providing a high-quality data basis for intelligent operation and life cycle management of air conditioning equipment.

Description

Technical Field

[0001] This invention belongs to the field of materials corrosion science and industrial big data management technology, specifically relating to a method for quality assessment and hierarchical management of multi-source corrosion data of heat exchangers in air conditioning equipment under complex service environments. This invention is particularly applicable to corrosion state analysis, lifespan prediction model construction, and equipment lifecycle health management based on multi-source heterogeneous data. Background Technology

[0002] The core heat exchange components of air conditioning equipment, including evaporators and condensers, inevitably suffer from the combined effects of multiple environmental factors such as high temperature, high humidity, high salt spray, and atmospheric pollutants during long-term service, leading to corrosion failure. This, in turn, causes a series of problems such as decreased heat exchange efficiency, increased operating energy consumption, and shortened equipment lifespan. To accurately assess the corrosion behavior and service condition of heat exchangers, engineering practice typically requires the comprehensive use of data from multiple sources, mainly including: accelerated corrosion test data from laboratories, natural environment exposure test data, and operational and environmental data collected during actual equipment operation.

[0003] However, the three types of data mentioned above differ significantly in their data characteristics and engineering value. Laboratory data, obtained through precise control of environmental parameters, possesses high controllability and a high signal-to-noise ratio, revealing the intrinsic mechanisms of corrosion; however, it inherently deviates from real, complex service environments. Natural environment data originates from long-term exposure tests at field stations, accurately reflecting the corrosion behavior of materials under specific conditions; however, its acquisition cycle is long, data dispersion is large, and it is difficult to cover the dynamic operating conditions of equipment. Actual operating data comes from the equipment's built-in IoT sensors, characterized by massive volume, high dynamism, and high complexity, best reflecting the performance evolution of equipment under real operating conditions; however, this type of data often suffers from high noise, data gaps, and inconsistent formats.

[0004] Existing technologies generally employ two approaches when processing multi-source corrosion data: one is to treat data from different sources as a single entity for unified analysis and modeling; the other is to perform simple classification processing, but lacks a scientific and quantitative grading standard and differentiated management strategy. This approach leads to ambiguity in the credibility, applicable scenarios, and weight allocation of different data types during data application, failing to effectively utilize the core advantages of various data types and struggling to avoid their inherent defects. Consequently, it severely impacts the accuracy of corrosion assessment results, the generalization ability of prediction models, and the actual effectiveness of engineering applications.

[0005] Therefore, there is an urgent need to establish a hierarchical management method for multi-source corrosion data of air conditioning heat exchangers, so as to realize the rational application and accurate support of multi-source heterogeneous data through scientific evaluation and hierarchical division of data quality. Summary of the Invention

[0006] The purpose of this invention is to overcome the technical shortcomings of existing technologies, such as the mixed use of multi-source corrosion data, the inability to quantify quality differences, and the unclear application scenarios. This invention provides a hierarchical management method for multi-source corrosion data in air conditioning heat exchangers. This method aims to construct a scientific, multi-dimensional evaluation index system to quantitatively assess and hierarchically classify corrosion data from different sources, and establish differentiated management strategies and access mechanisms, thereby achieving scientific management, precise application, and maximizing the value of multi-source data.

[0007] To achieve the above-mentioned objectives, this invention provides a hierarchical management method for multi-source corrosion data in air conditioning heat exchangers. This method follows a technical route of "data acquisition—indicator evaluation—rule construction—hierarchical classification—strategy formulation—mechanism establishment—system integration," and specifically includes the following steps: S1: Obtain the multi-source corrosion dataset First, acquire multi-source data related to the corrosion behavior of air conditioning heat exchangers. These data sources include at least three categories: the first is laboratory accelerated corrosion test data, obtained by simulating the dynamic coupling of multiple environmental factors, aiming to reveal the corrosion mechanism; the second is natural environment exposure test data, obtained through long-term material exposure at typical natural environment test stations, used to verify corrosion behavior under real-world conditions; and the third is actual equipment operation data, collected through the air conditioning equipment's built-in IoT sensors and external environmental monitoring networks, reflecting the equipment's operating status and performance evolution under real-world conditions.

[0008] S2: Constructing a data hierarchical evaluation index system To scientifically assess the quality characteristics of data from different sources, this invention constructs a hierarchical evaluation index system comprising three core dimensions, specifically including: Data controllability indicators are used to characterize the degree of controllability of environmental parameters and experimental conditions during data acquisition. Laboratory data typically has high controllability because environmental parameters can be precisely adjusted and stably controlled; while the environmental conditions of actual operational data are uncontrollable, resulting in lower controllability.

[0009] Data authenticity index: used to characterize the ability of data to reflect the corrosion behavior of materials under real service environments. Natural environment exposure data and actual operation data are derived from real environments and have high authenticity; while laboratory data, due to idealization and simplification, have relatively lower authenticity.

[0010] Data integrity metrics: These metrics characterize the continuity and completeness of data records in time and space. They can be quantitatively assessed through metrics such as data missing rate and sampling frequency stability.

[0011] S3: Establish data classification and determination rules Based on the above three evaluation indicators, a clear set of data classification rules is constructed. This invention divides corrosion data into three levels: Level 1 data (high-value foundational data): refers to data that demonstrates excellent overall performance across the three dimensions of controllability, completeness, and authenticity, especially with extremely high controllability indicators. This type of data is primarily used to reveal corrosion mechanisms, construct fundamental physical models, and serve as a standardized benchmark for comparison.

[0012] Level 2 data (high-value calibration data): This refers to data that performs well in terms of authenticity and completeness, and has moderate controllability. This type of data is mainly used to validate and calibrate models built on Level 1 data, making them closer to real-world environments.

[0013] Level 3 data (high-value application data): This refers to data that performs exceptionally well in terms of accuracy indicators, but has lower controllability, and its completeness may fluctuate or have high noise levels. This type of data originates directly from actual operating conditions and is mainly used for status analysis, trend prediction, and operation and maintenance decisions in engineering scenarios.

[0014] S4: Execution Data Hierarchy Division Based on the judgment rules established in step S3, each piece of data or each dataset obtained in step S1 is evaluated one by one and assigned a corresponding hierarchical label (level 1, level 2 or level 3), thereby forming a hierarchical dataset with hierarchical identification.

[0015] S5: Establish a hierarchical data management strategy Establish differentiated management strategies and application guidelines for different levels of data: For Level 1 data, the highest standards of standardized management are implemented to ensure its traceability and reproducibility. It is primarily used for quantitative analysis of fundamental patterns, standardized comparisons, and basic pre-training of large-scale models.

[0016] For secondary data, the focus is on managing its correlation with primary data. It is primarily used for sensitivity analysis of environmental factors, model calibration and validation, and as a bridge connecting laboratory research and engineering applications.

[0017] For Level 3 data, the focus is on managing its dynamism and timeliness. It is primarily used in real-world engineering scenarios for status monitoring, fault diagnosis, performance evaluation, and as a core data source for fine-tuning and continuous optimization of large models.

[0018] S6: Establish a hierarchical data access mechanism To support efficient engineering applications, a data retrieval mechanism based on hierarchical tags should be established. This mechanism should include at least the following rules: Scenario matching rules: Based on the needs of specific application tasks (such as mechanism research, model calibration, state prediction), the data level with the highest matching degree is called first.

[0019] Priority rules: When multiple levels of data are available, a clear priority order for calling is set according to the different requirements of the task for accuracy, real-time performance, and generalization.

[0020] Joint Invocation Rules: Supports extracting information from different levels of data according to task needs, and conducting joint analysis and comprehensive application. For example, using first-level data to ensure the basic accuracy of the model, while using third-level data to capture the dynamic changes of actual working conditions.

[0021] S7: Construct a hierarchical data management system Based on the above steps, a complete hierarchical data management system with engineering application capabilities is finally constructed. The core components of this system include: a unified data hierarchical tag library, a standardized data retrieval rule library, and a data application strategy library for different application scenarios, thereby realizing the scientific management of multi-source corrosive data throughout its entire lifecycle.

[0022] The present invention has the following significant beneficial effects: It achieves scientific quantification of data quality: For the first time, a multi-dimensional evaluation index system based on controllability, authenticity and integrity is proposed, which provides an objective and quantifiable evaluation standard for the quality differences of multi-source corrosive data and overcomes the drawbacks of relying on subjective experience judgment in traditional methods.

[0023] A hierarchical data classification system was established: by dividing data into three levels—basic, calibration, and application—the core value and best application scenarios of different types of data in corrosion analysis and model building were clarified, and the precise allocation of data resources was achieved.

[0024] Improved engineering reliability of data applications: Through hierarchical management and differentiated access, the interference of low-quality data on analysis results was effectively avoided. At the same time, the unique advantages of various types of data were fully utilized, significantly improving the reliability and engineering applicability of corrosion assessment and prediction results.

[0025] It supports iterative optimization of complex models: providing an ideal data architecture for building large models through "step-by-step training". Level 1 data is used for basic pre-training, level 2 data is used for environmental calibration, and level 3 data is used for fine-tuning and continuous optimization under actual working conditions, thereby effectively improving the model's generalization ability and robustness in complex environments.

[0026] It enhances the intelligence level of equipment operation and maintenance: Based on the hierarchical data management system, it can more scientifically support the condition assessment, life prediction and early warning decision-making of heat exchangers, and provide a high-quality data foundation for the intelligent operation and maintenance and full life cycle management of air conditioning equipment. Attached Figure Description

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall process of the multi-source corrosion data hierarchical management method described in this invention; Figure 2 This is a schematic diagram of the data hierarchy structure described in this invention, illustrating the logical relationship between the three evaluation dimensions of data controllability, authenticity, and integrity and the three data levels; Figure 3 This is a schematic diagram of the hierarchical data management mechanism described in this invention, illustrating the collaborative working relationship between hierarchical labels, management strategies, and invocation rules. Detailed Implementation

[0028] The present invention will be further explained below with reference to specific implementation schemes, but it is not limited to the present invention. The structures, proportions, sizes, etc. shown in the accompanying drawings are only used to complement the content disclosed in the specification, so as to enable those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0029] This embodiment takes the outdoor unit heat exchanger (condenser) of a certain model of split air conditioner as an example. The heat exchanger adopts a copper tube and aluminum fin structure.

[0030] Step S1: Obtain the multi-source corrosion dataset First, obtain three types of data related to the corrosion of the aforementioned heat exchanger: Laboratory data: Accelerated corrosion tests were conducted on standard aluminum fin samples in a circulating salt spray test chamber to obtain electrochemical impedance spectroscopy, corrosion weight loss, and surface morphology image data under different corrosion cycles.

[0031] Natural environment data: Samples from the same batch were placed at the Wanning Natural Environment Test Station for a 12-month exposure test. Corrosion weight loss data of the samples were collected periodically, and environmental parameters such as temperature, humidity, and chloride ion deposition at the station were recorded simultaneously.

[0032] Actual operating data: The air conditioning equipment with the same model of heat exchanger was operated in Wanning for one year, and its operating data was collected, including compressor frequency, inlet and outlet air temperature difference, energy consumption, and the temperature and humidity of the micro-environment around the equipment.

[0033] Step S2: Construct a data hierarchical evaluation index system A quality assessment was conducted on the above three types of data: Data controllability indicators: The environmental parameters (temperature, humidity, salt spray concentration) of laboratory data are precisely controlled by the test chamber, with extremely high controllability (assessed as 10 / 10); the exposure environment of natural environment data is determined by natural conditions, with moderate controllability (assessed as 5 / 10); the environmental conditions of actual operation data are entirely determined by the weather, with extremely low controllability (assessed as 2 / 10).

[0034] Data authenticity indicators: Laboratory data is simplified compared to the real environment, with moderate authenticity (5 / 10); Natural environment data accurately reflects local atmospheric corrosion behavior, with high authenticity (9 / 10); Actual operation data comes directly from the equipment and can reflect the coupled impact of corrosion on performance, with extremely high authenticity (10 / 10).

[0035] Data integrity indicators: Laboratory data is collected according to the predetermined cycle, with no missing data, and has high integrity (missing rate <1%); natural environment data is collected quarterly, with time intervals, and has moderate integrity (missing rate about 5%); actual operation data has some missing data due to occasional sensor failures (missing rate about 8%).

[0036] Steps S3 and S4: Establish grading determination rules and perform grading division. Based on preset indicator thresholds, the above data is classified into different levels: Laboratory data, due to its high controllability and integrity, is classified as Level 1 data. Its core value lies in revealing the electrochemical behavior and corrosion kinetics of aluminum fins under typical corrosive environments.

[0037] Natural environmental data, due to its high fidelity and moderate completeness, is classified as secondary data. Its core value lies in validating and calibrating corrosion models based on laboratory data, making them more consistent with real marine atmospheric environments.

[0038] Due to its extremely high level of authenticity, actual operating data, even with lower controllability and slightly insufficient completeness, is still classified into three levels of data. Its core value lies in analyzing the impact of corrosion on the actual heat exchange efficiency and energy consumption of heat exchangers, and in using it for condition early warning under actual operating conditions.

[0039] Step S5: Establish a hierarchical data management strategy The primary data is stored in a dedicated, high-quality database using a standardized format, and is used to construct a fundamental kinetic model for atmospheric corrosion of aluminum alloys.

[0040] Secondary data are linked to primary data through an index to validate and correct the environmental factor impact coefficients in the base model.

[0041] The Level 3 data is integrated into the dynamic data platform to monitor the performance degradation trend of the heat exchanger in real time, and, combined with the basic thresholds provided by the Level 1 data, to issue early warnings for abnormal conditions.

[0042] Step S6: Establish a hierarchical data retrieval mechanism In practical applications, if it is necessary to analyze the "influence of salt spray concentration on corrosion rate", the experimental data of control variables in the primary data should be used first. If it is necessary to evaluate the "performance degradation of air conditioners in Wanning area after one year of actual operation", the tertiary data should be used first, and the background information of the long-term environment of the region should be provided in conjunction with the secondary data.

[0043] Step S7: Construct a hierarchical data management system Through the above steps, a complete system comprising hierarchical data tags, management strategies, and retrieval rules is ultimately formed. This system provides clear and reliable data support for heat exchanger corrosion status assessment, lifespan prediction, and intelligent operation and maintenance decisions.

[0044] Matters not covered in this invention are common knowledge.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers, characterized in that, Includes the following steps: S1: Obtain a multi-source corrosion dataset, wherein the data sources include at least laboratory accelerated corrosion test data, natural environment exposure test data, and actual equipment operation data; S2: Construct a data hierarchical evaluation index system, which includes at least a data controllability index for characterizing the controllability of the data acquisition process, an authenticity index for characterizing the ability of the data to reflect the real service environment, and a data integrity index for characterizing the continuity and integrity of data records. S3: Establish data classification and judgment rules, and divide the data into first-level data, second-level data and third-level data based on the evaluation index system; S4: According to the data classification judgment rules, the acquired multi-source corrosion data is classified and assigned corresponding classification labels to form a classification dataset; S5: Establish differentiated hierarchical data management strategies for different levels of data, and clarify the core application scenarios and usage guidelines for various types of data; S6: Establish a hierarchical data access mechanism, which includes data level access rules that match the application scenario, multi-level data access priority rules, and rules that support multi-level data joint access; S7: Based on the aforementioned hierarchical labels, management strategies, and invocation mechanisms, construct a complete hierarchical management system for corrosion data.

2. The method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers according to claim 1, characterized in that, The data controllability index mentioned in step S2 is evaluated based on the adjustable range and stability of environmental parameters during data acquisition; the data authenticity index is evaluated based on the degree of similarity between the data source environment and the actual service environment; and the data integrity index is evaluated based on the data missing rate or sampling continuity.

3. The method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers according to claim 1 or 2, characterized in that, The data classification and determination rules mentioned in step S3 are as follows: data that meets the high controllability characteristics are classified as first-level data, data with high authenticity characteristics are classified as third-level data, and data with good authenticity and moderate controllability are classified as second-level data.

4. The method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers according to claim 1, characterized in that, The differentiated management strategy described in step S5 includes: using primary data for basic corrosion law analysis and standardized comparison; using secondary data for environmental impact law verification and model calibration; and using tertiary data for state analysis and prediction applications in actual engineering scenarios.

5. The method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers according to claim 1, characterized in that, The hierarchical data retrieval mechanism described in step S6 supports prioritizing the retrieval of the data level that best matches the application scenario based on specific application needs, or performing multi-level data fusion analysis according to preset priorities during joint retrieval.

6. The method for hierarchical management of multi-source corrosion data for air conditioning heat exchangers according to claim 1, characterized in that, The primary data corresponds to laboratory accelerated corrosion test data, the core value of which lies in revealing the corrosion mechanism and supporting the pre-training of the basic model; the secondary data corresponds to natural environment exposure test data, the core value of which lies in calibrating the model's performance in real environments; and the tertiary data corresponds to actual equipment operation data, the core value of which lies in supporting the deployment and optimization of the model under real working conditions.

7. A multi-source corrosion data hierarchical management system for air conditioning heat exchangers, characterized in that, include: The index evaluation module is used to perform the operation of step S2 in claim 1 to evaluate the acquired multi-source corrosion data in multiple dimensions. The grading determination module is used to perform the operations of steps S3 and S4 in claim 1, and to classify and label the data based on the evaluation results. The data management module is used to perform the operations of steps S5 and S6 in claim 1, store hierarchical data tags, and manage and output data according to preset management strategies and calling rules.

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