Refrigeration Machine Room Equipment Performance Model Health Assessment Method, System, Medium and Terminal

Abstract

The present application provides a method, system, medium and terminal for evaluating the health of a refrigeration machine room equipment performance model. By monitoring the real-time operation data, historical operation data and equipment performance prediction data generated during the operation of the refrigeration machine room, according to the prediction accuracy of the equipment performance model, and using multi-modal fusion technology, combined with the current operation status, environmental parameters and equipment aging conditions, the weights of different input dimensions in the health calculation model are dynamically adjusted, realizing the adaptive health evaluation of the equipment performance model, so as to be able to more comprehensively and accurately evaluate the model health, effectively reduce the error of a single data source, and then calculate the comprehensive health score of the most representative equipment performance model. And when the comprehensive health score of the equipment performance model is lower than the preset health threshold, the model retraining is automatically triggered, realizing the adaptive adjustment of the model to ensure that the equipment performance model is always in the optimal state.

Description

Technical Field

[0001] This application relates to the technical field of industrial refrigeration, and particularly to a method, system, medium, and terminal for evaluating the health of equipment performance models in a refrigeration machine room. Background Art

[0002] In the operation and management of an efficient industrial refrigeration machine room, chillers, pumps, and cooling towers, as the core equipment of the refrigeration system, their operating states and performances are directly related to the energy efficiency and stability of the entire system. To maximize the operating efficiency of these devices and ensure their long-term stable operation, it has become a common practice in the industry to model their operating data to fit the equipment performance curves. Such modeling not only helps predict the operating trends of the equipment but also provides a scientific basis for equipment maintenance and optimization.

[0003] However, in the current field of monitoring the health of equipment performance models, there are still many challenges and deficiencies. Most traditional monitoring methods focus on the monitoring of a single performance parameter, such as energy consumption, temperature difference, or operating efficiency, and use the anomalies of these indicators to indirectly reflect the health of the model. However, this single-dimensional evaluation method ignores various factors that may affect the accuracy of the model, such as changes in environmental temperature, the aging of the equipment itself, and the dynamic fluctuations of operating conditions. Therefore, it is often difficult to obtain comprehensive and accurate evaluation results by simply monitoring a single parameter.

[0004] Periodic calibration is another commonly used means for evaluating model health. By periodically recalibrating the equipment performance model, the deviation of the model caused by environmental factors or equipment aging can be corrected to a certain extent. However, the limitation of this method is that it cannot reflect the changes in the health status of the equipment during dynamic operation in real time, resulting in a certain lag in the evaluation results and making it difficult to guide equipment maintenance and optimization work in a timely manner.

[0005] Manual maintenance, as an auxiliary means, usually involves intervention by operation and maintenance personnel to make adjustments or retraining when the model shows obvious deviations. However, this method is not only inefficient but also unable to achieve the adaptive adjustment of the model, making it difficult to cope with complex and changing operating environments. In addition, over-reliance on manual maintenance will increase operating costs and reduce the overall reliability and stability of the system.

[0006] More critically, existing methods often ignore the importance of environmental and equipment aging factors in evaluating model health. The environmental conditions where the equipment is located, service life, and degree of equipment aging will all have a significant impact on the accuracy of the model. However, in the current evaluation system, these factors have not been fully considered and quantified, resulting in incomplete and inaccurate evaluation results.

[0007] In summary, the existing methods for evaluating the health of equipment performance models have deficiencies in terms of comprehensiveness, real-time performance, and accuracy, and cannot accurately reflect the health status of equipment performance models. Summary of the Invention

[0008] In view of the above-mentioned shortcomings of the prior art, the purpose of this application is to provide a method, system, medium, and terminal for evaluating the health of a refrigeration plant equipment performance model, which are used to solve the technical problems existing in the existing methods for evaluating the health of equipment performance models, such as relying on the monitoring of a single performance parameter, being unable to reflect the changes in the health status of equipment during dynamic operation in real time, and being unable to achieve the adaptive adjustment of the model.

[0009] To achieve the above purpose and other related purposes, the first aspect of this application provides a method for evaluating the health of a refrigeration plant equipment performance model, including: collecting real-time operation data and historical operation data generated during the operation of the refrigeration plant; respectively preprocessing the collected real-time operation data and historical operation data; evaluating the prediction accuracy of a pre-constructed equipment performance model according to the preprocessed real-time operation data and the preprocessed historical operation data; adjusting the weights in the pre-constructed health calculation model according to the prediction accuracy of the equipment performance model and based on the multi-modal fusion technology; calculating the comprehensive health score of the equipment performance model according to the health calculation model with adjusted weights.

[0010] In some embodiments of the first aspect of this application, the calculation formula of the health calculation model includes:

[0011] ;

[0012] where H represents the comprehensive health score of the equipment performance model; i represents different equipment operation parameters in the refrigeration plant; w i represents the operation coefficient weight corresponding to different equipment operation parameters in the refrigeration plant; β i represents the sensitivity coefficient weight corresponding to different equipment operation parameters in the refrigeration plant; T i represents the preprocessed real-time operation data; P i represents the equipment performance prediction data; γ(E) represents the equipment aging weight function; n represents the total number of different equipment operation parameters in the refrigeration plant; exp represents the exponential function.

[0013] In some embodiments of the first aspect of the present application, the method for adjusting the weights in the pre-constructed health calculation model according to the prediction accuracy of the device performance model and based on the multi-modal fusion technology includes: based on the multi-modal fusion technology, adjusting the operation coefficient weights corresponding to different device operation parameters in the refrigeration machine room, the sensitivity coefficient weights corresponding to different device operation parameters in the refrigeration machine room, and the device aging weight function respectively.

[0014] In some embodiments of the first aspect of the present application, the historical operation data includes one or a combination of operation parameter data, device aging data, and operation environment data; the method for adjusting the device aging weight function includes: evaluating the aging degree of different devices in the refrigeration machine room according to the operation parameter data, device aging data, and operation environment data, and based on the pre-constructed device aging model; adjusting the device aging weight function by using the multiple regression analysis technology according to the aging degree of different devices in the refrigeration machine room.

[0015] In some embodiments of the first aspect of the present application, the method further includes: comparing the comprehensive health score of the device performance model with a preset health threshold, and determining whether the device performance model needs to be retrained according to the comparison result.

[0016] In some embodiments of the first aspect of the present application, the method for evaluating the prediction accuracy of the pre-constructed device performance model includes: comparing the pre-processed real-time operation data with the training data for constructing the device performance model based on the data offset detection algorithm, and calculating the offset value between the pre-processed real-time operation data and the training data; evaluating the prediction accuracy of the pre-constructed device performance model according to the offset value between the pre-processed real-time operation data and the training data.

[0017] In some embodiments of the first aspect of the present application, the method for evaluating the prediction accuracy of the pre-constructed device performance model includes: obtaining device performance prediction data according to the pre-processed real-time operation data and based on the pre-constructed device performance model; calculating the deviation value between the pre-processed real-time operation data and the device performance prediction data; calculating the fitting error of the device performance model according to the deviation value between the pre-processed real-time operation data and the device performance prediction data and through the curve fitting technology; evaluating the prediction accuracy of the pre-constructed device performance model according to the fitting error of the device performance model.

[0018] To achieve the above object and other related objects, a second aspect of the present application provides a health assessment system for the performance model of refrigeration plant equipment, including: a data acquisition module for acquiring real-time operation data and historical operation data generated during the operation of the refrigeration plant; a data preprocessing module for preprocessing the acquired real-time operation data and historical operation data respectively; a model accuracy evaluation module for evaluating the prediction accuracy of a pre-constructed equipment performance model according to the preprocessed real-time operation data and the preprocessed historical operation data; a weight adjustment module for adjusting the weights in the pre-constructed health calculation model according to the prediction accuracy of the equipment performance model and based on the multi-modal fusion technology; and a health evaluation module for calculating the comprehensive health score of the equipment performance model according to the health calculation model with adjusted weights.

[0019] To achieve the above object and other related objects, a third aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned health assessment method for the performance model of refrigeration plant equipment is implemented.

[0020] To achieve the above object and other related objects, a fourth aspect of the present application provides an electronic terminal, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the above-mentioned health assessment method for the performance model of refrigeration plant equipment.

[0021] As described above, the health assessment method, system, medium, and terminal for the performance model of refrigeration plant equipment of the present application have the following beneficial effects: By monitoring the real-time operation data, historical operation data, and equipment performance prediction data generated during the operation of the refrigeration plant, according to the prediction accuracy of the equipment performance model, and using the multi-modal fusion technology, combining the current operation status, environmental parameters, and equipment aging situation, the weights of different input dimensions in the health calculation model are dynamically adjusted, realizing the adaptive health assessment of the equipment performance model, so as to be able to more comprehensively and accurately evaluate the model health, effectively reduce the error of a single data source, and further calculate the most representative comprehensive health score of the equipment performance model. And when the comprehensive health score of the equipment performance model is lower than the preset health threshold, the model re-training is automatically triggered, realizing the adaptive adjustment of the model to ensure that the equipment performance model is always in the optimal state, enabling the equipment performance model to comprehensively evaluate the current operation state and future operation state of the equipment, so as to be able to accurately evaluate the health of the refrigeration plant in the dynamic operation process in real time, providing strong support for the operation optimization of the refrigeration plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1It shows a schematic flowchart of a method for evaluating the health of a refrigeration plant equipment performance model in an embodiment of the present application.

[0023] Figure 2 It shows a schematic flowchart of a method for evaluating the prediction accuracy of an equipment performance model in an embodiment of the present application.

[0024] Figure 3 It shows another schematic flowchart of a method for evaluating the prediction accuracy of an equipment performance model in an embodiment of the present application.

[0025] Figure 4 It shows a schematic block diagram of a system for evaluating the health of a refrigeration plant equipment performance model in an embodiment of the present application.

[0026] Figure 5 It shows a schematic structural diagram of an electronic terminal in an embodiment of the present application. Detailed implementation manners

[0027] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0028] Existing methods for evaluating the health of equipment performance models have deficiencies in terms of comprehensiveness, real-time performance, and accuracy, and cannot accurately reflect the health status of equipment performance models. To solve the problems in the above background technology, the present application provides a method, system, medium, and terminal for evaluating the health of a refrigeration plant equipment performance model, which are used to solve technical problems such as the existing methods for evaluating the health of equipment performance models relying on single performance parameter monitoring, being unable to reflect the changes in the health status of equipment during dynamic operation in real time, and being unable to achieve adaptive adjustment of the model.

[0029] To facilitate the understanding of the embodiments of the present application, first in combination with Figure 1 Detailed description is given. Figure 1 It shows a schematic flowchart of a method for evaluating the health of a refrigeration plant equipment performance model in an embodiment of the present invention. The method for evaluating the health of a refrigeration plant equipment performance model in this embodiment mainly includes the following steps:

[0030] S101: Collect real-time operation data and historical operation data generated during the operation of the refrigeration plant.

[0031] In this embodiment, real-time operation data and historical operation data of equipment such as chillers, pumps, and cooling towers in the refrigeration machine room are collected. The real-time operation data can be used to reflect the current operation state of the refrigeration machine room, and the historical operation data is stored in the database and can be used for long-term trend analysis and comparison. Various sensors are installed in the refrigeration machine room, including temperature sensors, humidity sensors, energy consumption metering devices, and operation state monitoring devices, etc. Through the sensors, operation data such as temperature, humidity, energy consumption, pressure, flow rate, and equipment operation state of the refrigeration machine room can be collected in real time. At the same time, the data transmission function built into the equipment in the refrigeration machine room can also output operation data, such as data on equipment efficiency, water volume, etc. The various operation data are stored in the data center to facilitate calling for analysis and judging the state of the current refrigeration machine room.

[0032] S102: Preprocess the collected real-time operation data and historical operation data respectively.

[0033] In this embodiment, the collected real-time operation data and historical operation data are preprocessed respectively. The preprocessing methods include but are not limited to: data cleaning preprocessing, data outlier preprocessing, data normalization preprocessing, data type conversion preprocessing, etc.

[0034] In this embodiment, the data cleaning preprocessing is to delete duplicate data, fill in missing values, etc., to ensure the integrity and accuracy of the data.

[0035] In this embodiment, the data outlier preprocessing is to identify and process outliers (such as extremely large or extremely small values) in the data. It can be detected and processed by setting thresholds, using statistical methods (such as the 3σ principle) or machine learning algorithms. The set threshold is to set reasonable upper and lower limit thresholds according to the characteristics of the data and business requirements, and regard the data exceeding the threshold as outliers. Use statistical methods (such as the 3σ principle) to detect and process outliers. The 3σ principle means that the probability that the data value is within the range of the mean plus or minus 3 times the standard deviation is 99.73%, and the data outside this range can be regarded as outliers. Use machine learning algorithms (such as Isolation Forest) to automatically detect and process outliers. This method can adapt to the dynamic changes of the data and improve the accuracy and efficiency of outlier detection.

[0036] In this embodiment, the data normalization preprocessing is to scale the data to a specific range so that data of different magnitudes can be compared and comprehensively analyzed. For the key operation parameters of equipment such as chillers, pumps, and cooling towers collected in real time, since their numerical ranges and units may be different, normalization processing is required.

[0037] In this embodiment, the data type conversion preprocessing refers to converting different data types in the original data into a format or type suitable for analysis and modeling before data analysis and modeling.

[0038] S103: Evaluate the prediction accuracy of a pre-constructed device performance model based on the preprocessed real-time operation data and the preprocessed historical operation data.

[0039] In this embodiment, as Figure 2 shown, it is a schematic flowchart for evaluating the prediction accuracy of a device performance model in an embodiment of the present invention. The methods for evaluating the prediction accuracy of a pre-constructed device performance model include:

[0040] S1031: Based on a data offset detection algorithm, compare the preprocessed real-time operation data with the training data for constructing the device performance model, and calculate the offset value between the preprocessed real-time operation data and the training data.

[0041] In this embodiment, the device performance model is trained using historical operation data stored in a data center and is used to predict the operating condition of the device. By comparing the preprocessed real-time operation data with the training data for constructing the device performance model, the offset value between the preprocessed real-time operation data and the training data can be obtained, thereby evaluating the offset degree between the preprocessed real-time operation data and the training data.

[0042] In this embodiment, the data offset detection algorithm includes: KL divergence algorithm, Mahalanobis distance algorithm, etc. The KL divergence algorithm (Kullback-Leibler Divergence) is an algorithm for measuring the difference between two probability distributions. First, convert the preprocessed real-time operation data and the model training data into the form of probability distributions. Then, compare the probability distribution of the preprocessed real-time operation data (denoted as P) with the probability distribution of the model training data (denoted as Q), and calculate the KL divergence value. The KL divergence value represents the degree of difference between the two data distributions, thereby obtaining the offset value between the preprocessed real-time operation data and the training data. If the KL divergence value is small, it indicates that the distribution of the device real-time operation data is similar to the distribution of the model training data, and the model has good generalization ability. If the KL divergence value is large, it indicates that there is a large difference between the two data distributions, which may be caused by changes in the device state, interference from environmental factors, or insufficient model training, etc.

[0043] In this embodiment, the Mahalanobis distance algorithm is an effective algorithm for measuring the similarity of multi-dimensional space data points. First, the means and covariance matrices of the pre-processed real-time operation data and model training data are calculated respectively, which are the basis for calculating the Mahalanobis distance. Then, for each data point in the pre-processed real-time operation data, the Mahalanobis distance between it and the mean of the model training data is calculated. The obtained Mahalanobis distance value reflects the statistical distance between the pre-processed real-time operation data point and the mean of the model training data, thereby obtaining the offset value between the pre-processed real-time operation data and the training data. If the distance value is small, it indicates that the distribution of the data point is similar to that of the model training data, and the model has good predictive ability for this data point. If the distance value is large, it indicates that there is a large difference in the distribution between the data point and the model training data, which may be caused by changes in equipment status, interference from environmental factors, etc.

[0044] S1032: Evaluate the prediction accuracy of the pre-constructed equipment performance model according to the offset value between the pre-processed real-time operation data and the training data.

[0045] In this embodiment, if the offset value between the pre-processed real-time operation data and the training data exceeds the preset offset threshold, it means that the prediction accuracy of the equipment performance model has decreased, and the equipment performance cannot be accurately predicted based on the real-time operation data. The model health score will decrease, and it is necessary to re-collect training data and train the model to adapt to the new environment. If the offset value between the pre-processed real-time operation data and the training data does not exceed the preset offset threshold, it means that the prediction accuracy of the equipment performance model remains within an acceptable range, and the model can accurately predict the equipment performance based on the real-time operation data, and the health of the model is good.

[0046] In this embodiment, as Figure 3 shown, it is another flowchart showing the evaluation of the prediction accuracy of the equipment performance model in the embodiment of the present invention. The methods for evaluating the prediction accuracy of the pre-constructed equipment performance model include:

[0047] S1031A: Obtain equipment performance prediction data according to the pre-processed real-time operation data and based on the pre-constructed equipment performance model.

[0048] In this embodiment, the pre-processed real-time operation data is applied to the pre-constructed equipment performance model. By inputting the pre-processed real-time operation data into the equipment performance model, equipment performance prediction data is obtained.

[0049] S1032B: Calculate the deviation value between the preprocessed real-time operation data and the device performance prediction data.

[0050] In this embodiment, by calculating the difference or a certain distance metric between the two, the deviation value between the preprocessed real-time operation data and the device performance prediction data is obtained. The deviation value can be a scalar (representing the magnitude of the overall deviation) or a vector (representing the deviation of each data point).

[0051] S1033C: According to the deviation value between the preprocessed real-time operation data and the device performance prediction data, and through curve fitting technology, calculate the fitting error of the device performance model.

[0052] In this embodiment, the curve fitting technology is a mathematical method for finding a curve that best describes a set of data points. Using the preprocessed real-time operation data as the independent variable (input) and the device performance prediction data as the dependent variable (output), and finding the best fitting curve through curve fitting technology. The fitting error is a measure of the difference between the fitting curve and the actual data points, usually represented by metrics such as mean square error or weighted square error.

[0053] S1034D: According to the fitting error of the device performance model, evaluate the prediction accuracy of the pre-constructed device performance model.

[0054] In this embodiment, according to the fitting error of the device performance model, the prediction accuracy of the pre-constructed device performance model can be evaluated. If the fitting error does not exceed the preset error threshold, it indicates that the model can well fit the actual data and the prediction accuracy is high. If the fitting error exceeds the preset error threshold, it means that the difference between the model and the actual data is large, the prediction accuracy is low, the model health score will decrease, and it is necessary to re-collect training data and train the model to adapt to the new environment.

[0055] S104: According to the prediction accuracy of the device performance model and based on multi-modal fusion technology, adjust the weights in the pre-constructed health calculation model.

[0056] In this embodiment, the calculation formula of the health calculation model includes:

[0057] ; Formula (1)

[0058] where H represents the comprehensive health score of the device performance model; i represents different device operation parameters in the refrigeration machine room; w i represents the operation coefficient weight corresponding to different device operation parameters in the refrigeration machine room; β iRepresents the sensitivity coefficient weights corresponding to the operating parameters of different devices in the refrigeration machine room; T i Represents the preprocessed real-time operating data; P i Represents the device performance prediction data; γ(E) represents the device aging weight function; n represents the total number of operating parameters of different devices in the refrigeration machine room; exp represents the exponential function.

[0059] In this embodiment, the health status of the devices in the refrigeration machine room is affected by various factors, including the passage of time, changes in the external environment, and the aging of the devices themselves. These factors will cause changes in the device performance, thereby affecting the prediction accuracy of the pre-constructed device performance model. This application uses multi-modal fusion technology to dynamically adjust the weights of various items in the pre-constructed health calculation model in a timely manner according to the actual situation to ensure the accuracy and reliability of the model health calculation.

[0060] In this embodiment, according to the prediction accuracy of the device performance model and based on the multi-modal fusion technology, the method of adjusting the weights in the pre-constructed health calculation model includes: based on the multi-modal fusion technology, adjusting the operation coefficient weights corresponding to the operating parameters of different devices in the refrigeration machine room, the sensitivity coefficient weights corresponding to the operating parameters of different devices in the refrigeration machine room, and the device aging weight function respectively.

[0061] In this embodiment, the refrigeration machine room contains many devices, such as chillers, pumps, cooling towers, etc. Their operating parameters (such as temperature, pressure, flow rate, etc.) all have a significant impact on the system performance. Each of the devices such as chillers, pumps, and cooling towers in the refrigeration machine room has sensor monitoring data in multiple dimensions. These data can be divided into:

[0062] (1) Physical dimension: The real-time operating data of the device, such as temperature, pressure, flow rate, etc.

[0063] (2) Model dimension: The prediction data output by the device performance model, including energy efficiency prediction, flow rate prediction, etc.

[0064] (3) Historical dimension: The historical operating data of the device, including device aging, failure rate, maintenance records, etc.

[0065] In this embodiment, the multi-modal fusion technology can fuse the data from different sensors and monitoring systems, comprehensively evaluate the current operating state and future health status of the device, and enrich the input dimension of the health degree. Subsequently, according to the prediction accuracy of the device performance model and using the multi-modal fusion technology, dynamically adjust the influence of different input dimensions on the health degree. If a certain parameter has a greater impact on the model prediction accuracy, then increase its weight accordingly; otherwise, reduce the weight to achieve adaptive model health degree evaluation.

[0066] In this embodiment, in the multi-modal fusion technology, common fusion strategies include, but are not limited to: weighted average fusion algorithm, deep learning fusion algorithm, inter-modal error adjustment fusion algorithm, etc.

[0067] In this embodiment, the weighted average fusion algorithm assigns different weights to each dimension of data according to the reliability and importance of the data, and then calculates the weighted average value as the final fusion result. The assignment of weights is usually based on the accuracy and stability of the data, as well as their contribution to the assessment of equipment health. By calculating the weighted average value, a health assessment result that combines information from multiple dimensions can be obtained.

[0068] In this embodiment, the deep learning fusion algorithm uses a deep learning model (such as a neural network) to automatically learn and extract features from multi-dimensional data and perform fusion. The deep learning model can extract useful features from the original data. The extracted features can be fused at different levels of the model to generate a comprehensive health assessment. The deep learning model needs to be trained to optimize its parameters to improve the accuracy of the health assessment.

[0069] In this embodiment, the inter-modal error adjustment fusion algorithm aims to improve the accuracy of the fusion result by adjusting the errors between different dimensions of data. First, the sources and magnitudes of the errors between different dimensions of data need to be analyzed. According to the error analysis results, the different dimensions of data are appropriately adjusted to reduce the errors between them. The dimension data after error adjustment can be fused to generate a more accurate health assessment result.

[0070] In this embodiment, the historical operation data includes one or a combination of more of operation parameter data, equipment aging data, and operation environment data; the ways to adjust the equipment aging weight function include:

[0071] (1) Based on the operation parameter data, equipment aging data, and operation environment data, and based on a pre-constructed equipment aging model, evaluate the aging degree of different equipment in the refrigeration machine room.

[0072] In this embodiment, historical operation parameter data, historical equipment aging data, and historical operation environment data are integrated into a single dataset, where each data point contains the operation parameters, aging data, and environment data of the equipment. The integrated dataset is divided into a training set and a validation set (or test set), typically in a ratio of 80% for the training set and 20% for the validation set. Ensure that the data distributions in the training set and the validation set are similar to avoid overfitting or underfitting of the model. Use the training set data to train the deep learning model. During the training process, continuously adjust the parameters of the model to minimize the loss function (such as mean squared error, cross-entropy, etc.). Use the validation set data to monitor the performance of the model and avoid overfitting to construct the equipment aging model, which can comprehensively consider operation parameters, equipment aging data, and operation environment data to evaluate the aging degree of the equipment.

[0073] (2) According to the aging degrees of different equipment in the refrigeration machine room, use multiple regression analysis techniques to adjust the equipment aging weight function.

[0074] In this embodiment, according to the aging degrees of different equipment in the refrigeration machine room, use multiple regression analysis techniques to calculate the impact of equipment aging on the model health, and dynamically adjust the equipment aging weight function in the health calculation model of the equipment aging. The multiple regression analysis techniques include, but are not limited to: linear regression algorithm, polynomial regression algorithm, ridge regression algorithm, etc.

[0075] In this embodiment, the linear regression algorithm assumes a linear relationship between the dependent variable and the independent variables. By fitting a straight line or a plane (for multiple independent variables), the value of the dependent variable can be predicted. In the linear regression algorithm, each independent variable has a corresponding coefficient, and these coefficients represent the degree of influence of the independent variables on the dependent variable. The polynomial regression algorithm is an extension of linear regression, which allows the relationship between the independent variables and the dependent variable to be non-linear. By introducing polynomial terms of the independent variables (such as squares, cubes, etc.), polynomial regression can fit more complex curves. This makes polynomial regression more flexible and accurate than linear regression when dealing with data with non-linear relationships. The ridge regression algorithm is a linear regression method for dealing with the problem of multicollinearity. It limits the magnitudes of the regression coefficients by adding an L2 regularization term (i.e., the sum of the squares of all regression coefficients) to the regression equation, thereby reducing the impact of multicollinearity. By adjusting the parameter of the regularization term (i.e., the ridge parameter), the fitting degree and generalization ability of the model can be balanced.

[0076] In this embodiment, the regression coefficients of each variable are extracted by using multiple regression analysis technology. These coefficients reflect the influence degree of each variable (including variables related to equipment aging) on the model health index. Specifically, the model health index of the equipment is used as the dependent variable, and the operation parameter data, equipment aging data (such as service life, maintenance times, etc.) and operation environment data are used as independent variables. Through multiple regression analysis, the regression coefficient of each independent variable can be obtained, and these coefficients represent the specific influence degree of each independent variable on the model health. In order to quantify the influence of equipment aging on the model health, the product of the coefficient of the aging variable and the corresponding data value can be calculated, and these products are summed or weighted and summed to obtain a quantified value reflecting the influence degree of equipment aging. Based on this quantified value, the equipment aging weight function in the health calculation model of the equipment is dynamically adjusted. The equipment aging weight function can comprehensively consider the influence of different aging factors on the model health and give corresponding weights according to the actual situation. For example, if the service life of a certain equipment has a particularly large influence on the model health, a higher weight should be given to this factor during evaluation.

[0077] It should be noted that this application uses multi-modal fusion technology to effectively fuse multi-dimensional input data (real-time operation data, historical operation data, equipment performance prediction data), so as to dynamically adjust the weights of each input dimension according to environmental changes, equipment aging and current operating conditions, realize the adaptive health assessment of the equipment performance model, and then be able to comprehensively evaluate the current operating state and future health status of the equipment, making the equipment performance model provide a more comprehensive and accurate assessment when facing complex industrial systems, which is mainly reflected in the following aspects:

[0078] (1) The multi-modal fusion technology can combine the real-time operation data, historical operation data collected by different sensors and the equipment performance prediction data output by the model to realize the adaptive health assessment of the equipment performance model, so that the equipment performance model can comprehensively evaluate the current operating state and future health state of the equipment. This multi-modal fusion technology does not rely on a single data source or model result, but improves the comprehensiveness and accuracy of the assessment through multi-modal feature fusion. For example, when the cooling tower cannot dissipate heat efficiently due to climatic conditions, the multi-modal fusion technology can adjust the weights in the health calculation model according to different input signals to avoid excessive system load resulting in equipment loss.

[0079] (2) The multi-modal fusion technology takes health as one of the core dimensions in the state space. Through multi-modal data fusion, it enriches the input dimensions of health and expands the state space of health. Usually, a single data source can only reflect the local state of the equipment, while the multi-modal fusion algorithm can provide a more comprehensive description of the current health status of the equipment by integrating multi-dimensional data.

[0080] (3) The use of multimodal fusion technology can dynamically adjust the influence of different input dimensions on the health calculation model. Specifically, it can dynamically adjust the weights of each input dimension according to environmental changes, equipment aging, and current operating conditions to achieve adaptive health assessment. For example, for weight adjustment due to environmental changes, when the external temperature rises, the temperature load of the cooling tower increases, and this application will increase the weight of the operating parameters related to the cooling tower to ensure the accuracy of the assessment. For dynamic adjustment of health due to equipment aging, when the equipment enters the later stage of use, the influence weight of historical data will be increased to better reflect the impact of aging on the model health.

[0081] (4) The fusion of multi-dimensional input data using multimodal fusion technology can effectively reduce the errors caused by a single modality. Especially when the fitting error of the equipment performance model is large or a certain sensor fails, the multimodal fusion technology can automatically allocate more weights to other modalities to ensure the stability of the model health assessment. For example, if the data of a certain sensor of the chiller fluctuates greatly or the model prediction deviates significantly from the actual situation, the multimodal fusion technology can automatically reduce the weight of this modality, thereby reducing the impact of this error signal on the model health assessment, identifying abnormal data and automatically correcting it, achieving automatic adjustment of modality failure, and ensuring the accuracy of the model health assessment result.

[0082] (5) The use of multimodal fusion technology can take into account both long-term and short-term goals in the model health assessment. Among them, short-term optimization is that when the real-time operation data of the equipment shows that it is in an efficient state, a positive assessment will be given to improve the short-term health. Long-term balance is achieved by introducing equipment aging data and long-term historical data, which can balance the relationship between short-term efficiency and long-term health and avoid excessive consumption of the equipment.

[0083] S105: Calculate the comprehensive health score of the equipment performance model according to the adjusted-weight health calculation model.

[0084] In this embodiment, according to the adjusted-weight health calculation model, the comprehensive health score of the equipment performance model is calculated using formula (1).

[0085] In this embodiment, the method further includes: comparing the comprehensive health score of the device performance model with a preset health threshold, and determining whether the device performance model needs to be retrained according to the comparison result. If the comprehensive health score of the device performance model is higher than or equal to the preset health threshold, it indicates that the device performance model is currently in a good health state and does not need to be retrained. If the comprehensive health score of the device performance model is lower than the preset health threshold, it indicates that there are deviations or degradations in the device performance model, and model retraining is automatically triggered to ensure that the device performance model is always in an optimal state, providing strong support for the operation optimization of the refrigeration machine room. Based on the updated device performance model, combined with real-time or recent operation data, equipment aging data, and operation environment data, it is possible to accurately evaluate the health of the refrigeration machine room during dynamic operation in real time, so as to adjust the equipment operation strategy, such as optimizing the start-stop frequency of the chiller or adjusting the wind speed of the cooling tower, optimizing the load distribution and operation time of the equipment, reducing unnecessary energy consumption, and improving energy utilization efficiency. In this way, through continuous monitoring and optimization, health status problems of the equipment are identified in advance, maintenance or load adjustment is carried out in a timely manner, the service life of the equipment is extended, and the refrigeration machine room is ensured to always maintain an efficient, stable, and environmentally friendly operation state.

[0086] In this embodiment, the method further includes: displaying the comprehensive health score and change trend of the device performance model through a visual interface, so that users can intuitively understand the device status.

[0087] It should be noted that the health assessment method of the refrigeration machine room equipment performance model of the present application monitors the real-time operation data, historical operation data, and equipment performance prediction data generated during the operation of the refrigeration machine room, according to the prediction accuracy of the device performance model, and uses multi-modal fusion technology to combine the current operation status, environmental parameters, and equipment aging conditions, dynamically adjusts the weights of different input dimensions in the health calculation model, realizes the adaptive health assessment of the device performance model, and thus can more comprehensively and accurately evaluate the model health, effectively reduce the error of a single data source, and then calculate the most representative comprehensive health score of the device performance model. And when the comprehensive health score of the device performance model is lower than the preset health threshold, model retraining is automatically triggered to achieve the adaptive adjustment of the model, so as to ensure that the device performance model is always in an optimal state, enabling the device performance model to comprehensively evaluate the current and future operation states of the equipment, and thus being able to accurately evaluate the health of the refrigeration machine room during dynamic operation in real time, providing strong support for the operation optimization of the refrigeration machine room.

[0088] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0089] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or similar expressions thereof refer to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one (item) of a, b or c can represent: a, b, c, a - b, a - c, b - c or a - b - c, where a, b, c can be single or multiple.

[0090] Figure 4 is a schematic block diagram of a system for evaluating the health of a refrigeration plant equipment performance model provided by the embodiments of the present application. As Figure 4 shown, the system for evaluating the health of a refrigeration plant equipment performance model includes:

[0091] A data acquisition module 401, configured to acquire real - time operation data and historical operation data generated during the operation of the refrigeration plant.

[0092] A data pre - processing module 402, configured to pre - process the acquired real - time operation data and historical operation data respectively.

[0093] A model accuracy evaluation module 403, configured to evaluate the prediction accuracy of a pre - constructed equipment performance model according to the pre - processed real - time operation data and the pre - processed historical operation data.

[0094] A weight adjustment module 404, configured to adjust the weights in a pre - constructed health calculation model according to the prediction accuracy of the equipment performance model and based on a multi - modal fusion technology.

[0095] A health evaluation module 405, configured to calculate the comprehensive health score of the equipment performance model according to the health calculation model with adjusted weights.

[0096] In this embodiment, the health assessment module 405 is further configured to perform the following steps: compare the comprehensive health score of the device performance model with a preset health threshold, and determine whether the device performance model needs to be retrained according to the comparison result. If the comprehensive health score of the device performance model is higher than or equal to the preset health threshold, it indicates that the device performance model is currently in a good health state and does not need to be retrained. If the comprehensive health score of the device performance model is lower than the preset health threshold, it indicates that there are deviations or degradations in the device performance model, and model retraining is automatically triggered to ensure that the device performance model is always in an optimal state and provides strong support for the operation optimization of the refrigeration machine room.

[0097] It should be understood that the specific processes of the above corresponding steps executed by each module have been described in detail in the above method embodiment. For the sake of brevity, they will not be repeated here.

[0098] It should also be understood that the division of modules in the embodiments of the present application is illustrative, and is only a logical function division. There may be other division methods in actual implementation. In addition, in each embodiment of the present application, the functional modules can be integrated in one processor, can also exist separately physically, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0099] Figure 5 is a schematic block diagram of an electronic terminal provided by an embodiment of the present application. As Figure 5 shown, the electronic terminal 500 includes: at least one processor 501, a memory 502, at least one network interface 503, and a user interface 505. Each component in the device is coupled together through a bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear description, in Figure 5 all kinds of buses are labeled as the bus system.

[0100] Among them, the user interface 505 may include a display, a keyboard, a mouse, a trackball, a click gun, a button, a button, a touchpad, or a touch screen, etc.

[0101] It can be understood that the memory 502 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM, Static Random Access Memory), synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory). The memory described in the embodiments of the present invention is intended to include but not limited to these and any other suitable categories of memories.

[0102] The memory 502 in the embodiments of the present invention is used to store various categories of data to support the operation of the electronic terminal 500. Examples of these data include: any executable program for operating on the electronic terminal 500, such as the operating system 5021 and the application program 5022; the operating system 5021 contains various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 5022 can include various application programs, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for evaluating the health of the performance model of the refrigeration plant equipment provided by the embodiments of the present invention can be included in the application program 5022.

[0103] The method disclosed in the above embodiments of the present invention can be applied to the processor 501 or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 501 or by instructions in software form. The above-mentioned processor 501 can be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor 501 can be a microprocessor or any conventional processor, etc. Combining the steps of the accessory optimization method provided by the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or by a combination of the hardware and software modules in the decoding processor. The software module can be located in the storage medium, and this storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the foregoing method.

[0104] In an exemplary embodiment, the electronic terminal 500 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the foregoing method.

[0105] According to the method provided by the embodiments of the present application, the present application also provides a computer program product, which includes computer program code. When the computer program code runs on a computer, the computer is caused to execute Figures 1 to 3 the method of any one of the illustrated embodiments.

[0106] According to the method provided by the embodiments of the present application, the present application also provides a computer-readable storage medium storing program code. When the program code runs on a computer, the computer is caused to execute Figures 1 to 3 the method of any one of the illustrated embodiments.

[0107] As used in this specification, the terms "component", "module", "system", etc. are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, an execution thread, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be components. One or more components may reside in a process and / or execution thread, and a component may be located on one computer and / or distributed between two or more computers. In addition, these components may execute from various computer-readable media storing various data structures. A component may communicate, for example, through a signal with other systems via local and / or remote processes based on one or more data packets (e.g., data from two components interacting with another component in a local system, a distributed system, and / or a network, such as via the Internet).

[0108] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0109] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0110] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, the functional units in the various embodiments of this application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0113] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a high-definition digital video disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD), etc.).

[0114] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The foregoing storage medium includes: USB flash drive, removable hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disc, etc., which can store program codes of various kinds.

[0115] As described above, the above are only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0116] In summary, the present application provides a method, system, medium, and terminal for evaluating the health of a refrigeration machine room equipment performance model. By monitoring the real-time operation data, historical operation data, and equipment performance prediction data generated during the operation of the refrigeration machine room, according to the prediction accuracy of the equipment performance model, and using multi-modal fusion technology, combined with the current operation status, environmental parameters, and equipment aging situation, the weights of different input dimensions in the health calculation model are dynamically adjusted, realizing the adaptive health evaluation of the equipment performance model. Thus, it can more comprehensively and accurately evaluate the model health, effectively reduce the error of a single data source, and then calculate the comprehensive health score of the most representative equipment performance model. And when the comprehensive health score of the equipment performance model is lower than the preset health threshold, the model retraining is automatically triggered, realizing the adaptive adjustment of the model to ensure that the equipment performance model is always in the optimal state, enabling the equipment performance model to comprehensively evaluate the current and future operation states of the equipment, so as to be able to accurately evaluate the health of the refrigeration machine room in the dynamic operation process in real time, providing strong support for the operation optimization of the refrigeration machine room. Therefore, the present application effectively overcomes various disadvantages in the prior art and has high industrial utilization value.

[0117] The above embodiments are only illustrative of the principles and effects of the present application and are not intended to limit the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A method for evaluating the health of a refrigeration room equipment performance model, characterized in that: include: Collect real-time and historical operation data generated by the refrigeration room during operation; Preprocessing the collected real-time operation data and historical operation data respectively; Evaluating the prediction accuracy of the pre-built equipment performance model based on the pre-processed real-time operation data and the pre-processed historical operation data; According to the prediction accuracy of the equipment performance model and based on multimodal fusion technology, adjusting the weights in the pre-built health calculation model; The calculation formula of the health degree calculation model is include: Where H represents the comprehensive health score of the equipment performance model; i represents the operating parameters of different equipment in the refrigeration room; w i Indicates the weight of the operating coefficient corresponding to the operating parameters of different equipment in the refrigeration room; β i Indicates the sensitivity coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room; T i represents the real-time running data after preprocessing; P i represents the equipment performance prediction data; γ(E) represents the equipment aging weight function; n represents the total number of different equipment operating parameters in the refrigeration room; exp represents the exponential function; The method of adjusting the weights in the pre-built health calculation model includes: adjusting the operating coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room, the sensitivity coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room, and the equipment aging weight function based on the multimodal fusion technology; The comprehensive health score of the equipment performance model is calculated according to the health calculation model after the weight is adjusted.

2. The refrigeration room equipment performance model health evaluation method according to claim 1 is characterized in that: The historical operation data includes one or a combination of operating parameter data, equipment aging data, and operating environment data; The method of adjusting the device aging weight function includes: According to the operating parameter data, equipment aging data, operating environment data, and based on a pre-built equipment aging model, evaluating the aging degree of different equipment in the refrigeration room; According to the aging degree of different equipment in the refrigeration room, the equipment aging weight function is adjusted using multiple regression analysis technology.

3. The refrigeration room equipment performance model health evaluation method according to claim 1 is characterized in that: The method further comprises: The comprehensive health score of the device performance model is compared with a preset health threshold, and it is determined whether the device performance model needs to be retrained based on the comparison result.

4. The refrigeration room equipment performance model health evaluation method according to claim 1 is characterized in that: Ways to evaluate the predictive accuracy of pre-built equipment performance models include: Based on a data offset detection algorithm, the preprocessed real-time operation data is compared with the training data for building the equipment performance model, and an offset value between the preprocessed real-time operation data and the training data is calculated; The prediction accuracy of the pre-built equipment performance model is evaluated according to the offset value between the pre-processed real-time operation data and the training data.

5. The refrigeration room equipment performance model health evaluation method according to claim 1 is characterized in that: Ways to evaluate the predictive accuracy of pre-built equipment performance models include: According to the pre-processed real-time operation data and based on a pre-built equipment performance model, obtaining equipment performance prediction data; Calculating a deviation value between the preprocessed real-time operation data and the equipment performance prediction data; Calculating the fitting error of the equipment performance model based on the deviation value between the preprocessed real-time operation data and the equipment performance prediction data by using a curve fitting technique; The prediction accuracy of the pre-built equipment performance model is evaluated based on the fitting error of the equipment performance model.

6. A refrigeration room equipment performance model health assessment system, characterized in that: include: Data collection module, used to collect real-time operation data and historical operation data generated during the operation of the refrigeration room; A data preprocessing module, used to preprocess the collected real-time operation data and historical operation data respectively; A model accuracy evaluation module, used to evaluate the prediction accuracy of the pre-built equipment performance model based on the pre-processed real-time operation data and the pre-processed historical operation data; A weight adjustment module, used to adjust the weights in a pre-built health calculation model according to the prediction accuracy of the equipment performance model and based on multimodal fusion technology; The calculation formula of the health degree calculation model is include: Among them, H represents the comprehensive health score of the equipment performance model; i represents the operating parameters of different equipment in the refrigeration room; wi represents the operating coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room; βi represents the sensitivity coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room; Ti represents the real-time operating data after preprocessing; Pi represents the equipment performance prediction data; γ(E) represents the equipment aging weight function; n represents the total number of operating parameters of different equipment in the refrigeration room; exp represents the exponential function; The method of adjusting the weights in the pre-built health calculation model includes: adjusting the operating coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room, the sensitivity coefficient weights corresponding to the operating parameters of different equipment in the refrigeration room, and the equipment aging weight function based on the multimodal fusion technology; The health evaluation module is used to calculate the comprehensive health score of the equipment performance model according to the health calculation model after adjusting the weight.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the health status of a refrigeration room equipment performance model according to any one of claims 1 to 5 is implemented.

8. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the refrigeration room equipment performance model health assessment method according to any one of claims 1 to 5.

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