Battery swap station health degree evaluation method, electronic equipment and computer storage medium

Abstract

The invention discloses a battery swap station health degree assessment method, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining energy storage equipment data information, battery swap related equipment data information and fault maintenance related information of a battery swap station in a previous detection period in response to the start of a current detection period; performing feature extraction on the energy storage equipment data information, the battery replacement related equipment data information and the fault maintenance related information to obtain battery replacement station data features; and inputting the data features of the battery swap station into a preset evaluation model to obtain the health degree of the battery swap station. And at the beginning of the current round of detection period, obtaining the health degree of the battery replacing station according to the energy storage equipment data information, the battery replacing related equipment data information and the fault maintenance related information of the battery replacing station in the previous round of detection period, thereby improving the fault prediction and preventive maintenance of the equipment, ensuring the stable operation of a power system, and improving the safety of the battery replacing station. The maintenance cost and the waste of human resources are reduced, and the user experience and the enterprise operation and maintenance efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery swap stations, and in particular to a battery swap station health assessment method, electronic equipment and computer storage medium. Background Art

[0002] At present, battery swap stations, as an important infrastructure for energy supply of new energy vehicles, have developed rapidly in recent years. However, although the popularity of battery swap stations is increasing, there is still a lot of room for development and improvement in equipment health management, fault prediction and preventive maintenance.

[0003] First, the long-term operation and environmental impact of the battery swap station equipment will inevitably lead to failures. These failures may cause abnormal operation of the power system, power outages, or even equipment damage, causing serious economic losses and safety risks.

[0004] Secondly, the operation and maintenance costs of battery swap stations are high. Downtime and reduced charging and swapping success rates caused by equipment failure will directly affect user experience and the company's operational efficiency. Summary of the invention

[0005] The purpose of this application is to provide a method for evaluating the health of a battery swap station, an electronic device, and a computer storage medium, aiming to ensure the stable operation of the power system, reduce maintenance costs and waste of human resources, and improve the user experience and the operation and maintenance efficiency of the enterprise.

[0006] To achieve the above objectives: In a first aspect, an embodiment of the present application provides a method for evaluating the health of a battery swap station, the method comprising: In response to the start of this round of detection cycle, the energy storage equipment data information, the battery replacement related equipment data information and the fault repair related information of the battery replacement station in the previous round of detection cycle are obtained; Perform feature extraction on the energy storage device data information, battery swapping related equipment data information, and fault repair related information to obtain battery swapping station data features; The battery swap station data features are input into a preset evaluation model to obtain the health of the battery swap station.

[0007] Optionally, before responding to the start of the current round of detection cycle, the method further includes: Obtain historical energy storage equipment data information, historical battery swap-related equipment data information, and historical fault repair-related information from multiple rounds of inspection cycles of the battery swap station; Preprocessing the historical energy storage equipment data information, historical battery replacement related equipment data information, and historical fault repair related information; Perform feature extraction on the preprocessed historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information to respectively obtain historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features; The evaluation model is obtained according to the historical energy storage equipment data characteristics, the historical battery replacement related data characteristics and the historical fault repair data characteristics.

[0008] Optionally, the preprocessing of the historical energy storage device data information, the historical battery replacement related equipment data information and the historical fault repair related information includes: Performing mean smoothing processing on abnormal values ​​in the historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information, and / or, Fill missing values ​​in the historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information with zero values, and / or, Normalizing the time series data information in the historical energy storage device data information, the historical battery replacement related equipment data information, and the historical fault repair related information, and / or, The historical energy storage equipment data information, historical battery replacement related equipment data information and historical fault repair related information are aligned in time.

[0009] Optionally, the obtaining of the evaluation model according to the historical energy storage device data characteristics, the historical battery replacement related data characteristics and the historical fault repair data characteristics includes: Respectively obtain weight coefficients corresponding to the historical energy storage device data characteristics, historical battery replacement related data characteristics, and historical fault maintenance data characteristics; The historical energy storage device data characteristics, historical battery replacement related data characteristics, historical fault repair data characteristics and the weight coefficient are input into the untrained model to obtain the evaluation model.

[0010] Optionally, inputting the historical energy storage device data features, the historical battery replacement related data features, the historical fault repair data features and the weight coefficient into an untrained model to obtain the evaluation model includes: Randomly segment the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features according to a preset segmentation ratio to obtain a training data set and a test data set; The training data set, the test data set, and the weight coefficients are input into the untrained model to obtain a trained model.

[0011] Optionally, inputting the training data set, the test data set, and the weight coefficient into the untrained model to obtain a trained model includes: Inputting the training data set and the weight coefficient into the untrained model to obtain a trained model; Inputting the test data set and the weight coefficient into the trained model to obtain an evaluation result; Determining whether the accuracy of the evaluation result is greater than a preset accuracy threshold; If yes, the trained model is determined as the evaluation model; If not, adjust the weight coefficients corresponding to the historical energy storage device data characteristics, historical battery replacement related data characteristics and historical fault repair data characteristics according to the evaluation results, and re-execute the step of inputting the historical energy storage device data characteristics, historical battery replacement related data characteristics, historical fault repair data characteristics and the weight coefficients into the untrained model.

[0012] Optionally, the energy storage device data information includes at least: any one of photovoltaic energy storage device status basic data and compressed air energy storage device status basic data; The data information of the battery swapping related equipment includes at least: any one of the basic data of the status of each module of the battery swapping station software system, the basic data of the status of the battery swapping station equipment, and the basic data of the integrity of the battery swapping station equipment; The fault repair related information includes at least: any one of the basic data of fault and quality assessment of the battery swap station and the basic data of work order processing.

[0013] Optionally, after inputting the battery swap station data features into a preset evaluation model to obtain the health of the battery swap station, the method further includes: Corresponding alarm feedback is performed according to the score interval in which the health level is located. In a second aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the battery swap station health assessment method as described above when executing the computer program.

[0014] In a third aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the battery swap station health assessment method as described above are implemented.

[0015] In this application, at the beginning of this round of detection cycle, the health of the battery swap station is obtained based on the energy storage equipment data information, battery swap related equipment data information and fault repair related information of the battery swap station in the previous round of detection cycle. In this way, the fault prediction and preventive maintenance of the equipment are improved, the stable operation of the power system is ensured, the maintenance cost and waste of human resources are reduced, and the user experience and the operation and maintenance efficiency of the enterprise are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for evaluating the health of a battery swap station provided in an embodiment of the present invention.

[0017] Figure 2 A schematic diagram of the structure of a battery swap station operation status monitoring system provided in an embodiment of the present invention.

[0018] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0019] Description of Figure Numbers: 310. Processor; 311. Memory; 312. Network interface; 313. Bus system. DETAILED DESCRIPTION

[0020] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0021] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0022] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0023] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0024] It should be noted that in this article, step codes such as S1 and S2 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the order. When implementing the step, those skilled in the art may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0025] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0027] Please refer to Figure 1 , Figure 1 A flow chart of a method for evaluating the health of a battery swap station is shown, and the method comprises: S10. In response to the start of this round of detection cycle, the energy storage equipment data information, battery swapping-related equipment data information, and fault repair related information of the battery swapping station in the previous detection cycle are obtained. Specifically, by setting the detection cycle, on the one hand, potential problems and failures of the equipment can be discovered in a timely manner to ensure the continuity of production and operation. On the other hand, it helps to implement preventive maintenance strategies, effectively extend the service life of the equipment and improve the operational safety of the equipment. In this embodiment, at the beginning of each detection cycle, the battery swapping station operation status monitoring system will automatically obtain the energy storage equipment data information, battery swapping-related equipment data information, and fault repair related information of the battery swapping station in the previous detection cycle.

[0028] Among them, the energy storage equipment of the battery swap station serves as the source of power output for the entire battery swap station. According to the energy storage equipment data information generated by it, it can timely detect abnormal conditions of the energy storage equipment, thereby preventing the abnormal conditions of the energy storage equipment from causing the entire battery swap station to fail to operate normally.

[0029] As the output source for executing the battery swapping process for electric vehicles, the battery swapping related equipment can promptly detect abnormal conditions of the battery swapping related equipment based on the data information it generates, thereby avoiding the situation where abnormal conditions of the battery swapping related equipment cause users to be unable to swap batteries normally when driving into a battery swapping station for battery swapping, affecting the user's experience.

[0030] Fault repair related information is used as a source of information for feedback on whether the battery swap station has fault repair conditions in each round of detection cycle. By obtaining fault repair related information of the previous round of detection cycle, the fault and repair conditions of the battery swap station can be known, and the fault and repair conditions can be quickly checked in the current round of detection cycle, which is more targeted and has higher detection efficiency.

[0031] Furthermore, the detection cycle can be set by the operator of the battery swap station according to actual conditions, for example, it can be every minute, every hour, every day, every week, every month, etc., and is not limited here.

[0032] S20, extracting features from the energy storage device data information, the battery swapping related device data information, and the fault repair related information to obtain the battery swapping station data features. Specifically, in this embodiment, the energy storage device data information, the battery swapping related device data information, and the fault repair related information are extracted to obtain the battery swapping station data features. This process can reduce the impact of noise and outliers on the model, improve the robustness of the model and the operational efficiency of the algorithm.

[0033] Illustratively, feature extraction of energy storage equipment data information can obtain photovoltaic energy storage equipment features, compressed air energy storage equipment features, generator equipment features, charger equipment features, etc.; feature extraction of battery swapping-related equipment data information can obtain monitoring features of key parts of battery swapping stations, temperature control features of battery swapping stations, battery temperature features, etc.; feature extraction of fault maintenance-related information can obtain fault quality and other features, work order processing status features, etc.

[0034] S30. Input the data features of the battery swap station into a preset evaluation model to obtain the health of the battery swap station. Specifically, in this embodiment, by inputting the data features of the battery swap station obtained in step S2 into the preset evaluation model, the current health of the battery swap station can be output. Exemplarily, the health can be divided into "healthy", "sub-healthy", "degraded", "severely degraded", etc. according to the level of excellence. The battery swap station operation status monitoring system can upload the health generated each time to the cloud server, and the battery swap station and operation and maintenance personnel can also follow up in time according to the health to quickly carry out targeted processing actions to ensure the stable operation of the battery swap station.

[0035] By executing steps S10 to S30, at the beginning of this round of detection cycle, the energy storage equipment data information, battery replacement related equipment data information and fault repair related information of the battery swap station in the previous detection cycle are obtained, and the health of the battery swap station is obtained based on the preset evaluation model. In this way, the fault prediction and preventive maintenance of the equipment are improved, the stable operation of the power system is ensured, the maintenance cost and waste of human resources are reduced, and the user experience and the operation and maintenance efficiency of the enterprise are improved.

[0036] Optionally, by formulating a complete battery swap station indicator evaluation system, the indicator evaluation system can quantitatively evaluate the health status of battery swap station related equipment and energy storage equipment, and provide a basis for subsequent maintenance and optimization. In one embodiment, the total amount of battery swap station data features obtained in step S20 is fixed, wherein the evaluation dimension of battery swap related equipment data information is 40%, the evaluation dimension of energy storage equipment data information is 20%, the evaluation dimension of fault repair related information is 20%, and the evaluation dimension of battery swap success rate is 20%.

[0037] Optionally, before responding to the start of the current round of detection cycle in step S10, the method further includes: S6. Obtain historical energy storage equipment data information, historical battery swap-related equipment data information, and historical fault repair-related information for multiple rounds of detection cycles of the battery swap station. Specifically, in this embodiment, the multiple rounds of detection cycles include at least two rounds of detection cycles before the current round of detection cycles, and can also be much larger than two rounds of detection cycles, which are used as material for model training, and can effectively improve the training degree of the model, thereby improving the accuracy of the model.

[0038] S7. Preprocess the historical energy storage equipment data information, historical battery replacement related equipment data information, and historical fault repair related information. Specifically, in this embodiment, each basic data in the historical energy storage equipment data information, historical battery replacement related equipment data information, and historical fault repair related information is cleaned, missing values ​​and abnormal values ​​are processed, features are selected, features are converted, and other preprocessing methods are performed to ensure the integrity and high quality of the data.

[0039] S8. Perform feature extraction on the preprocessed historical energy storage equipment data information, historical battery swap-related equipment data information, and historical fault repair related information to obtain the historical energy storage equipment data features, historical battery swap-related data features, and historical fault repair data features, respectively. Specifically, in this embodiment, similar to step S20, feature extraction is performed on the preprocessed historical energy storage equipment data information, historical battery swap-related equipment data information, and historical fault repair related information to obtain the historical energy storage equipment data features, historical battery swap-related data features, and historical fault repair data features, respectively. This process can reduce the impact of noise and outliers on the model, and improve the robustness of the model and the operational efficiency of the algorithm.

[0040] S9. Obtain an evaluation model based on historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features. Specifically, in this embodiment, the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features are used as the material for model training. After multiple rounds of cyclic training, when ① the model converges, and / or the number of training times reaches a preset number threshold; ② the accuracy of the model generation result reaches a preset accuracy threshold, the trained model is determined as the evaluation model.

[0041] Optionally, the preprocessing of historical energy storage device data information, historical battery replacement related device data information, and historical fault repair related information in step S7 includes: The outliers in the historical energy storage equipment data information, the historical battery replacement related equipment data information, and the historical fault repair related information are processed by mean smoothing. Specifically, in this embodiment, the mean smoothing process can reduce the impact of random noise, making the data smoother, thereby reducing the impact of outliers on the overall data, so as to achieve the effect of maintaining the overall trend and pattern of the data. And / or, Zero-fill missing values ​​in historical energy storage equipment data information, historical battery replacement related equipment data information, and historical fault repair related information. Specifically, in this embodiment, zero-filling can maintain the integrity of the data set, avoid data rows or columns being deleted due to missing values, and reduce data loss. And / or, Normalize the time series data information in the historical energy storage equipment data information, the historical battery replacement related equipment data information, and the historical fault repair related information. Specifically, in this embodiment, by normalizing the time series data information, the data features have similar scales, which helps the model better understand the weights of each data feature and improve the stability of the model. And / or, The historical energy storage equipment data information, the historical battery swapping related equipment data information and the historical fault repair related information are aligned in time. Specifically, in this embodiment, the data information is aligned in time, which can reduce the noise caused by inconsistent data collection time and improve the data quality. On the other hand, it can enable the model to more accurately capture the data jump trend generated by the energy storage equipment and battery swapping related equipment of the battery swapping station, thereby improving the performance of the model.

[0042] Optionally, the step of acquiring the evaluation model according to the historical energy storage device data characteristics, the historical battery replacement related data characteristics and the historical fault repair data characteristics in step S9 includes: S91. Obtain the weight coefficients corresponding to the historical energy storage device data features, the historical battery swap-related data features, and the historical fault repair data features, respectively. Specifically, in this embodiment, the weight coefficient reflects the relative importance of the three sets of values ​​in the assessment of the health of the battery swap station. The historical energy storage device data features, the historical battery swap-related data features, and the historical fault repair data features have preset weight coefficients, respectively. In one embodiment, the weight coefficient corresponding to the historical battery swap-related data features>the weight coefficient corresponding to the historical energy storage device data features≥the historical fault repair data features.

[0043] S92, input the historical energy storage equipment data features, historical battery replacement related data features, historical fault repair data features and weight coefficients into the untrained model to obtain an evaluation model. Specifically, in one embodiment, a linear regression prediction model is used as an untrained model (base model), and a linear relationship model is established with the historical energy storage equipment data features, historical battery replacement related data features, historical fault repair data features and weight coefficients as independent variables (explanatory variables) and health as the dependent variable (response variable) to achieve the purpose of predictive analysis.

[0044] Optionally, in step S92, the historical energy storage device data features, historical battery replacement related data features, historical fault repair data features and weight coefficients are input into the untrained model to obtain the evaluation model, including: S93. Randomly segment the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features according to a preset segmentation ratio to obtain a training data set and a test data set. Specifically, in this embodiment, the preset segmentation ratio can be 2.5:7.5, 2:8, 3:7, 3.5:6.5, 4:6, etc., and can be adaptively adjusted according to the accuracy of the model generation results, which is not limited here. The preset segmentation ratio can be used to segment the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features into two parts, respectively, and the parts with a larger proportion of the three groups of data features are aggregated to form a training data set for model training, and the remaining other part is aggregated to form a test data set for model testing.

[0045] S94, input the training data set, the test data set and the weight coefficient into the untrained model to obtain the trained model. Specifically, in this embodiment, based on the above implementation, the linear regression prediction model is used as the untrained model (base model), and the training data set, the test data set and the weight coefficient are input into the untrained model for training, thereby obtaining the trained model.

[0046] Optionally, in step S94, inputting the training data set, the test data set, and the weight coefficients into the untrained model to obtain the trained model includes: S95, input the training data set and the weight coefficient into the untrained model to obtain a trained model. Specifically, in this embodiment, each data in the training data set and its corresponding weight coefficient are input into the untrained model to achieve the purpose of training the model, thereby obtaining a trained model.

[0047] S96, input the test data set and the weight coefficient into the trained model to obtain an evaluation result. Specifically, in this embodiment, each data in the test data set and its corresponding weight coefficient are input into the trained model obtained in step S95 to achieve the purpose of testing the model, and then obtain the evaluation results of each input corresponding to the output.

[0048] S97, determine whether the accuracy of the evaluation result is greater than a preset accuracy threshold. If so, determine the trained model as the evaluation model; if not, adjust the weight coefficients corresponding to the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features according to the evaluation results, and re-execute the step of inputting the historical energy storage device data features, historical battery replacement related data features, historical fault repair data features, and weight coefficients into the untrained model.

[0049] Specifically, in this embodiment, the accuracy of the evaluation result output by the trained model is determined. When the accuracy of the evaluation result is greater than the accuracy threshold, it proves that the trained model has converged and can be determined as the evaluation model for the subsequent evaluation of the health of the battery swap station. Otherwise, it is necessary to readjust the weight coefficients corresponding to the historical energy storage device data features, the historical battery swap-related data features, and the historical fault repair data features, and then re-execute step S92, and repeat the cycle until it returns to step S97 in a certain round of training cycles, and when the accuracy of the evaluation result output by the trained model is greater than the accuracy threshold, the training cycle is stopped, and the finally obtained trained model is determined as the evaluation model.

[0050] Optionally, in one implementation, the evaluation model is obtained by following the steps: Step 1: Model definition y=ρ0+ρ1x1+ρ2x2+⋯+ρ p x p in: y is the target variable.

[0051] x1, x2, …, x p are input features.

[0052] β0, β1, β2, …, β p is the model parameter, that is, the weight coefficient.

[0053] Step 2: Prepare data The temperature characteristics of photovoltaic equipment, DC to AC inverter current characteristics, energy management equipment characteristics, generator equipment characteristics, thermal energy storage equipment temperature characteristics, battery swap station temperature control characteristics, battery swap station water cooling characteristics, battery swap station fire water level characteristics, battery swap station charger characteristics, battery temperature characteristics, battery swap station noise characteristics, battery swap station key parts characteristics, battery swap station network characteristics, battery swap station equipment life characteristics are processed by feature engineering to generate a feature array: data = { 'feature1': [30,40,50,60,70,80,90] #Photovoltaic equipment temperature characteristics; 'feature2': [15,20,25,30,35,40,45] #DC to AC inverter current characteristics; 'feature3': [30,40,50,60,70,80,90] #Temperature characteristics of thermal energy storage equipment; 'feature4': [30,40,50,60,70,80,90] #Temperature control features of the battery swap station; 'feature5': [30,40,50,60,70,80,90] #Water cooling features of the battery swap station; 'feature6': [5,6,7,8,9,10,11] #Fire water level characteristics of the battery swap station; 'feature7': [0,1,2,3,-1] #Charger status characteristics of battery swap station; 'feature8': [10,15,20,25,30,35,40] #Noise characteristics of battery swap stations; ……} 'target': [95,90,86,80,75,70,65] #Swap station health score.

[0054] Step 3: Split the dataset X = df[['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8']]; y = df['target']; X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42); Among them, the test data set accounts for 30% of the total data set, and the training data set accounts for the remaining 70%; the random_state parameter is used to control the randomness of the data set segmentation, and the seed of the initialized random number generator is 42.

[0055] Step 4: Create and train a linear regression model In this phase, a linear regression model is first created and then trained using the data from the training dataset.

[0056] model = LinearRegression(); model.fit(X_train, y_train).

[0057] Step 5: Model prediction After the untrained model is trained to obtain the trained model, it is used to predict the data in the test dataset to obtain the value of the target variable.

[0058] y_pred = model.predict(X_test).

[0059] Step 6: Output the health model score In this step, the coefficients and intercept of the trained model are output, which are parameters that describe the relationship between the features and the target variable.

[0060] coefficients = model.coef_; intercept = model.intercept_; print(f'Coefficients: {coefficients}'); print(f'Intercept: {intercept}').

[0061] Step 7: Detection model effect evaluation When evaluating the effectiveness of a prediction model, we usually focus on aspects such as the model’s accuracy, stability, and practicality. Common evaluation indicators include: 1. Accuracy: Definition: The ratio of the number of samples predicted correctly to the total number of samples.

[0062] Formula: Accuracy = (TP+TN) / (TP+TN+FP+FN).

[0063] Among them, TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative).

[0064] 2. Precision: Definition: The proportion of samples predicted to be positive that are actually positive.

[0065] Formula: Precision = = TP / (TP+FP).

[0066] 3. Recall: Definition: The proportion of samples that are correctly predicted to be positive among all samples that are actually positive.

[0067] Formula: Recall = TP / (TP+FN).

[0068] In one embodiment, it is determined whether any one of the precision, recall and F1 score (accuracy) meets the preset accuracy threshold. If not, it indicates that the model has not yet reached the expected performance level and needs further improvement. The model can be retrained by the method executed in step S97 to improve the training effect of the model, and finally a battery swap station health assessment model that can reach the accuracy threshold is obtained.

[0069] In another embodiment, the accuracy of the model can also be determined by: (1)mse = mean_squared_error(y_test, y_pred); The mean squared error (MSE) is used to calculate the mean squared error between the true value (`y_test`) and the predicted value (`y_pred`). It represents the average of the squares of the difference between the predicted value and the true value, and can measure the error degree of the predicted value. The smaller the mse, the more accurate the model's prediction.

[0070] (2) r2 = r2_score(y_test, y_pred); The R-squared value is also called the coefficient of determination. It is used to measure the accuracy of model predictions. It indicates how much of the variation in the dependent variable can be explained by the independent variable. The R-squared value ranges from 0 to 1. The closer the value is to 1, the stronger the explanatory power of the model and the more accurate the prediction.

[0071] (3) print(f'Mean Squared Error: {mse:.2f}'); print(f'R-squared: {r2:.2f}'); The above two lines of code correspond to the output results of mean square error (mse) and R square value respectively. `:.2f` is a format string, which means to retain two decimal places.

[0072] In summary, the above two lines of code are used to calculate and print out the mean square error (mse) and R-squared value of the model. These two indicators can intuitively obtain the accuracy and explanatory power of the model prediction.

[0073] Based on the establishment and specific application of the evaluation model described in the above embodiments, the current prediction accuracy of battery swap stations can reach 90%, the recall rate can reach 90%, and the prediction value accuracy can reach 85%. It has been widely used to conduct health assessments on battery swap stations deployed in reality.

[0074] Optionally, the energy storage device data information includes at least: any one of the basic data of the photovoltaic energy storage device status and the basic data of the compressed air energy storage device status. The basic data of the photovoltaic energy storage device status and the basic data of the compressed air energy storage device status may include, for example, charging and discharging efficiency, system loss, SOC (State of Charge) consistency, electrical parameters, environmental factors (such as temperature, humidity, weather, wind volume, etc.), energy conversion efficiency, etc., which are not limited here.

[0075] Furthermore, the data information of the battery swapping related equipment includes at least any one of the following: basic data of the status of each module of the battery swapping station software system, basic data of the status of the battery swapping station equipment, and basic data of the integrity of the battery swapping station equipment. Among them, the basic data of the status of each module of the battery swapping station software system may include, for example, the status of the positioning system, the status of the battery swapping system, the status of the operation and maintenance system, the status of the safety system, the status of the power supply system, etc., which are not limited here.

[0076] The basic data of the battery swap station equipment status may include, for example, the lifting device status, the battery swap robot arm status, the charging cabinet status, the battery box status, the transformer status, etc., which are not limited here.

[0077] The basic data of equipment integrity of the battery swap station may include, for example, equipment utilization rate, equipment list, equipment vacancy rate, power battery failure rate, power battery replacement success rate, etc., which are not limited here.

[0078] Furthermore, the fault repair related information includes at least one of the following: basic data on fault and quality assessment of the power swap station and basic data on work order processing. The basic data on fault and quality assessment of the power swap station may include, for example, fault records, quality accident records, equipment maintenance records, performance testing and evaluation, equipment replacement or upgrade, and operation and maintenance personnel training records, which are not limited here.

[0079] The basic data of work order processing may include, for example, user reports, shift handover records, safety responsibility records, task management records, defects and handling status, fault levels and reporting management requirements, etc., which are not limited here.

[0080] Optionally, after inputting the battery swap station data features into a preset evaluation model to obtain the health of the battery swap station, the method further includes: The corresponding alarm feedback is executed according to the score interval in which the health level is located. Specifically, in this embodiment, firstly, by establishing a set of quantifiable health level tables, after obtaining the health level of the battery swap station in this round of detection cycle, the battery swap station operation status monitoring system will automatically match the score corresponding to the health level with the health level table, and after confirming the score interval in which the health level is located, execute the alarm feedback corresponding to the score interval.

[0081] In one embodiment, in the health level table, the health level can be divided into four levels according to the level of quality, namely "healthy", "sub-healthy", "degraded", and "severely degraded", and the score ranges corresponding to the four levels are [90, 95), [80, 90), [60, 80), and [0, 60), respectively. The alarm feedback corresponding to the four score ranges are "APP prompts alarm text", "Initiate work order and manual intervention", "SMS reminder + APP prompts alarm text + initiates work order", and "Telephone alarm on-duty personnel for emergency processing, such as immediate visit to the site for inspection and processing".

[0082] Please refer to Figure 2 , Figure 2 The schematic diagram of the structure of a battery swap station operation status monitoring system is shown. The basic data layer is used to store all data information of the battery swap station, including energy storage equipment data information, battery swap related equipment data information and fault repair related information. The data processing layer is used to pre-process the data in the basic data layer to ensure the integrity and high quality of the data. The feature layer is used to extract features from the pre-processed data information. The extracted features include but are not limited to the following: Figure 2 The model layer is used to input the data information after feature extraction into the preset evaluation model and output the health score. The business layer is used to perform subsequent alarm feedback actions based on the health score. In this way, the entire battery swap station operation status monitoring system can collect and analyze the operation data of battery swap station-related equipment and energy storage equipment in real time, and through fault prediction and preventive maintenance models, timely discover and deal with potential problems in the battery swap station, effectively improving the operation stability and reliability of the equipment.

[0083] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 3The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 3 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the method applied to the above device is implemented.

[0084] The device may also include: at least one network interface 312. The various components in the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 3 Various buses are labeled as bus system 313.

[0085] The memory 311 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0086] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the device. Examples of these data include: any computer program used to operate on the device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0087] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the above-mentioned method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0088] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0090] The above is only a specific embodiment of the present invention, but the protection scope of the present invention 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 invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the health of a battery swap station, characterized in that: The method comprises: In response to the start of this round of detection cycle, the energy storage equipment data information, the battery replacement related equipment data information and the fault repair related information of the battery replacement station in the previous round of detection cycle are obtained; Perform feature extraction on the energy storage device data information, battery swapping related equipment data information, and fault repair related information to obtain battery swapping station data features; The battery swap station data features are input into a preset evaluation model to obtain the health of the battery swap station.

2. The method according to claim 1, characterized in that Before responding to the start of the current round of detection cycle, the method further includes: Obtain historical energy storage equipment data information, historical battery swap-related equipment data information, and historical fault repair-related information from multiple rounds of inspection cycles of the battery swap station; Preprocessing the historical energy storage equipment data information, historical battery replacement related equipment data information, and historical fault repair related information; Perform feature extraction on the preprocessed historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information to respectively obtain historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features; The evaluation model is obtained according to the historical energy storage equipment data characteristics, the historical battery replacement related data characteristics and the historical fault repair data characteristics.

3. The method according to claim 2, characterized in that The preprocessing of the historical energy storage device data information, the historical battery replacement related equipment data information and the historical fault repair related information includes: Performing mean smoothing processing on abnormal values ​​in the historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information, and / or, Fill missing values ​​in the historical energy storage device data information, historical battery replacement related equipment data information, and historical fault repair related information with zero values, and / or, Normalizing the time series data information in the historical energy storage device data information, the historical battery replacement related equipment data information, and the historical fault repair related information, and / or, The historical energy storage equipment data information, historical battery replacement related equipment data information and historical fault repair related information are aligned in time.

4. The method according to claim 2, characterized in that: The obtaining of the evaluation model according to the historical energy storage device data characteristics, the historical battery replacement related data characteristics and the historical fault repair data characteristics includes: Respectively obtain weight coefficients corresponding to the historical energy storage device data characteristics, historical battery replacement related data characteristics, and historical fault maintenance data characteristics; The historical energy storage device data characteristics, historical battery replacement related data characteristics, historical fault repair data characteristics and the weight coefficient are input into the untrained model to obtain the evaluation model.

5. The method according to claim 4, characterized in that The step of inputting the historical energy storage device data features, the historical battery replacement related data features, the historical fault repair data features and the weight coefficient into the untrained model to obtain the evaluation model includes: Randomly segment the historical energy storage device data features, historical battery replacement related data features, and historical fault repair data features according to a preset segmentation ratio to obtain a training data set and a test data set; The training data set, the test data set, and the weight coefficients are input into the untrained model to obtain a trained model.

6. The method according to claim 5, characterized in that The step of inputting the training data set, the test data set, and the weight coefficient into the untrained model to obtain a trained model includes: Inputting the training data set and the weight coefficient into the untrained model to obtain a trained model; Inputting the test data set and the weight coefficient into the trained model to obtain an evaluation result; Determining whether the accuracy of the evaluation result is greater than a preset accuracy threshold; If yes, the trained model is determined as the evaluation model; If not, adjust the weight coefficients corresponding to the historical energy storage device data characteristics, historical battery replacement related data characteristics and historical fault repair data characteristics according to the evaluation results, and re-execute the step of inputting the historical energy storage device data characteristics, historical battery replacement related data characteristics, historical fault repair data characteristics and the weight coefficients into the untrained model.

7. The method according to claim 1, characterized in that The energy storage device data information includes at least: any one of photovoltaic energy storage device status basic data and compressed air energy storage device status basic data; The data information of the battery swapping related equipment includes at least: any one of the basic data of the status of each module of the battery swapping station software system, the basic data of the status of the battery swapping station equipment, and the basic data of the integrity of the battery swapping station equipment; The fault repair related information includes at least: any one of the basic data of fault and quality assessment of the battery swap station and the basic data of work order processing.

8. The method according to claim 1, characterized in that After inputting the battery swap station data features into a preset evaluation model to obtain the health of the battery swap station, the method further includes: Corresponding alarm feedback is performed according to the score interval in which the health level is located.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for assessing the health of a battery swap station as claimed in any one of claims 1 to 8 when executing the computer program.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for assessing the health of a battery swap station according to any one of claims 1 to 8 are implemented.

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