Monitoring and early warning system for power lithium ion battery transport case and monitoring and early warning method

Abstract

The present invention discloses a thermal runaway monitoring and early warning system and method for a power lithium ion battery in a transport case. The system includes a monitoring unit, a data processing unit, a data transmission unit, a power supply unit, a display unit, and the like. The monitoring unit contains infrared array temperature sensors, gas sensors, smoke sensors, and pressure sensors, which are arranged using a distributed scheme to monitor the inside of the transport case. The data transmission unit sends early warning information to a terminal. According to a multi-information monitoring-based thermal runaway early warning method under different transport conditions, different transport condition data and data of the battery in the transport case are fused. A clustering algorithm and an ensemble learning algorithm are involved, and a lithium ion battery thermal runaway risk level is quantified, thereby improving the early warning accuracy.

Claims

1. A multi-information monitoring-based transported battery thermal runaway early warning method based on a monitoring and early warning system installed in a transport case: comprising the following steps: S1: placing a lithium ion battery in the transport case, and performing temperature, vibration, impact, and humidity processing on the transport case; S2: monitoring temperature, gas, smoke, and pressure in the transport case through sensors of a monitoring unit; S3: obtaining various feature parameters of lithium ion battery risks, and normalizing acquired data; S4: performing risk classification by monitoring multi-information state data of the lithium ion battery, and calculating feature parameter thresholds of lithium ion battery thermal runaway risks under different transport conditions; S5: inputting quantified feature values into a clustering algorithm to quantify lithium ion battery thermal runaway risk levels; S6: establishing, to improve risk identification accuracy and prevent a risk false alarm, a data balancing-ensemble learning algorithm to construct a thermal runaway risk identification model of the power lithium ion battery in the transport case; and S7 setting, through the thermal runaway risk identification model of the power lithium ion battery under the transport conditions, a dynamic threshold and a second-level early warning process, and reporting, if it is identified that there is a thermal runaway risk of the lithium ion battery, an early warning signal to a backend; wherein the monitoring and early warning system comprises the monitoring unit, a data processing unit, a data transmission unit, a power supply unit, a display unit, and a response unit; sensors of the monitoring unit comprise temperature sensors, gas sensors, pressure sensors, and smoke sensors; the monitoring unit is deployed on a lower surface of a case cover, and a sensing apparatus adopts a distributed design and is equipped with the temperature sensors, the gas (H2, CO sensors, the smoke sensors, and the pressure sensors; the data processing unit is arranged on an inner side of the case cover; the display unit comprises a display panel and a display light; the display unit is arranged on an outer side of the case cover; the display panel is able to display battery temperature, gas concentration, smoke concentration, and pressure inside the transport case; the display light emits a photoelectric alarm when determining that thermal runaway of the lithium ion battery occurs; the power supply unit is a lithium ion battery pack; the power supply unit is arranged on the inner side of the case cover to supply power to the monitoring and early warning system; the data transmission unit comprises a 4G module and WIFI; the data transmission unit and the data processing unit are integrated in one housing; the response unit comprises a release box and alarm information issued by a platform; when the monitoring and early warning system determines that thermal runaway occurs in the transport case, the release box is opened through an electrical signal, and fire extinguishing material placed in the release box falls to suppress battery flames; the monitoring and early warning system sends the alarm information to a terminal.

2. The method according to claim 1, wherein different transport condition states X.sub.y(t) (temperature Tenv(t) T, vibration V(t), humidity S(t), and impact H(t)) of the lithium ion battery in the transport case in S1 are obtained through the following formula:
X.sub.y(t)=[Tenv(t),V(t),S(t),H(t))].

3. The method according to claim 2, wherein, in S2, battery thermal runaway risk states X.sub.d (battery temperature T.sub.bat, H2 concentration H.sub.2 in the transport case, CO concentration CO in the transport case, smoke concentration SM in the transport case, and pressure P) and different battery monitoring states R.sub.y(t) under the transport conditions are obtained through the following formulas:
X.sub.d(t)=[T.sub.bat(t),H.sub.2(t),CO(t),SM(t),P(t)], and R.sub.y(t)=[X.sub.y(t),X.sub.d(t))] lithium ion battery thermal runaway risk feature parameters under different transport conditions are extracted, comprising the lithium ion battery temperature and a temperature rise rate, the H2 concentration and a change rate, the CO concentration and a change rate, the smoke concentration and a change rate, and a pressure value and a change rate, to describe a changing trend of the lithium ion battery,
T=dT.sub.bat/dt; H.sub.2=dH.sub.2/dt; CO=dCO/dt; SM=dSM/dt; and P=dP/dt; a dynamic changing threshold R*.sub.i is comprehensively determined through the lithium ion battery thermal runaway risk parameters to further determine a risk degree of the lithium ion battery; when the feature parameter is lower than R*.sub.i, it indicates that the lithium ion battery is in a normal state; when the feature parameter is greater than R*.sub.i, it indicates that the lithium ion battery is in a risky state; a comprehensive feature threshold R*.sub.i is obtained through the following formula:
R*.sub.i=W.sub.TT.sub.n+W.sub.H.sub.2H.sub.2.sub.n+W.sub.COCO.sub.n+W.sub.SMSM.sub.n+W.sub.pP.sub.n, wherein W.sub.T, W.sub.H.sub.2, W.sub.CO, W.sub.SM, W.sub.p are weights of the battery temperature, the H2 concentration, the CO concentration, the smoke concentration, and the pressure, respectively, and T.sub.n, H.sub.2.sub.n, CO.sub.n, SM.sub.n, P.sub.n are normalized feature values of the battery temperature, the H2 concentration, the CO concentration, the smoke concentration, and the pressure, respectively.

4. The method according to claim 1, wherein in S5, clustering is performed using an algorithm to classify risk probability levels of thermal runaway of the lithium ion battery when different parameters are monitored during transportation; K objects are set through n samples, with each sample having three dimensions, distances d between an object X.sub.i and cluster centroids C.sub.j are compared in sequence, and jth dimension values of X.sub.i and C.sub.j are X.sub.it, C.sub.jt: $d\left(X_i,C_j\right)=\sqrt{{.Math.}_t^m\left(X_{\mathrm{it}}-C_{\mathrm{jt}}\right),}$ wherein C.sub.L is an Lth cluster centroid; N.sub.L is the number of samples in the Lth cluster centroid; a centroid point is recalculated with a mean value of all objects in a current category; $C_l=\frac{{.Math.}_{X_i}^{C_L}{X}_I}{N_l};$ the foregoing steps are repeated until the cluster centroid no longer changes, thereby completing clustering; meanwhile, the number of K of a cluster is determined accordingly; a change of a sum of squared errors (SSE) within the cluster with respect to the number of clusters is determined, $\mathrm{SSE}\left(k\right)={.Math.}_{i=1}^k{.Math.}_{C_i}{.Math.x-.Math.}^2,$ wherein SSE(k) is the SSE within the cluster, x is a data sample point, C.sub.i is an ith cluster where the data sample point x is located, and is a centroid of C.sub.i.

5. The method according to claim 1, wherein a data set is balanced to make the number of high-risk samples equivalent to the number of low-risk samples; according to each minority class sample x.sub.i, Euclidean distances between x.sub.i and all other minority class samples are calculated to obtain a neighbor interval range of the minority class sample; a sampling rate M is set by evaluating a degree of imbalance among categories in the data set, and several data samples are randomly selected from a neighborhood of x.sub.i according to the sampling rate M; the number of data samples is determined by M, and a selected neighboring data sample is recorded as x; a new minority class sample is generated using the selected x, wherein is a random number between 0 and 1, and a newly generated data sample is x.sub.n;
x.sub.n=x.sub.i+(xx.sub.i); and in S6, a learning algorithm is adopted; guess values of all samples are first initialized, and after a loss function is determined, predicted values of occurrence probabilities of thermal runaway and derivatives of the loss function are obtained; then, based on the foregoing values, a new decision tree is created, and predicted results of the new decision tree are added to the previous guess values; and finally, derivatives of the loss function are obtained again based on a second step; (1) an objective function combines a loss function S and a regularization term : the loss function measures a difference between the predicted value and an actual value, and the regularization term penalizes complexity of the model to prevent overfitting; y.sub.i is the actual value, .sub.i, is a predicted value of an ith instance, f.sub.k is a kth tree, and K is a total number of trees; the objective function for a given step is defined as: $\mathrm{Obj}={.Math.}_{i=1}^{n}S\left(y_i-{\stackrel{}{y}}_i\right)+{.Math.}_{k=1}^K\left(f_k\right);$ (2) gradient information: based on gradient boosting, an improvement is made to approximate curvature of the loss function accurately using first-order gradient information and second-order gradient information; (3) decision tree construction: for each decision tree, an optimal split point is found by enumerating all possible split points of all features; this process is based on calculation of structure scores, and the structure scores adopt gradient statistics of data points that fall into each split region, wherein L and R represent a left sub-region and a right sub-regions after splitting, respectively; T represents an entire region before splitting; and are regularization parameters; a gain obtained through splitting is given by the following formula: $\mathrm{Gain}=\frac{1}{2}\left[\frac{{\left({.Math.}_i^L{g}_i\right)}^2}{{.Math.}_i^L{h}_i+}+\frac{{\left({.Math.}_i^R{g}_i\right)}^2}{{.Math.}_i^R{h}_i+}-\frac{{\left({.Math.}_i^T{g}_i\right)}^2}{{.Math.}_i^T{h}_i+}\right]-.$

6. The method according to claim 1, wherein batter data is acquired by arranging the temperature sensors, the gas sensors, the pressure sensors, and the smoke sensors inside the transport case; the temperature sensors, the gas sensors, the smoke sensors, and the pressure sensors are non-contact sensors and are packaged inside respective housings.

7. The method according to claim 1, wherein the response unit is provided with a fire extinguishing release box installed under the case cover; when the data processing unit determines that thermal runaway of the lithium ion battery in the transport case occurs, the electrical signal is sent to open the release box, and the fire extinguishing material inside the release box falls to suppress flames emitted from the lithium ion battery.

8. The method according to claim 1, wherein the display panel is able to display the battery temperature, the gas concentration, the smoke concentration, and the pressure inside the transport case; the display light is an LED alarm light; when the data processing unit determines that thermal runaway of the lithium ion battery in the transport case occurs, the LED alarm light displays red and emits the photoelectric alarm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a diagram of a monitoring and early warning apparatus for a power lithium ion battery transport case according to the present invention;

(2) FIG. 2 is a layout diagram of an early warning system on a transport case cover;

(3) FIG. 3 is an early warning flowchart.

DETAILED DESCRIPTION

(4) For ease of understanding by a person skilled in that art, the present invention is further described below in conjunction with the embodiments, and the contents mentioned in the implementations are not intended to limit the present invention.

(5) A monitoring and early warning system for a power lithium ion battery transport case includes a sensing apparatus 1 and a data acquisition and transmission unit 4. A display unit 2 is connected to the data acquisition and transmission unit 4 through a lead, the data acquisition and transmission unit 4 sends an electrical signal to a response unit 5, and a power supply unit 3 supplies power to the entire system. The sensing apparatus 1 includes H2 sensors 11, CO sensors 12, infrared temperature sensors 13 and pressure sensors 14.

(6) A monitoring and early warning method based on the foregoing monitoring and early warning system for a power lithium ion battery transport case is provided, including the following specific steps.

(7) The lithium ion battery is placed in a transport case, temperature, vibration, impact, and humidity processing is performed on the transport case, and each transport condition is classified into three dimensions, i.e., low, normal, and high, to obtain different transport condition states X.sub.y(t) (temperature Tenv(t) T, vibration V(t), humidity S(t), and impact H(t)) of the battery in the transport case.
X.sub.y(t)=[Tenv(t),V(t),S(t),H(t)].

(8) Battery thermal runaway risk states X.sub.d (battery temperature T.sub.bat, H2 concentration H.sub.2 in the transport case, CO concentration CO in the transport case, smoke concentration SM in the transport case, and pressure P) and different battery monitoring states R.sub.y(t) under the transport conditions are monitored through the following formulas:

(9) X.sub.d(t)=[T.sub.bat(t),H.sub.2(t),CO(t),SM(t),P(t)]. The temperature, gas, smoke, and pressure in the transport case are monitored through different types of sensors of a monitoring unit, and the data is shown in the following table.

(10) TABLE-US-00002 TABLE 2 Monitoring data of monitoring and early warning system Transport case Sensor Serial Time Serial number (s) number Sensor type Value 1 00:00:14 1 Temperature 13 1 00:00:14 2 Temperature 13 1 00:00:14 01 CO 0 1 00:00:14 02 CO 0 1 00:00:15 010 Smoke 0 1 00:00:15 011 Smoke 0 1 00:00:15 0111 Pressure 0 . . . . . . . . . . . . . . .

(11) Various feature parameters of lithium ion battery risks are obtained, and acquired data is normalized through score calculation.

(12) Risk classification is performed by monitoring multi-information state data of the lithium ion battery, including the battery temperature and a temperature rise rate, the H2 concentration and a change rate, the CO concentration and a change rate, the smoke concentration and a change rate, and a pressure value and a change rate. The feature parameter thresholds of lithium ion battery thermal runaway risks under different transport conditions are calculated.

(13) TABLE-US-00003 TABLE 3 Feature parameter threshold of thermal runaway Monitoring Battery parameter temperature CO H.sub.2 Smoke Pressure Threshold 100 C. 10 ppm 10 ppm 10 ppm 0.25 kpa

(14) Thermal runaway risk scores of 5 feature metrics under 4 different transport conditions are calculated through the interquartile range method and normalization, and thermal runaway levels of the lithium ion battery are quantified accord to the scores.

(15) TABLE-US-00004 TABLE 4 Thermal runaway risk score Serial number Temperature H.sub.2 CO Smoke Pressure 1 0.006 0.164 0.164 0.000 0.000 2 0.187 0.210 0.210 0.000 0.000 3 0.075 0.000 0.000 0.000 0.000 4 0.049 0.142 0.142 0.000 0.001 5 0.247 0.249 0.249 0.000 0.000 . . . . . . . . . . . . . . . . . .

(16) A dynamic changing threshold R*.sub.i is comprehensively determined through the lithium ion battery thermal runaway risk parameters obtained through calculation to further determine a risk degree of the battery. When the feature parameter is lower than R*.sub.i, it indicates that the battery is in a normal state; when the feature parameter is greater than R*.sub.i, it indicates that the battery is in a risky state.

(17) First, weights of the battery temperature, the H2 concentration, the CO concentration, the smoke concentration, and the pressure are calculated.
W.sub.T=0.287, W.sub.H.sub.2=0.196, W.sub.CO=0.196, W.sub.SM=0.182, W.sub.p=0.139.

(18) T.sub.n, H.sub.2.sub.n, CO.sub.n, SM.sub.n, P.sub.n are normalized feature values of the battery temperature, the H2 concentration, the CO concentration, the smoke concentration, and the pressure, respectively. Therefore, a comprehensive feature threshold R*.sub.i is obtained through the following formula:
R*.sub.i=W.sub.TT.sub.n+W.sub.H.sub.2H.sub.2.sub.n+W.sub.COCO.sub.n+W.sub.SMSM.sub.n+W.sub.pP.sub.n=0.056.

(19) Clustering is performed using an algorithm to classify risk probability levels of thermal runaway of the lithium ion battery when different parameters are monitored during transportation; K objects are set through n samples, with each sample having three dimensions, distances d between an object X.sub.i and cluster centroids C.sub.j are compared in sequence, and jth dimension values of X.sub.i and C.sub.j are X.sub.it, C.sub.jt:

(20) $d\left(X_i,C_j\right)=\sqrt{{.Math.}_t^m\left(X_{\mathrm{it}}-C_{\mathrm{jt}}\right)}.$

(21) C.sub.L is an Lth cluster centroid; N.sub.L is the number of samples in the Lth cluster centroid; a centroid point is recalculated with a mean value of all objects in a current category.

(22) $C_l=\frac{{.Math.}_{X_i}^{C_L}{X}_I}{N_l}.$

(23) The foregoing steps are repeated until the cluster centroid no longer changes, thereby completing clustering. Meanwhile, through an elbow method, the number of categories K of a cluster is determined accordingly.

(24) Through the elbow method, an SSE within the cluster with respect to the number of clusters is evaluated, where x is a data sample point, C.sub.i is an ith cluster where the data sample point x is located, and is a centroid of C.sub.i.

(25) $\mathrm{SSE}\left(k\right)={.Math.}_{i=1}^k{.Math.}_{C_i}{.Math.x-.Math.}^2.$

(26) Clustering results are shown in the following table. It can be seen from the results in the table that for a category 1, the proportion is the lowest, and for a category 3, the proportion is the highest. Therefore, a normal state, a thermal runaway low risk, and a thermal runaway high risk are classified.

(27) TABLE-US-00005 TABLE 5 K-means clustering result Cluster Number Final cluster category of samples Proportion/% centroid 1 24 4.7% 0.33 2 179 34.9% 0.16 3 309 60.4% 0.06

(28) The number of samples of battery thermal runaway under transport conditions is small, and the entire data set is unevenly distributed. Therefore, the data set needs to be balanced to make the number of high-risk samples equivalent to the number of low-risk samples.

(29) According to each minority class sample x.sub.i, Euclidean distances between x.sub.i and all other minority class samples are calculated to obtain a neighbor interval range of the minority class sample.

(30) Secondly, a sampling rate M is set by evaluating a degree of imbalance among categories in the data set. The sampling rate is adjusted based on a current imbalance ratio. Next, several data samples are randomly selected from a neighborhood of x.sub.i according to the sampling rate M; the number of data samples is determined by M, and a selected neighboring data sample is recorded as x.

(31) Finally, a new minority class sample is generated using the selected x, where is a random number between 0 and 1, and a newly generated data sample is x.sub.n.
x.sub.n=x.sub.i+(xx.sub.i).

(32) A learning algorithm is adopted; guess values of all samples are first initialized, and after a loss function is determined, predicted values of occurrence probabilities of thermal runaway and derivatives of the loss function are obtained; then, based on the foregoing values, a new decision tree is created, and predicted results of the new decision tree are added to the previous guess values; and finally, derivatives of the loss function are obtained again based on a second step.

(33) (1) An objective function combines a loss function S and a regularization term : the loss function measures a difference between the predicted value and an actual value, and the regularization term penalizes complexity of the model to prevent overfitting. y.sub.i is the actual value, .sub.i is a predicted value of an ith instance, f.sub.k is a kth tree, and K is a total number of trees; the objective function for a given step is defined as:

(34) $\mathrm{Obj}={.Math.}_{i=1}^{n}S\left(y_i-{\stackrel{}{y}}_i\right)+{.Math.}_{k=1}^K\left(f_k\right).$

(35) (2) Gradient information: based on gradient boosting, an improvement is made to approximate curvature of the loss function accurately using first-order gradient information and second-order gradient information. For a given loss function S, gradients gi and hi of each instance are calculated as follows:
g.sub.i=.sub..sub.iS(y.sub.i,.sub.i), and h.sub.i=.sub..sub.i.sup.2S(y.sub.i,.sub.i).

(36) (3) Decision tree construction: for each decision tree, an optimal split point is found by enumerating all possible split points of all features; this process is based on calculation of structure scores, and the structure scores adopt gradient statistics of data points that fall into each split region, where L and R represent a left sub-region and a right sub-regions after splitting, respectively; T represents an entire region before splitting; and are regularization parameters; a gain obtained through splitting is given by the following formula:

(37) $\mathrm{Gain}=\frac{1}{2}\left[\frac{{\left({.Math.}_i^L{g}_i\right)}^2}{{.Math.}_i^L{h}_i+}+\frac{{\left({.Math.}_i^R{g}_i\right)}^2}{{.Math.}_i^R{h}_i+}-\frac{{\left({.Math.}_i^T{g}_i\right)}^2}{{.Math.}_i^T{h}_i+}\right]-.$

(38) For an imbalanced data-multi-classification identification model, the accuracy, precision, recall, F-score, and AUC value usually need to be used as evaluation metrics to perform macro average calculation and micro average calculation. Calculation is performed based on confusion matrix calculation metrics.

(39) (1) Accuracy: it represents the proportion of the number of correctly identified risk levels to the total number of samples (i.e., a sum of all elements of the confusion matrix). Calculation is performed using the low-risk category as an example, with other categories following the same method.

(40) $\mathrm{accuracy}=\frac{a_1+b_2+c_3}{a_1+a_2+.Math.+c_2+c_3}.$

(41) (2) Precision: it represents the proportion of the number of thermal runaway risk levels actually caused by a transport condition to the number of thermal runaway risk levels identified as being caused by this transport condition. Calculation is performed using the low-risk category as an example, with other categories following the same method.

(42) $\mathrm{precision}=\frac{a_1}{a_1+b_1+c_1}.$

(43) (3) Recall, also referred to as sensitivity: it represents the proportion of the number of actual risk levels to the number of identified risk levels. Calculation is performed using the low-risk category as an example, with other categories following the same method.

(44) $\mathrm{recall}=\frac{a_1}{a_1+a_2+a_3}.$

(45) (4) Comprehensive evaluation metric (F-score): a weighted harmonic mean of accuracy and precision is calculated.

(46) In particular, when a parameter is used, it becomes the most commonly used F1-score:

(47) $F-\mathrm{score}=\frac{\left(a^2+1\right).Math.\mathrm{precison}.Math.\mathrm{recall}}{a^2\left(\mathrm{precison}+\mathrm{recall}\right)}.$

(48) (5) Macro average involves calculating the evaluation metrics independently for each category and then averaging them so that macro average values of precision, recall, and F1-score may be calculated.

(49) 0$\mathrm{Macro}-F1=\frac{2\mathrm{Macro\_p}\mathrm{Macro\_r}}{\mathrm{Macro\_p}+\mathrm{Macro\_r}}.$

(50) These risk samples are labeled as three categories, i.e., 0, 1, and 2. Then, data modeling is performed in the Spyder environment using the Python programming language. First, a vehicle trajectory data set is imported by invoking numpy and pandas libraries, and feature metrics of all samples and the corresponding risk labels are read. Secondly, three algorithms, i.e., XGBoost, LGBM, and LCE, are introduced into a Sklearn machine learning library to directly construct a model. Common data set division ratios include 70% for a training set and 30% for a testing set, 80% for the training set and 20% for the testing set, or the like. A risky driving behavior identification model is established, and the performance of the model is evaluated. Model performance evaluation metrics are shown in Table 6.

(51) TABLE-US-00006 TABLE 6 Performance evaluation of ensemble learning algorithm (Macro) Model category Precision (%) Recall (%) F1-score AUC XGBoost 46.06 47.66 0.465 0.835

(52) Based on the thermal runaway risk identification model of the power lithium ion battery under transport conditions, the thermal runaway monitoring and early warning method for the lithium ion battery under different transport conditions is obtained, including a dynamic early warning threshold and two early warning processes.

(53) The specific steps are as follows.

(54) At S1, according to the volume of the transport case, several monitoring sections are arranged, including environmental parameters such as H2 concentration, CO concentration, monitoring point temperature, and pressure in the transport equipment. Sensors are arranged in the transport equipment to acquire data, and an early warning threshold is set.

(55) At S2, the non-contact temperature sensor is started to monitor the battery surface temperature and acquire the H2 concentration, CO concentration, monitoring point temperature, and environmental pressure data in real time. The real-time acquired data is compared and analyzed to determine whether a thermal runaway determination condition is met.

(56) At S3, when the battery temperature reaches the set threshold, it is determined that the thermal runaway low risk occurs, and the first-level early warning is triggered. In this case, a temperature value monitored by the non-contact temperature sensor rises abnormally, and the system will automatically turn on the H2 sensor, the CO sensor, the smoke sensor, and the pressure sensor and upload abnormal information to the remote monitoring platform.

(57) At S4, After the first-level early warning, a second-level early warning mechanism is started. The H2 sensor and the CO sensor are adopted to monitor the gas concentration during thermal runaway, the pressure sensor is adopted to monitor the environmental pressure in transport case, and the non-contact temperature sensor is adopted to monitor the monitoring point temperature. When any metric in the monitoring data exceeds the set threshold, the system will immediately send early warning information to the remote monitoring platform and start a corresponding response process.

(58) At S5, when the second-level early warning is triggered, that is, the thermal runaway high risk occurs, the system automatically selects an emergency plan and issues a disposal instruction to the driver through vehicle-mounted and ship-mounted terminals (applicable to road, railway, and waterway transportation). For air transportation, the early warning information is transmitted to the cockpit through aircraft's cargo hold Wi-Fi and responded to according to a predetermined emergency processing process.

(59) At S6, meanwhile, the system activates the top cover release box to start the fire extinguishing process through the internal fire extinguishing material automatic fire extinguisher to minimize accident losses caused by thermal runaway.

(60) It will be understood by a person skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by a person skilled in the art to which the present invention belongs. It will be further understood that terms such as those defined in general dictionaries are to be understood as having a meaning consistent with the meaning in the context of the related art and are not to be interpreted in an idealized or overly formal sense unless specifically defined as herein.

(61) It should be understood that the above detailed description of the technical solutions of the present invention using preferred embodiments is illustrative rather than restrictive. Based on reading the specification of the present invention, a person skilled in the art may modify the technical solutions recorded in the embodiments or make equivalent replacements to some of the technical features thereof. However, these modifications or replacements do not make the essence of the corresponding technical solutions detached from the spirit and scope of the technical solutions of the embodiments of the present invention.