CN118032228A - Method for effectively judging battery bulge by utilizing air pressure detection - Google Patents
Method for effectively judging battery bulge by utilizing air pressure detection Download PDFInfo
- Publication number
- CN118032228A CN118032228A CN202410196771.6A CN202410196771A CN118032228A CN 118032228 A CN118032228 A CN 118032228A CN 202410196771 A CN202410196771 A CN 202410196771A CN 118032228 A CN118032228 A CN 118032228A
- Authority
- CN
- China
- Prior art keywords
- air pressure
- pressure signal
- signal sequences
- monitoring
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000001514 detection method Methods 0.000 title claims abstract description 21
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 148
- 238000012544 monitoring process Methods 0.000 claims abstract description 98
- 238000004140 cleaning Methods 0.000 claims abstract description 24
- 230000008859 change Effects 0.000 claims description 39
- 238000004458 analytical method Methods 0.000 claims description 22
- 230000002159 abnormal effect Effects 0.000 claims description 21
- 238000009792 diffusion process Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 5
- 239000013589 supplement Substances 0.000 claims description 4
- 238000007726 management method Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 230000000875 corresponding effect Effects 0.000 description 11
- 230000008569 process Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000008961 swelling Effects 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Measuring Fluid Pressure (AREA)
Abstract
The invention discloses a method for effectively utilizing air pressure detection to judge battery bulge, which relates to the technical field of air pressure detection and comprises the following steps: arranging a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery; acquiring air pressure signals of a first air pressure sensor and a second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences; data cleaning is carried out on the plurality of air pressure signal sequences, and a plurality of monitoring air pressure signal sequences are obtained; transmitting the plurality of monitoring air pressure signal sequences to a processor for threshold judgment to obtain first early warning information; and sending the first early warning information to a user, and maintaining and managing the target battery. The invention solves the technical problems that the battery bulge cannot be detected efficiently in the prior art, and whether the battery bulge exists can be judged only by disassembling the machine, and the technical effect of detecting whether the battery bulge exists efficiently is achieved by installing the air pressure sensor.
Description
Technical Field
The invention relates to the technical field of air pressure detection, in particular to a method for effectively utilizing air pressure detection to judge battery bulge.
Background
The swelling of the lithium battery is also called swelling, and is a phenomenon in which the outer package of the battery swells and deforms due to gas generated by chemical reaction inside the battery. This situation may lead to degradation of the battery and even to safety problems, and thus needs to be recognized and handled in time. Lithium batteries are all installed inside electronic equipment, but traditional battery bulge judgment needs to be disassembled to see the batteries, and whether the batteries bulge can be timely found out.
Disclosure of Invention
The application provides a method for effectively utilizing air pressure detection to judge battery bulge, which is used for solving the technical problem that whether the battery bulge exists or not can not be judged by disassembling a machine in the prior art because the battery bulge cannot be detected efficiently.
In view of the above, the present application provides a method for effectively utilizing air pressure detection to realize judgment of battery bulge.
In a first aspect of the present application, there is provided a method for determining a battery bulge by effectively using air pressure detection, the method comprising:
Respectively arranging a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery; acquiring air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence; respectively carrying out data cleaning on the first air pressure signal sequences and the second air pressure signal sequences to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences; the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences are sent to a processor to judge a threshold value, and first early warning information is obtained, wherein a target difference value threshold value is stored in the processor; and sending the first early warning information to a user, and maintaining and managing the target battery.
In a second aspect of the present application, there is provided a system for determining battery bulge by effectively using air pressure detection, the system comprising:
The sensor layout module is used for respectively laying a first air pressure sensor and a second air pressure sensor at a first position and a second position of the target battery; the air pressure signal acquisition module acquires air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence; the data cleaning module is used for respectively carrying out data cleaning on the first air pressure signal sequences and the second air pressure signal sequences to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences; the threshold judgment module is used for sending the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to a processor to judge the threshold and obtain first early warning information, wherein a target difference value threshold is stored in the processor; and the first early warning information sending module sends the first early warning information to a user and performs maintenance management on the target battery.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application arranges a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery; acquiring air pressure signals of a first air pressure sensor and a second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences; data cleaning is carried out on the plurality of air pressure signal sequences, and a plurality of monitoring air pressure signal sequences are obtained; transmitting the plurality of monitoring air pressure signal sequences to a processor for threshold judgment to obtain first early warning information; and sending the first early warning information to a user, and maintaining and managing the target battery. The application solves the technical problems that the battery bulge cannot be detected efficiently in the prior art, and whether the battery bulge exists can be judged only by disassembling the machine, and the technical effect of detecting whether the battery bulge exists efficiently is achieved by installing the air pressure sensor.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for effectively utilizing air pressure detection to realize battery bulge judgment according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining a sensor interference factor in a method for effectively using air pressure detection to determine a battery bulge according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of a system for effectively utilizing air pressure detection to realize battery bulge determination according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a sensor layout module 11, an air pressure signal acquisition module 12, a data cleaning module 13, a threshold judgment module 14 and a first early warning information transmission module 15.
Detailed Description
The application provides a judging method for realizing battery bulge by effectively utilizing air pressure detection, which is used for solving the technical problem that whether the battery bulge can be judged only by disassembling a machine in the prior art, and achieves the technical effect of efficiently detecting whether the battery bulge is or not by installing an air pressure sensor.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a method for effectively using air pressure detection to determine a battery bulge, where the method includes:
step S100: respectively arranging a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery;
In an embodiment of the application, the first position is selected at or near the interior of the battery. The reason for this is that the change in air pressure inside the battery is a direct reflection of the change in the state of health of the battery. For example, when the battery bulges, the internal air pressure may change. When the sensor is arranged, enough contact area between the sensor and the inside of the battery needs to be ensured so as to acquire accurate air pressure data.
The second position is selected to be external or near the battery, for example on the housing of the battery. By doing so, the influence of the external environment of the battery on the battery, such as the change of air pressure possibly caused by the factors of temperature, humidity and the like, can be obtained. The choice of this location helps to understand the impact of the external environment on the battery and how these factors are reflected by changes in air pressure.
In selecting the air pressure sensor, factors such as accuracy, stability, response time and the like need to be considered. Accuracy and stability are critical to accurately capturing air pressure changes, while response time determines how fast the system can respond to air pressure changes.
It is necessary to ensure that the surface of the target cell is clean and free of dust, dirt or other impurities before the sensor is deployed so that the sensor can accurately measure air pressure. When the sensors are arranged, a first air pressure sensor and a second air pressure sensor are respectively arranged at a first position and a second position. Ensuring that the sensor is in close contact with the battery surface and is firmly secured against falling off or moving during monitoring. The leads of the sensor are connected to a data acquisition system or processor to enable transmission of the air pressure signal. And the firm and reliable connection needs to be ensured, and the condition of broken wires or poor contact in the monitoring process is avoided. After the installation is completed, the sensor is tested and calibrated by comparing with the equipment with known air pressure value or taking average value by measuring a plurality of times so as to ensure the accuracy and reliability.
Step S200: acquiring air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence;
In an embodiment of the present application, a plurality of monitoring windows are first set, which are predetermined time periods for specifying when to start and end data acquisition. Within each preset detection window, the system collects air pressure signals from the first air pressure sensor and the second air pressure sensor. These signals are continuous, time-varying data reflecting the internal and external air pressure conditions of the battery. The process of data acquisition needs to ensure the accuracy and integrity of the data, avoiding missing or erroneous data.
The collected air pressure signals are converted into digital signals and are arranged according to a time sequence to form a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences. Each sequence corresponds to a monitoring window containing all of the barometric pressure data within the window.
The first air pressure signal sequences and the second air pressure signal sequences are in one-to-one correspondence, namely each first air pressure signal sequence is provided with a corresponding second air pressure signal sequence, and the time sequences of the first air pressure signal sequences and the second air pressure signal sequences are synchronous.
Step S300: respectively carrying out data cleaning on the first air pressure signal sequences and the second air pressure signal sequences to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences;
In the embodiment of the application, data cleaning is an important step of data preprocessing, and aims to eliminate irrelevant information such as noise, abnormal value, repeated value and the like in original data and improve the quality and usability of the data. For the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences collected from the air pressure sensor, the data cleaning is particularly critical, and the accuracy and the reliability of subsequent analysis can be ensured through the data cleaning.
When data cleaning is performed on the air pressure signal, noise is first removed. The air pressure signal contains noise introduced by the sensor itself, environmental factors or during data transmission. These noise results in large data fluctuations, affecting the accuracy of the analysis results. Therefore, it is necessary to smooth data by a filter such as a moving average filter, a low-pass filter, or the like, to reduce the influence of noise. The outliers are then processed. Outliers are caused by sensor failures, data acquisition errors, or other incidents. These values are often quite different from normal data and may be misleading for subsequent analysis if not processed. When the abnormal value is processed, a reasonable threshold range is set, and the value exceeding the range is regarded as the abnormal value and is processed, or the abnormal value is identified by a method such as standard deviation, quartile and the like and is processed correspondingly.
In some cases, repeated data points may occur due to sensor failure or problems with the data acquisition system. These repeated values are not valuable for analysis and need to be removed. If data at some point in time is missing, interpolation of the data is required. Interpolation methods include using values of previous or next data points, linear interpolation, polynomial interpolation, etc. The selection of the appropriate interpolation method depends on the nature of the data and the analysis requirements. In order to eliminate dimensional differences between different air pressure signal sequences, data are standardized or normalized. Normalization is the conversion of data into a distribution with a mean of 0 and standard deviation of 1, while normalization is the scaling of data to a range of 0,1 or-1, 1. To capture the locally varying characteristics of the barometric pressure signal, the data needs to be segmented or windowed.
After the data is cleaned, the processed data is verified and checked by drawing a chart, calculating statistical indexes and the like, so that the quality and accuracy of the data are ensured to meet the analysis requirement.
After the data cleaning step, the original first air pressure signal sequences and the second air pressure signal sequences are converted into first monitoring air pressure signal sequences and second monitoring air pressure signal sequences.
Step S400: the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences are sent to a processor to judge a threshold value, and first early warning information is obtained, wherein a target difference value threshold value is stored in the processor;
In the embodiment of the application, a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences which are obtained after data cleaning are firstly sent to a processor, and the process relates to data transmission and storage, so that the data can reach the processor accurately. In the processor, the received data needs to undergo further preprocessing, including format conversion, resampling, alignment, etc. of the data to ensure consistency and comparability of the data.
A target difference threshold is preset in the processor. This threshold is set based on knowledge of the change in air pressure during normal operation of the battery, and predictions of the range of air pressure changes over which the battery may be experiencing problems. The target difference threshold serves as a basis for evaluating whether the air pressure change is out of the normal range.
After the data preprocessing is completed, the processor compares the difference value between each first monitoring air pressure signal sequence and the corresponding second monitoring air pressure signal sequence one by one, and the difference value reflects the change relation between the internal air pressure and the external air pressure of the battery. The processor calculates these differences and compares them to a target difference threshold. If the difference value at a certain time point exceeds the target difference value threshold, the processor marks the difference value as abnormal and generates corresponding early warning information. The early warning information comprises information such as the occurrence time point of the abnormality, the degree of the abnormality, possible reasons and the like.
Based on the judgment result, the processor generates first early warning information.
Step S500: and sending the first early warning information to a user, and maintaining and managing the target battery.
In the embodiment of the application, the processor sends the generated first early warning information to the corresponding receiving equipment or system so that the related personnel can timely receive the early warning information and take corresponding actions. The early warning information is sent by means of network, short message, mail and the like.
After receiving the first early warning information, the user needs to confirm the information as soon as possible and take corresponding measures according to early warning content. And checking the battery state according to the early warning content, and checking the basic conditions such as appearance, connection, temperature and the like of the battery so as to preliminarily judge the severity of the problem. A review of the battery's operating manual, maintenance guidelines, or previous maintenance records is required to learn about historical problems and possible solutions.
Based on the content of the pre-warning information and the preliminary evaluation of the user, specific maintenance management actions are required next. Including adjusting or replacing sensors, cleaning or replacing batteries, and system calibration or restarting.
Further, as shown in fig. 2, step S100 in the method provided in the application embodiment further includes:
Calculating life coefficients of the first air pressure sensor and the second air pressure sensor to obtain a first life coefficient and a second life coefficient;
collecting interference source information of the target battery to obtain an interference source information set;
matching a first interference coefficient based on the set of interferer information;
And carrying out sensor interference analysis according to the first interference coefficient, the first life coefficient and the second life coefficient to obtain a first sensor interference factor and a second sensor interference factor.
In the embodiment of the application, when the life coefficient of the first air pressure sensor is calculated, firstly, the historical data of the first air pressure sensor is collected, including performance parameters, working environment, use frequency and the like of the first air pressure sensor in the use process. Based on the collected data, performance changes of the sensor are evaluated, including indicators of reduced accuracy, slower response speed, increased failure rate, and the like. And establishing a life model according to the performance evaluation result, wherein the life model is established based on an empirical formula. And calculating a life coefficient of the first air pressure sensor by using the life model, wherein the life coefficient is a numerical value and reflects the performance attenuation degree of the sensor relative to a brand new state.
And similarly, collecting historical data of the second air pressure sensor, evaluating performance change of the second air pressure sensor, establishing a life model of the second air pressure sensor, and calculating a life coefficient of the second air pressure sensor.
In performing the interference source information collection, various interference sources that may affect the target battery, such as temperature change, humidity change, external impact, electromagnetic interference, and the like, are determined. The interference source is monitored and data acquired in real time using appropriate sensors and instrumentation. The tools used include temperature sensors, humidity sensors, electromagnetic field measuring instruments, and the like. The collected interference source data is stored in a reliable data storage medium for subsequent analysis.
A database is established containing various interference source information and its corresponding interference coefficients. And according to the acquired interference source information, a matching algorithm such as fuzzy matching, nearest neighbor algorithm and the like is used for searching corresponding interference coefficients in a database. And obtaining an interference coefficient which is most matched with the acquired interference source information from the database as a first interference coefficient.
A model suitable for analyzing sensor interference is selected based on statistical rules, wherein the model considers the influence of a first interference coefficient, a first life coefficient and a second life coefficient. A simple linear model is used here, sensor interference factor=α×interference coefficient+β×lifetime coefficient. And determining the weight of each factor in the model according to the historical data and the environmental conditions.
Substituting the first interference coefficient and the first life coefficient into an interference analysis model to calculate an interference factor of the first sensor. Similarly, the first interference coefficient and the second life coefficient are substituted into the model, and the interference factor of the second sensor is calculated.
Further, the method further comprises:
collecting the first air pressure sensor model information and the second air pressure sensor model information;
performing signal interference analysis according to the first air pressure sensor model information and the first sensor interference factor to obtain a first signal diffusion threshold;
performing signal interference analysis according to the second air pressure sensor signal information and the second sensor interference factor to obtain a second signal diffusion threshold;
And performing data cleaning on the first air pressure signal sequences and the second air pressure signal sequences by using the first signal diffusion threshold and the second signal diffusion threshold to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences.
In the embodiment of the application, the first air pressure sensor model information and the second air pressure sensor model information are obtained by consulting the specification and the technical manual of the sensor or directly inquiring the internal information of the sensor through the sensor interface, and the obtained model information comprises important parameters such as a manufacturer, a model number, an accuracy grade, a measuring range, a power supply requirement and the like of the sensor.
And identifying external factors such as electromagnetic interference, mechanical vibration, temperature change and the like which interfere with the first air pressure sensor by combining the acquired interference source information set. The effect of these disturbances on the sensor output is quantified using a first sensor disturbance factor. The interference factor is a numerical value reflecting the degree of interference of different interference sources with the sensor output at different intensities. Based on the sensor model information and the interference factors, a simulation model is established for simulating the output signals of the sensor under different interference conditions. This model may be a physical model, a mathematical model or a software-based simulation model. And simulating sensor output under different interference scenes by adjusting parameters in the simulation model. The adjustment of the model parameters includes different interference intensities, interference frequencies, interference durations, etc. And analyzing the simulation result, and observing the change condition of the sensor output signal under different interference scenes. These variations may manifest themselves as drift in the signal, increased noise, or periodic fluctuations, etc. A reasonable signal diffusion threshold, namely a first signal diffusion threshold, is set according to the observed signal change condition.
Similarly, a second signal spreading threshold is determined based on the results of the second signal interference analysis.
When the data of the first air pressure signal sequences and the second air pressure signal sequences are cleaned, each data point in each first air pressure signal sequence and each second air pressure signal sequence is compared with a corresponding signal diffusion threshold value, and if the value of the data point exceeds the threshold value range, the data point is marked as an abnormal point. In processing an exception signal, one simple approach is to delete those data points marked as being abnormal directly. This ensures that each data point in the dataset is valid. For the case where data continuity needs to be maintained, interpolation methods such as linear interpolation, polynomial interpolation, etc. may be employed to estimate reasonable values of outliers. Another approach is to use smoothing algorithms such as moving averages, filters, etc. to reduce the effects of noise and outliers. And then carrying out quality inspection to ensure that the washed data sequence is kept continuous in time without obvious fracture or jump. And calculating indexes such as mean value, standard deviation and correlation of the data, ensuring that the indexes are in a reasonable range and are consistent with the expected air pressure signal characteristics.
After the above processing steps, the resulting data set will contain the cleaned and corrected first and second air pressure signal sequences. These data more accurately reflect the state and performance of the battery. These cleaned data sequences are named as a plurality of first monitored air pressure signal sequences and a plurality of second monitored air pressure signal sequences, respectively.
Further, the method further comprises:
detecting whether missing values exist in the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences based on time sequence;
If so, interpolation calculation is performed to supplement missing values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences.
In the embodiment of the application, time sequence continuity analysis is firstly carried out on each monitoring air pressure signal sequence. Including checking the time stamp of each data point to ensure that they are continuous and have no jumps. In the timing analysis, those missing data points are identified. The missing values may appear as blank, non-numeric values in the time series, or other special values that identify the missing.
The effect of the deletion value on the signal sequence was evaluated. Missing values may result in data discontinuities, affecting the accuracy of subsequent analysis. Based on the result of the impact analysis, it is determined whether interpolation calculation is required to supplement the missing value. Interpolation may not be required if the missing values are less and the overall data is not greatly affected; but if the missing values are more or the effect is significant, interpolation is necessary.
And selecting a proper interpolation method according to the property, the number and the characteristics of the signal sequence of the missing values. Common interpolation methods include linear interpolation, polynomial interpolation, spline interpolation, and the like. Where linear interpolation is selected to interpolate the missing values, the missing values are typically estimated using adjacent valid data points.
The accuracy and rationality of the interpolation result are verified by comparing the data sequences before and after interpolation, calculating interpolation errors, analyzing the statistical characteristics of the data after interpolation and the like. If the interpolation result is not ideal, the parameters of the interpolation method need to be adjusted or different interpolation methods need to be tried.
And replacing the missing value in the original data sequence with the interpolation calculation result, and updating the stored data set to ensure that the missing value is correctly supplemented.
Through the steps, the integrity and the accuracy of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences can be ensured,
Further, step S400 in the method provided in the application embodiment further includes:
respectively extracting modes of the first monitoring air pressure signal sequences to obtain a plurality of first numerical values;
constructing a first monitoring change curve by utilizing the first mode values based on time sequence;
Calculating the change rate of the first monitoring change curve to obtain a first monitoring change rate;
And matching a first difference threshold according to the first monitoring change rate.
In the embodiment of the application, for each first monitoring air pressure signal sequence, the frequency of occurrence of each value is counted, and the value with the largest occurrence number, namely the mode of the sequence, is found out. The mode of each first monitoring air pressure signal sequence is recorded, and a plurality of first numerical values are obtained.
The extracted first mode values are arranged in time sequence, so that each mode value is ensured to have a corresponding time stamp. With these data points, a first monitored change curve is plotted on the timing graph, which curve will exhibit a mode trend over time. In order to clearly show the variation trend, the curve is subjected to smoothing treatment to obtain a first monitoring variation curve.
The change rate is the slope change of the curve at different points, and the fluctuation degree of the curve can be seen and is used for quantifying the dynamic characteristics of the data. For adjacent data points on the first monitored change curve, the difference between them is calculated and divided by the time interval to obtain the rate of change. And recording the change rate of each time period to obtain a first monitoring change rate.
The difference threshold is a numerical criterion for determining whether a change in data is significant. A first difference threshold is determined based on historical first monitored rate of change data by analyzing statistics of rate of change distribution, mean, standard deviation, etc. in the historical data.
Further, the method comprises the steps of:
Obtaining a second difference threshold based on the plurality of second monitored barometric pressure signal sequences;
Calculating the average value of the first difference value threshold and the second difference value threshold to obtain a target difference value threshold;
And storing the target difference threshold value into the processor.
In the embodiment of the application, each second monitoring air pressure signal sequence is analyzed, and key characteristics such as average value, standard deviation, change trend and the like are extracted. Any outliers or noise are identified and processed to ensure data quality. The threshold is determined using statistical methods, such as based on multiples of the standard deviation. For example, the second difference threshold may be set to a multiple of the standard deviation of the second monitored air pressure signal sequence.
Once the first and second difference thresholds are determined, the average of the two thresholds is then calculated, and the two or more thresholds are combined by the average calculation to obtain the target difference threshold.
And converting the calculated target difference value threshold value into a data format which can be recognized by a processor. The target difference threshold is stored in a memory unit of the processor via an appropriate interface or protocol. After storage, the necessary verification and testing is performed to ensure that the target difference threshold has been properly stored and can be properly read and used by the processor.
Further, step S400 in the method provided in the application embodiment further includes:
Respectively calculating the difference values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to obtain a plurality of air pressure signal difference value sets;
The multiple air pressure signal difference value sets are sent to the processor to be compared with the target difference value threshold value, and multiple abnormal difference value amounts are obtained;
and judging whether the plurality of abnormal difference values exceed a preset abnormal difference value threshold value, and if so, acquiring the first early warning information.
In the embodiment of the application, the first monitoring air pressure signal sequence and the second monitoring air pressure signal sequence are aligned in time, namely, the data of each time point can be matched. For each time point, a corresponding first monitored air pressure signal value and second monitored air pressure signal value are obtained. For each time point, a difference between the first monitored barometric pressure signal value and the second monitored barometric pressure signal value is calculated. The difference may be obtained by a subtraction operation, i.e. difference = first monitored air pressure signal value-second monitored air pressure signal value. The differences at all time points are recorded, and a plurality of air pressure signal difference sets are obtained.
The plurality of sets of barometric pressure signal differences are sent to a processor where the differences in each set of differences are compared to a target difference threshold. This is achieved by a simple numerical comparison, i.e. checking if the difference exceeds or falls below a target difference threshold. If the difference at a certain point in time exceeds the target difference threshold, then the point in time is considered to have an abnormal amount of difference. A plurality of abnormal difference amounts are obtained by comparison.
To further determine the severity of the abnormal condition, a predetermined abnormal difference threshold is set. This threshold is determined based on historical data, traffic demand, or other relevant factors. If the anomaly differential exceeds a preset anomaly differential threshold, then a significant anomaly condition may be determined to exist.
The processor generates first warning information upon detection of a significant anomaly. The first warning message is a warning signal, an error code or any other form of notification that indicates to the system administrator or operator to notice the abnormal situation and take the necessary action.
In summary, the embodiment of the application has at least the following technical effects:
The application arranges a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery; acquiring air pressure signals of a first air pressure sensor and a second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences; data cleaning is carried out on the plurality of air pressure signal sequences, and a plurality of monitoring air pressure signal sequences are obtained; transmitting the plurality of monitoring air pressure signal sequences to a processor for threshold judgment to obtain first early warning information; and sending the first early warning information to a user, and maintaining and managing the target battery. The application solves the technical problems that the battery bulge cannot be detected efficiently in the prior art, and whether the battery bulge exists can be judged only by disassembling the machine, and the technical effect of detecting whether the battery bulge exists efficiently is achieved by installing the air pressure sensor.
Example two
Based on the same inventive concept as the method for effectively using air pressure detection to determine the battery bulge in the foregoing embodiments, as shown in fig. 3, the present application provides a system for effectively using air pressure detection to determine the battery bulge. Wherein the system comprises:
a sensor arrangement module 11, wherein the sensor arrangement module 11 respectively arranges a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery;
The air pressure signal acquisition module 12 acquires air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows, and a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences are obtained, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence;
the data cleaning module 13 is configured to perform data cleaning on the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences respectively by the data cleaning module 13 to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences;
the threshold value judging module 14, wherein the threshold value judging module 14 sends the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to a processor to judge the threshold value, and first early warning information is obtained, and the processor stores a target difference value threshold value;
and the first early warning information sending module 15 sends the first early warning information to a user, and maintains and manages the target battery.
Further, the system further comprises:
Calculating life coefficients of the first air pressure sensor and the second air pressure sensor to obtain a first life coefficient and a second life coefficient;
collecting interference source information of the target battery to obtain an interference source information set;
matching a first interference coefficient based on the set of interferer information;
And carrying out sensor interference analysis according to the first interference coefficient, the first life coefficient and the second life coefficient to obtain a first sensor interference factor and a second sensor interference factor.
Further, the system further comprises:
collecting the first air pressure sensor model information and the second air pressure sensor model information;
performing signal interference analysis according to the first air pressure sensor model information and the first sensor interference factor to obtain a first signal diffusion threshold;
performing signal interference analysis according to the second air pressure sensor signal information and the second sensor interference factor to obtain a second signal diffusion threshold;
And performing data cleaning on the first air pressure signal sequences and the second air pressure signal sequences by using the first signal diffusion threshold and the second signal diffusion threshold to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences.
Further, the system further comprises:
detecting whether missing values exist in the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences based on time sequence;
If so, interpolation calculation is performed to supplement missing values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences.
Further, the system further comprises:
respectively extracting modes of the first monitoring air pressure signal sequences to obtain a plurality of first numerical values;
constructing a first monitoring change curve by utilizing the first mode values based on time sequence;
Calculating the change rate of the first monitoring change curve to obtain a first monitoring change rate;
And matching a first difference threshold according to the first monitoring change rate.
Further, the system further comprises:
Obtaining a second difference threshold based on the plurality of second monitored barometric pressure signal sequences;
Calculating the average value of the first difference value threshold and the second difference value threshold to obtain a target difference value threshold;
And storing the target difference threshold value into the processor.
Further, the system further comprises:
Respectively calculating the difference values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to obtain a plurality of air pressure signal difference value sets;
The multiple air pressure signal difference value sets are sent to the processor to be compared with the target difference value threshold value, and multiple abnormal difference value amounts are obtained;
and judging whether the plurality of abnormal difference values exceed a preset abnormal difference value threshold value, and if so, acquiring the first early warning information.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. The method for effectively utilizing the air pressure detection to judge the battery bulge is characterized by further comprising the following steps:
respectively arranging a first air pressure sensor and a second air pressure sensor at a first position and a second position of a target battery;
Acquiring air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence;
Respectively carrying out data cleaning on the first air pressure signal sequences and the second air pressure signal sequences to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences;
The first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences are sent to a processor to judge a threshold value, and first early warning information is obtained, wherein a target difference value threshold value is stored in the processor;
and sending the first early warning information to a user, and maintaining and managing the target battery.
2. The method of claim 1, wherein the method further comprises:
Calculating life coefficients of the first air pressure sensor and the second air pressure sensor to obtain a first life coefficient and a second life coefficient;
collecting interference source information of the target battery to obtain an interference source information set;
matching a first interference coefficient based on the set of interferer information;
And carrying out sensor interference analysis according to the first interference coefficient, the first life coefficient and the second life coefficient to obtain a first sensor interference factor and a second sensor interference factor.
3. The method of claim 2, wherein the obtaining the first sensor interference factor and the second sensor interference factor is followed by the method further comprising:
collecting the first air pressure sensor model information and the second air pressure sensor model information;
performing signal interference analysis according to the first air pressure sensor model information and the first sensor interference factor to obtain a first signal diffusion threshold;
performing signal interference analysis according to the second air pressure sensor signal information and the second sensor interference factor to obtain a second signal diffusion threshold;
And performing data cleaning on the first air pressure signal sequences and the second air pressure signal sequences by using the first signal diffusion threshold and the second signal diffusion threshold to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences.
4. A method as claimed in claim 3, wherein the method further comprises:
detecting whether missing values exist in the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences based on time sequence;
If so, interpolation calculation is performed to supplement missing values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences.
5. The method of claim 1, wherein the method further comprises:
respectively extracting modes of the first monitoring air pressure signal sequences to obtain a plurality of first numerical values;
constructing a first monitoring change curve by utilizing the first mode values based on time sequence;
Calculating the change rate of the first monitoring change curve to obtain a first monitoring change rate;
And matching a first difference threshold according to the first monitoring change rate.
6. The method of claim 5, wherein the method further comprises:
Obtaining a second difference threshold based on the plurality of second monitored barometric pressure signal sequences;
Calculating the average value of the first difference value threshold and the second difference value threshold to obtain a target difference value threshold;
And storing the target difference threshold value into the processor.
7. The method of claim 1, wherein the method further comprises:
Respectively calculating the difference values of the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to obtain a plurality of air pressure signal difference value sets;
The multiple air pressure signal difference value sets are sent to the processor to be compared with the target difference value threshold value, and multiple abnormal difference value amounts are obtained;
and judging whether the plurality of abnormal difference values exceed a preset abnormal difference value threshold value, and if so, acquiring the first early warning information.
8. A system for efficiently utilizing air pressure detection to determine battery bulge, said system comprising:
The sensor layout module is used for respectively laying a first air pressure sensor and a second air pressure sensor at a first position and a second position of the target battery;
The air pressure signal acquisition module acquires air pressure signals of the first air pressure sensor and the second air pressure sensor in a plurality of preset monitoring windows to obtain a plurality of first air pressure signal sequences and a plurality of second air pressure signal sequences, wherein the plurality of first air pressure signal sequences and the plurality of second air pressure signal sequences are in one-to-one correspondence;
The data cleaning module is used for respectively carrying out data cleaning on the first air pressure signal sequences and the second air pressure signal sequences to obtain a plurality of first monitoring air pressure signal sequences and a plurality of second monitoring air pressure signal sequences;
The threshold judgment module is used for sending the first monitoring air pressure signal sequences and the second monitoring air pressure signal sequences to a processor to judge the threshold and obtain first early warning information, wherein a target difference value threshold is stored in the processor;
and the first early warning information sending module sends the first early warning information to a user and performs maintenance management on the target battery.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410196771.6A CN118032228A (en) | 2024-02-22 | 2024-02-22 | Method for effectively judging battery bulge by utilizing air pressure detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410196771.6A CN118032228A (en) | 2024-02-22 | 2024-02-22 | Method for effectively judging battery bulge by utilizing air pressure detection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118032228A true CN118032228A (en) | 2024-05-14 |
Family
ID=91003614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410196771.6A Pending CN118032228A (en) | 2024-02-22 | 2024-02-22 | Method for effectively judging battery bulge by utilizing air pressure detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118032228A (en) |
-
2024
- 2024-02-22 CN CN202410196771.6A patent/CN118032228A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110647133B (en) | Rail transit equipment state detection maintenance method and system | |
| EP1242923B1 (en) | A process for the monitoring and diagnostics of data from a remote asset | |
| CN110046453B (en) | Service life prediction method of laser radar | |
| CA2508008C (en) | A method for developing a unified quality assessment and providing an automated fault diagnostic tool for turbine machine systems and the like | |
| US20100100337A1 (en) | Method to extract parameters from in-situ monitored signals for prognostices | |
| CN112925279A (en) | Fault comprehensive analysis system based on MES system | |
| CN103154693A (en) | System for monitoring an engine test bed | |
| US11941521B2 (en) | Vibrating machine automated diagnosis with supervised learning | |
| CN117082105B (en) | Environment-friendly intelligent hospital facility monitoring system and method | |
| CN117134503B (en) | State monitoring method and system for large-scale power supply device | |
| KR102545672B1 (en) | Method and apparatus for machine fault diagnosis | |
| CN112232405A (en) | Fault prediction, monitoring and diagnosis method of gearbox and corresponding device | |
| CN112580858A (en) | Equipment parameter prediction analysis method and system | |
| KR20220132824A (en) | Distribution facility condition monitoring system and method | |
| CN117609716A (en) | Power equipment residual life remote prediction method and system based on state health information | |
| CN117330948A (en) | Online monitoring method and system for mechanical characteristics of circuit breaker | |
| CN118032228A (en) | Method for effectively judging battery bulge by utilizing air pressure detection | |
| CN116500441A (en) | Motor fault detection and positioning method and system | |
| CN206656749U (en) | Heat-engine plant sensor fault diagnosis system | |
| CN114063601A (en) | Equipment state diagnosis system and method based on artificial intelligence | |
| CN117589444B (en) | Wind driven generator gear box fault diagnosis method based on federal learning | |
| KR20090119471A (en) | Condition diagnosis system using various signals for shoot blasting impeller motors | |
| CN116738859B (en) | Online nondestructive life assessment method and system for copper pipe | |
| CN117591507B (en) | Automatic construction method and system for data quality characteristics of measurement and control equipment | |
| CN117689269B (en) | Fault tracing method for intelligent watch processing stage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |