CN116448209A - Liquid level identification method and device based on pressure sensor and electronic equipment - Google Patents
Liquid level identification method and device based on pressure sensor and electronic equipment Download PDFInfo
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- 239000007788 liquid Substances 0.000 title claims abstract description 68
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- 238000012549 training Methods 0.000 claims description 22
- 238000007781 pre-processing Methods 0.000 claims description 21
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/14—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measurement of pressure
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Abstract
The invention relates to the technical field of liquid level identification, in particular to a liquid level identification method, a liquid level identification device and electronic equipment based on a pressure sensor, which are used for analyzing and processing fluid pressure data acquired by the pressure sensor through a trained fluid liquid level identification model to accurately identify the fluid level, the invention is convenient to maintain the target object or modify the established scheme according to the identification result, has simple equipment requirement, reduces the resource consumption and reduces the manpower resource expenditure, and meanwhile, the invention can realize the prediction of the future fluid level through the trained fluid level prediction model, realize the early warning effect on the user and avoid unnecessary risks in the past.
Description
Technical Field
The present invention relates to the field of liquid level identification technologies, and in particular, to a liquid level identification method and apparatus based on a pressure sensor, and an electronic device.
Background
The liquid level is one of the most common control parameters in industrial production and life scenes, the accuracy of liquid level identification directly influences the state control of products and the adjustment of schemes, such as the identification of liquid replenishing of a storage battery, the liquid level of an oil tank, the water level of a river and the like, the identification result influences the subsequent control of liquid replenishing of the storage battery, the abnormal evaluation of the state of the oil tank, the adjustment of an emergency scheme caused by rapid growth of the liquid level of the river and the like, and the liquid level is identified in the prior art in a measuring scale or image identification mode, so that the equipment is more in application, larger human resources are consumed for observation and measurement, and larger errors are easy to exist in the accuracy of the liquid level measurement result.
Disclosure of Invention
The invention provides a liquid level identification method, a liquid level identification device and electronic equipment based on a pressure sensor, which solve the problems of more equipment application, more manpower consumption and larger error of liquid level measurement results during liquid level identification.
The embodiment of the specification provides a liquid level identification method based on a pressure sensor, which comprises the following steps:
acquiring historical fluid data in a target object, wherein the historical fluid data comprises historical fluid pressure data and historical fluid liquid level data;
preprocessing the historical fluid data, and extracting features of the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features;
constructing an initial fluid level identification model, and training the initial fluid level identification model based on the historical fluid pressure characteristics and the historical fluid level characteristics to obtain a trained fluid level identification model;
collecting fluid pressure data in a target object in real time by using a pressure sensor;
preprocessing the fluid pressure data, and extracting the characteristics of the preprocessed fluid pressure data to obtain fluid pressure characteristics;
inputting the fluid pressure characteristics into a trained fluid level identification model to obtain the fluid level in the target object.
Preferably, the method further comprises:
judging the fluid level in the target object based on a preset liquid level identification criterion;
and when the fluid level in the target object is lower than a first preset fluid level value or higher than a second preset fluid level value, sending a warning message to a user.
Preferably, the historical fluid data further comprises trend data of historical fluid pressure data over time, the method further comprising:
preprocessing trend data of the historical fluid pressure data along with time, and extracting features of the preprocessed trend data of the historical fluid pressure data along with time to obtain time features of the historical fluid pressure;
and constructing an initial fluid level prediction model, and training the initial fluid level prediction model based on the historical fluid pressure characteristic, the historical fluid pressure time characteristic and the historical fluid level characteristic to obtain a trained fluid level prediction model.
Preferably, the method further comprises:
the method comprises the steps of collecting fluid pressure data in a target object in real time by using a pressure sensor, and recording trend data of the fluid pressure data in the target object along with time change;
respectively extracting characteristics of the fluid pressure data and trend data of the fluid pressure data changing along with time to obtain the fluid pressure characteristics and fluid pressure time characteristics;
and inputting the fluid pressure characteristic and the fluid pressure time characteristic into a trained fluid liquid level prediction model to obtain a predicted fluid liquid level in the target object.
Preferably, the preprocessing the historical fluid data includes:
performing data cleaning on the historical fluid data;
carrying out data integration on the cleaned historical fluid data;
performing data smoothing processing on the integrated historical fluid data;
and performing data reduction processing on the smoothed historical fluid data.
Preferably, the method further comprises:
judging the fluid level in the target object based on a preset liquid level prediction criterion;
and when the predicted fluid level in the target object is lower than a third preset fluid level value or higher than a fourth preset fluid level value, sending a warning message to a user.
The embodiment of the specification also provides a liquid level identification device based on a pressure sensor, which comprises:
the data acquisition module is used for acquiring historical fluid data in the target object, wherein the historical fluid data comprises historical fluid pressure data and historical fluid liquid level data;
the data processing module is used for preprocessing the historical fluid data and extracting features of the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features;
the model training module is used for constructing an initial fluid level identification model, training the initial fluid level identification model based on the historical fluid pressure characteristics and the historical fluid level characteristics, and obtaining a trained fluid level identification model;
the data acquisition module is used for acquiring fluid pressure data in the target object in real time by utilizing the pressure sensor;
the characteristic extraction module is used for preprocessing the fluid pressure data, and extracting the characteristics of the preprocessed fluid pressure data to obtain fluid pressure characteristics;
and the fluid level determining module is used for inputting the fluid pressure characteristics into a trained fluid level identification model to obtain the fluid level in the target object.
Preferably, the apparatus further comprises:
the first liquid level judging module is used for judging the liquid level of the fluid in the target object based on a preset liquid level identification criterion;
the first warning module is used for sending warning information to a user when the fluid level in the target object is lower than a first preset fluid level value or higher than a second preset fluid level value.
Preferably, the historical fluid data further comprises trend data of historical fluid pressure data over time, the method further comprising:
the historical fluid pressure time feature extraction module is used for preprocessing trend data of the historical fluid pressure data along with time, and extracting features of the preprocessed trend data of the historical fluid pressure data along with time to obtain historical fluid pressure time features;
the fluid level prediction model construction module is used for constructing an initial fluid level prediction model, and training the initial fluid level prediction model based on the historical fluid pressure characteristics, the historical fluid pressure time characteristics and the historical fluid level characteristics to obtain a trained fluid level prediction model.
Preferably, the apparatus further comprises:
the data recording module is used for acquiring the fluid pressure data in the target object in real time by utilizing the pressure sensor and recording trend data of the change of the fluid pressure data in the target object along with time;
the characteristic acquisition module is used for respectively carrying out characteristic extraction on the fluid pressure data and trend data of the fluid pressure data changing along with time to obtain the fluid pressure characteristic and the fluid pressure time characteristic;
and the predicted fluid level module is used for inputting the fluid pressure characteristic and the fluid pressure time characteristic into a trained fluid level prediction model to obtain the predicted fluid level in the target object.
Preferably, the data processing module includes:
the data cleaning unit is used for cleaning the historical fluid data;
the data integration unit is used for integrating the data of the cleaned historical fluid data;
the data smoothing unit is used for carrying out data smoothing processing on the integrated historical fluid data;
and the data reduction processing unit is used for carrying out data reduction processing on the historical fluid data after the smoothing processing.
Preferably, the apparatus further comprises:
the second liquid level judging module is used for judging the liquid level of the fluid in the target object based on a preset liquid level prediction criterion;
the first warning module is used for sending warning information to a user when the predicted fluid level in the target object is lower than a third preset fluid level value or higher than a fourth preset fluid level value.
An electronic device, wherein the electronic device comprises:
a processor and a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of the above.
A computer readable storage medium storing one or more instructions which, when executed by a processor, implement the method of any of the above.
According to the invention, the fluid pressure data acquired by the pressure sensor is analyzed and processed through the trained fluid level identification model, the accurate identification of the fluid level is realized, the equipment requirements of maintaining the target object or modifying the set scheme and the like according to the identification result are simple, the resource consumption is reduced, and the manpower resource expenditure is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic diagram of a liquid level identification method based on a pressure sensor according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a liquid level recognition device based on a pressure sensor according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer readable medium according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus a repetitive description thereof will be omitted.
The features, structures, characteristics or other details described in a particular embodiment do not exclude that may be combined in one or more other embodiments in a suitable manner, without departing from the technical idea of the invention.
In the description of specific embodiments, features, structures, characteristics, or other details described in the present invention are provided to enable one skilled in the art to fully understand the embodiments. However, it is not excluded that one skilled in the art may practice the present invention without one or more of the specific features, structures, characteristics, or other details.
The drawings shown in the figures are merely exemplary and do not necessarily include all of the content and operations/steps nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Referring to fig. 1, a schematic diagram of a liquid level identification method based on a pressure sensor according to an embodiment of the present disclosure includes:
s101: acquiring historical fluid data in a target object, wherein the historical fluid data comprises historical fluid pressure data and historical fluid liquid level data;
s102: preprocessing the historical fluid data, and extracting features of the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features;
s103: constructing an initial fluid level identification model, and training the initial fluid level identification model based on the historical fluid pressure characteristics and the historical fluid level characteristics to obtain a trained fluid level identification model;
in this embodiment, historical fluid data in a target object is collected first, the historical fluid data includes historical fluid pressure data and historical fluid liquid level data, then the historical fluid data is preprocessed to avoid dirty data and unusable data formats, then feature extraction is performed on the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features, model training is performed on a built initial fluid liquid level recognition model through the extracted historical fluid pressure features and the historical fluid liquid level features, model training can be performed by one or more of an artificial neural network algorithm, a regression algorithm, a deep learning algorithm, a decision tree algorithm, a random forest algorithm, a gradient regression algorithm and the like when model training is performed, when model training is performed in a multi-model combination mode, a most suitable model is selected as a final fluid liquid level recognition model in a weight division mode to avoid an overfitting phenomenon, and recognition accuracy of the model is effectively improved.
S104: collecting fluid pressure data in a target object in real time by using a pressure sensor;
s105: preprocessing the fluid pressure data, and extracting the characteristics of the preprocessed fluid pressure data to obtain fluid pressure characteristics;
s106: inputting the fluid pressure characteristics into a trained fluid level identification model to obtain the fluid level in the target object.
In the embodiment, the pressure sensor is utilized to collect the fluid pressure data in the target object in real time, the pressure sensor is utilized to collect the data, the data collection process is simplified, then the fluid pressure data is preprocessed, the preprocessed fluid pressure data is subjected to feature extraction to obtain fluid pressure features, finally the fluid pressure features are input into the trained fluid level recognition model to obtain the fluid level in the target object, and recognition of the fluid level is realized.
Further, the method further comprises:
judging the fluid level in the target object based on a preset liquid level identification criterion;
and when the fluid level in the target object is lower than a first preset fluid level value or higher than a second preset fluid level value, sending a warning message to a user.
In this embodiment, for example, the target is an oil immersed transformer, fluid pressure data generated by oil in the oil immersed transformer is collected by installing a pressure sensor in the oil immersed transformer, then the collected data is transmitted to a fluid level identification model for analysis and processing of the fluid pressure data, and finally the liquid level of the oil in the oil immersed transformer is obtained.
Further, the historical fluid data further includes trend data of historical fluid pressure data over time, the method further comprising:
preprocessing trend data of the historical fluid pressure data along with time, and extracting features of the preprocessed trend data of the historical fluid pressure data along with time to obtain time features of the historical fluid pressure;
and constructing an initial fluid level prediction model, and training the initial fluid level prediction model based on the historical fluid pressure characteristic, the historical fluid pressure time characteristic and the historical fluid level characteristic to obtain a trained fluid level prediction model.
In this embodiment, the historical fluid data in the target object collected before further includes trend data of historical fluid pressure data changing along with time, then preprocessing the trend data of the historical fluid pressure data changing along with time, avoiding dirty data and unusable data formats, then extracting features of the trend data of the preprocessed historical fluid pressure data changing along with time to obtain historical fluid pressure time features, performing model training on the constructed initial fluid level prediction model through the extracted historical fluid pressure time features, and performing model training by one or more of an artificial neural network algorithm, a regression algorithm, a deep learning algorithm, a decision tree algorithm, a random forest algorithm, a gradient regression algorithm and the like when performing model training, selecting the most suitable model as a final fluid level prediction model in a weight division mode when performing model training in a multi-model combination mode, avoiding a overfitting phenomenon, and effectively improving the model prediction effect.
Further, the method further comprises:
the method comprises the steps of collecting fluid pressure data in a target object in real time by using a pressure sensor, and recording trend data of the fluid pressure data in the target object along with time change;
respectively extracting characteristics of the fluid pressure data and trend data of the fluid pressure data changing along with time to obtain the fluid pressure characteristics and fluid pressure time characteristics;
and inputting the fluid pressure characteristic and the fluid pressure time characteristic into a trained fluid liquid level prediction model to obtain a predicted fluid liquid level in the target object.
Further, the method further comprises:
judging the fluid level in the target object based on a preset liquid level prediction criterion;
and when the predicted fluid level in the target object is lower than a third preset fluid level value or higher than a fourth preset fluid level value, sending a warning message to a user.
In this embodiment, for example, the target is a river channel, the pressure sensor is installed under the river channel to collect fluid pressure data generated by water in the river channel, then the collected data is transmitted to the fluid level prediction model to perform analysis processing on the fluid pressure data, finally, the level of water in the river channel is obtained, as the river channel encounters a situation that flood or drought occurs due to heavy rain, therefore, the level threshold of water can be set according to a specific scene, the level of water in the river channel is assumed to be an abnormal condition when being higher than 350cm or lower than 30cm, the predicted future water level time is set to be 10 hours, when the collected fluid pressure data is input to the fluid level prediction model, the predicted water level value is 400cm, the water level in the river channel is indicated to be rapidly increased in a future period, at this moment, the display device of the server can display the abnormal mark of the water level of the river channel, and send warning information to the background, so that the relevant departments are reminded to issue emergency schemes or the effect of realizing emergency avoidance of navigation personnel in advance is realized, and larger accidents and losses are avoided.
Preferably, in order to further improve the recognition accuracy of the fluid level recognition model and the fluid level prediction model, other influence factor data such as fluid flow rate data, short-term change frequency of the fluid and the like can be added when the corresponding models are trained, and the corresponding model input and output parameter adjustment is carried out according to specific use situations according to specific requirements so as to achieve the effect of better recognizing the fluid level and predicting the fluid level.
Further, the preprocessing the historical fluid data includes:
performing data cleaning on the historical fluid data;
carrying out data integration on the cleaned historical fluid data;
performing data smoothing processing on the integrated historical fluid data;
and performing data reduction processing on the smoothed historical fluid data.
In this embodiment, the data is subjected to data cleaning, data integration, data smoothing and data reduction, so that format standardization of the data, removal of abnormal data, correction of data errors, repeated removal of data and the like are achieved, and the quality of the data is improved, so that the quality and the speed of subsequent data feature extraction are improved, and the model training effect is improved.
In this embodiment, the fluid level identification model and the fluid level prediction model are not limited to be used for river channel water level survey and anomaly detection of an oil immersed transformer, but can also be used for other devices with fluid to realize fault detection or prediction of the devices, and the like.
According to the invention, the fluid pressure data acquired by the pressure sensor is analyzed and processed through the trained fluid level identification model, the accurate identification of the fluid level is realized, the equipment requirements of maintaining the target object or modifying the set scheme and the like according to the identification result are simple, the resource consumption is reduced, and the manpower resource expenditure is reduced.
Fig. 2 is a schematic structural diagram of a liquid level recognition device based on a pressure sensor according to an embodiment of the present disclosure, including:
a data acquisition module 201, configured to acquire historical fluid data in the target object, where the historical fluid data includes historical fluid pressure data, historical fluid pressure time data, and historical fluid level data;
the data processing module 202 is configured to perform preprocessing on the historical fluid data, and perform feature extraction on the preprocessed historical fluid data to obtain a historical fluid pressure feature and a historical fluid level feature;
the model training module 203 is configured to construct an initial fluid level identification model, train the initial fluid level identification model based on the historical fluid pressure characteristic and the historical fluid level characteristic, and obtain a trained fluid level identification model;
a data acquisition module 204, configured to acquire fluid pressure data in the target object in real time using the pressure sensor;
the feature extraction module 205 is configured to perform preprocessing on the fluid pressure data, and perform feature extraction on the preprocessed fluid pressure data to obtain fluid pressure features;
the fluid level determination module 206 is configured to input the fluid pressure characteristic into a trained fluid level recognition model to obtain a fluid level in the target object.
Further, the apparatus further comprises:
the first liquid level judging module is used for judging the liquid level of the fluid in the target object based on a preset liquid level identification criterion;
the first warning module is used for sending warning information to a user when the fluid level in the target object is lower than a first preset fluid level value or higher than a second preset fluid level value.
Further, the historical fluid data further includes trend data of historical fluid pressure data over time, the method further comprising:
the historical fluid pressure time feature extraction module is used for preprocessing trend data of the historical fluid pressure data along with time, and extracting features of the preprocessed trend data of the historical fluid pressure data along with time to obtain historical fluid pressure time features;
the fluid level prediction model construction module is used for constructing an initial fluid level prediction model, and training the initial fluid level prediction model based on the historical fluid pressure characteristics, the historical fluid pressure time characteristics and the historical fluid level characteristics to obtain a trained fluid level prediction model.
Further, the apparatus further comprises:
the data recording module is used for acquiring the fluid pressure data in the target object in real time by utilizing the pressure sensor and recording trend data of the change of the fluid pressure data in the target object along with time;
the characteristic acquisition module is used for respectively carrying out characteristic extraction on the fluid pressure data and trend data of the fluid pressure data changing along with time to obtain the fluid pressure characteristic and the fluid pressure time characteristic;
and the predicted fluid level module is used for inputting the fluid pressure characteristic and the fluid pressure time characteristic into a trained fluid level prediction model to obtain the predicted fluid level in the target object.
Further, the data processing module includes:
the data cleaning unit is used for cleaning the historical fluid data;
the data integration unit is used for integrating the data of the cleaned historical fluid data;
the data smoothing unit is used for carrying out data smoothing processing on the integrated historical fluid data;
and the data reduction processing unit is used for carrying out data reduction processing on the historical fluid data after the smoothing processing.
Further, the apparatus further comprises:
the second liquid level judging module is used for judging the liquid level of the fluid in the target object based on a preset liquid level prediction criterion;
the first warning module is used for sending warning information to a user when the predicted fluid level in the target object is lower than a third preset fluid level value or higher than a fourth preset fluid level value.
Based on the same inventive concept, the embodiments of the present specification also provide an electronic device.
The following describes an embodiment of an electronic device according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to the present invention. Details described in relation to the embodiments of the electronic device of the present invention should be considered as additions to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described method or apparatus embodiments.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present disclosure is provided. An electronic device 300 according to this embodiment of the present invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 3, the electronic device 300 is embodied in the form of a general purpose computing device. Components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the different device components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 310 such that the processing unit 310 performs the steps according to various exemplary embodiments of the invention described in the above processing method section of the present specification. For example, the processing unit 310 may perform the steps shown in fig. 1.
The memory unit 320 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 3201 and/or cache memory 3202, and may further include Read Only Memory (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: operating devices, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 330 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 300, and/or any device (e.g., router, modem, etc.) that enables the electronic device 300 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 350. Also, electronic device 300 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 via the bus 330. It should be appreciated that although not shown in fig. 3, other hardware and/or software modules may be used in connection with electronic device 300, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, data backup storage devices, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned method according to the present invention. The computer program, when executed by a data processing device, enables the computer readable medium to carry out the above-described method of the present invention, namely: such as the method shown in fig. 1.
Referring to fig. 4, a schematic diagram of a computer readable medium according to an embodiment of the present disclosure is provided.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an apparatus, device, or means for electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in accordance with embodiments of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (9)
1. A method for identifying liquid level based on a pressure sensor, comprising:
acquiring historical fluid data in a target object, wherein the historical fluid data comprises historical fluid pressure data and historical fluid liquid level data;
preprocessing the historical fluid data, and extracting features of the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features;
constructing an initial fluid level identification model, and training the initial fluid level identification model based on the historical fluid pressure characteristics and the historical fluid level characteristics to obtain a trained fluid level identification model;
collecting fluid pressure data in a target object in real time by using a pressure sensor;
preprocessing the fluid pressure data, and extracting the characteristics of the preprocessed fluid pressure data to obtain fluid pressure characteristics;
inputting the fluid pressure characteristics into a trained fluid level identification model to obtain the fluid level in the target object.
2. A method of pressure sensor based level identification as claimed in claim 1, further comprising:
judging the fluid level in the target object based on a preset liquid level identification criterion;
and when the fluid level in the target object is lower than a first preset fluid level value or higher than a second preset fluid level value, sending a warning message to a user.
3. The pressure sensor-based fluid level identification method of claim 1, wherein the historical fluid data further comprises trend data of historical fluid pressure data over time, the method further comprising:
preprocessing trend data of the historical fluid pressure data along with time, and extracting features of the preprocessed trend data of the historical fluid pressure data along with time to obtain time features of the historical fluid pressure;
and constructing an initial fluid level prediction model, and training the initial fluid level prediction model based on the historical fluid pressure characteristic, the historical fluid pressure time characteristic and the historical fluid level characteristic to obtain a trained fluid level prediction model.
4. A method of pressure sensor based level identification as claimed in claim 3, wherein the method further comprises:
the method comprises the steps of collecting fluid pressure data in a target object in real time by using a pressure sensor, and recording trend data of the fluid pressure data in the target object along with time change;
respectively extracting characteristics of the fluid pressure data and trend data of the fluid pressure data changing along with time to obtain the fluid pressure characteristics and fluid pressure time characteristics;
and inputting the fluid pressure characteristic and the fluid pressure time characteristic into a trained fluid liquid level prediction model to obtain a predicted fluid liquid level in the target object.
5. A method of pressure sensor based level identification as claimed in claim 1, wherein said preprocessing of said historical fluid data comprises:
performing data cleaning on the historical fluid data;
carrying out data integration on the cleaned historical fluid data;
performing data smoothing processing on the integrated historical fluid data;
and performing data reduction processing on the smoothed historical fluid data.
6. The pressure sensor-based fluid level identification method of claim 4, further comprising:
judging the fluid level in the target object based on a preset liquid level prediction criterion;
and when the predicted fluid level in the target object is lower than a third preset fluid level value or higher than a fourth preset fluid level value, sending a warning message to a user.
7. A pressure sensor-based liquid level identification device, comprising:
the data acquisition module is used for acquiring historical fluid data in the target object, wherein the historical fluid data comprises historical fluid pressure data and historical fluid liquid level data;
the data processing module is used for preprocessing the historical fluid data and extracting features of the preprocessed historical fluid data to obtain historical fluid pressure features and historical fluid liquid level features;
the model training module is used for constructing an initial fluid level identification model, training the initial fluid level identification model based on the historical fluid pressure characteristics and the historical fluid level characteristics, and obtaining a trained fluid level identification model;
the data acquisition module is used for acquiring fluid pressure data in the target object in real time by utilizing the pressure sensor;
the characteristic extraction module is used for preprocessing the fluid pressure data, and extracting the characteristics of the preprocessed fluid pressure data to obtain fluid pressure characteristics;
and the fluid level determining module is used for inputting the fluid pressure characteristics into a trained fluid level identification model to obtain the fluid level in the target object.
8. An electronic device, wherein the electronic device comprises:
a processor and a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1-6.
9. A computer readable storage medium storing one or more instructions which, when executed by a processor, implement the method of any one of claims 1-6.
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