CN112802086A - Density estimation method, density estimation device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a density estimation method, a device, an electronic device and a storage medium, the method is used for estimating the density of a target liquid, the target liquid is contained in a container, and the method comprises the following steps: acquiring 2D image information of the target liquid; acquiring 2D image information and component labeling data of a first group of liquid samples; training to obtain a liquid component model by using a deep learning mode according to the 2D image information and the component marking data of the first group of liquid samples; determining the composition of the target liquid according to the 2D image information of the target liquid and the liquid composition model; and acquiring the density of the target liquid according to the composition of the target liquid. Because the liquid is a special substance, the taking and the placing are not convenient, and the liquid is easy to be polluted by impurities when contacting with instrument equipment, the method matches corresponding components through a computer vision technology, and the density is obtained according to component evaluation; the method has the advantages of no need of contacting liquid, high detection efficiency and high accuracy.
Description
Technical Field
The present application relates to the field of computer vision technology and industrial detection technology, and in particular, to a density estimation method, apparatus, electronic device, and computer-readable storage medium.
Background
Computer vision technology is used as a comprehensive subject, is applied to various fields such as military, agriculture and life at present, and particularly brings great benefits to the production field, so that more and more attention is paid to the technology, and related technologies are improved and developed increasingly.
For example, vision technology has many applications for online inspection of various products in industrial processes. However, these applications relate to solid-state devices, and are limited to detecting the quality of a liquid or detecting impurities contained in a liquid in the aspect of detecting a liquid. Methods have not been developed in estimating the density of a liquid, so as to obtain the quality of the liquid.
The liquid is a special substance, and it needs to have the container to bear to get to put, and manual detection needs to be with the help of instrument and equipment, is unfavorable for guaranteeing the purity of liquid like this, and some special circumstances, it is difficult to know its density when the liquid exists to be inconvenient to operate the detection. The present invention is intended to solve the above-mentioned problems and to obtain accurate density information of a liquid with high efficiency.
Disclosure of Invention
The present application aims to provide a density estimation method, a density estimation device, an electronic apparatus, and a storage medium, which can efficiently obtain accurate density information of a liquid.
The purpose of the application is realized by adopting the following technical scheme:
in a first aspect, the present application provides a density estimation method for estimating a density of a target liquid contained in a container, the method comprising: acquiring 2D image information of the target liquid; acquiring 2D image information and component labeling data of a first group of liquid samples; training to obtain a liquid component model by using a deep learning mode according to the 2D image information and the component marking data of the first group of liquid samples; determining the composition of the target liquid according to the 2D image information of the target liquid and the liquid composition model; and acquiring the density of the target liquid according to the composition of the target liquid. The technical scheme has the advantages that the liquid is a special substance, so that the liquid is not convenient to take and place, is easy to be contaminated when being contacted with instrument equipment, and the 2D image of the target liquid is obtained through a computer vision technology, the components of the liquid are identified, the corresponding components are obtained in a target component library in a matching manner, and then the density is obtained through evaluation according to the obtained components; the method does not need to contact liquid, has high detection efficiency and high accuracy; when the liquid component model is obtained, the 2D image information and the component labeling data of a large number of liquid samples are obtained, a deep learning model is trained to obtain the liquid component model, and then the components of the target liquid are determined according to the 2D image information and the liquid component model of the target liquid.
In some optional embodiments, the obtaining the density of the target liquid according to the composition of the target liquid includes: when the component of the target liquid is a single component, acquiring the density corresponding to the component as the density of the target liquid; when the composition of the target liquid is not a single composition, the density of the target liquid is obtained from the composition and liquid density model. The technical scheme has the advantages that when the single component of the target liquid is adopted, the density corresponding to the component is directly obtained as the density of the target liquid; when the target liquid component is not single, a multi-sensor fusion technology can be adopted, for example, a spectrum confocal sensor is adopted to detect the internal structure of the substance contained in the liquid, meanwhile, a deep learning model is combined to judge the components of the internal structure, and the density corresponding to each component is weighted according to the specific gravity of the component in the liquid, so that a converted uniform density value is obtained.
In some optional embodiments, the method of obtaining the liquid density model comprises: acquiring component and density labeling data of a second group of liquid samples; and training to obtain the liquid density model by using a deep learning mode according to the component and density labeling data of the second group of liquid samples. The technical scheme has the advantages that the components and the density marking data of a large number of liquid samples are obtained, and then the liquid density model is obtained in a deep learning mode, so that convenience is provided for subsequent density estimation, and the working time is saved.
In some optional embodiments, the method further comprises: acquiring image information of the container; acquiring the volume of the target liquid according to the image information of the container; and acquiring the mass of the target liquid according to the density and the volume of the target liquid. The technical scheme has the beneficial effects that the volume of the target liquid is obtained through the image information, so that the mass of the target liquid is calculated according to the density and the volume.
In some alternative embodiments, the image information of the container is obtained using CT techniques. The technical scheme has the beneficial effects that the image information of the container is obtained through the CT technology, namely, the tomographic image data of the container is obtained according to CT image tomographic scanning parameters, so that the volume of the target liquid is obtained.
In a second aspect, the present application provides a density estimation apparatus for estimating a density of a target liquid contained in a container, the apparatus comprising: the 2D acquisition module is used for acquiring 2D image information of the target liquid; a composition acquisition module comprising: a first specimen acquisition unit for acquiring 2D image information and component labeling data of a first group of liquid specimens; the first model training unit is used for training a liquid component model by using a deep learning mode according to the 2D image information and the component marking data of the first group of liquid samples; a component determination unit for determining a component of the target liquid based on the 2D image information of the target liquid and the liquid component model; and the density acquisition module is used for acquiring the density of the target liquid according to the components of the target liquid.
In some optional embodiments, the density acquisition module comprises: a first density acquisition unit configured to acquire, when a component of the target liquid is a single component, a density corresponding to the component as a density of the target liquid; a second density obtaining unit for obtaining a density of the target liquid based on the composition and a liquid density model when the composition of the target liquid is not a single composition.
In some optional embodiments, the apparatus further comprises a density model obtaining module, the density model obtaining module comprising: the second sample acquisition unit is used for acquiring the component and density labeling data of a second group of liquid samples; and the second model training unit is used for training to obtain the liquid density model by using a deep learning mode according to the components and density marking data of the second group of liquid samples.
In some optional embodiments, the apparatus further comprises: the container image acquisition module is used for acquiring the image information of the container; the volume acquisition module is used for acquiring the volume of the target liquid according to the image information of the container; and the mass acquisition module is used for acquiring the mass of the target liquid according to the density and the volume of the target liquid.
In some alternative embodiments, the image information of the container is obtained using CT techniques.
In a third aspect, the present application provides an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of any of the above methods when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of any of the methods described above.
Drawings
The present application is further described below with reference to the drawings and examples.
Fig. 1 is a schematic flow chart of a density estimation method provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a process for determining a target fluid component according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of obtaining a target liquid density according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a method for obtaining a liquid density model using deep learning according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart for obtaining a target liquid quality according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a density estimation apparatus according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of a component obtaining module provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of a density acquisition module according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of a density estimation apparatus according to an embodiment of the present application;
FIG. 10 is a schematic structural diagram of a density model obtaining module according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of a density estimation apparatus according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 13 is a schematic structural diagram of a program product for implementing a density estimation method according to an embodiment of the present application.
Detailed Description
The present application is further described with reference to the accompanying drawings and the detailed description, and it should be noted that, in the present application, the embodiments or technical features described below may be arbitrarily combined to form a new embodiment without conflict.
Referring to fig. 1, the present application provides a density estimation method for estimating a density of a target liquid contained in a container, the method including steps S101 to S103.
The target liquid is not limited to a liquid, and may be a single-component liquid or a liquid containing a plurality of components.
Step S101: acquiring 2D image information of the target liquid.
Step S102: and acquiring the components of the target liquid according to the 2D image information of the target liquid.
Step S103: and acquiring the density of the target liquid according to the composition of the target liquid.
Referring to fig. 2, in a specific implementation, step S102 may include steps S201 to S203.
Step S201: 2D image information and component labeling data for a first set of liquid specimens are acquired.
Step S202: and training to obtain a liquid component model by using a deep learning mode according to the 2D image information and the component labeling data of the first group of liquid samples.
Step S203: and determining the composition of the target liquid according to the 2D image information of the target liquid and the liquid composition model.
The method comprises the following steps of firstly acquiring 2D image information and component labeling data of a large number of liquid samples, training a deep learning model, and obtaining a liquid component model; and determining the components of the target liquid according to the 2D image information of the target liquid and the liquid component model.
In a specific implementation, referring to fig. 3, step S103 may include steps S301 to S302.
Step S301: when the component of the target liquid is a single component, the density corresponding to the component is acquired as the density of the target liquid.
Step S302: and when the component of the target liquid is not a single component, acquiring the density of the target liquid according to the component and the liquid density model.
In the above steps of this embodiment of the present application, when a single component of the target liquid is used, directly obtaining a density corresponding to the component as the density of the target liquid; when the target liquid component is not single, a multi-sensor fusion technology can be adopted, for example, a spectrum confocal sensor is adopted to detect the internal structure of the substance contained in the liquid, meanwhile, a deep learning model is combined to judge the components of the internal structure, and the density corresponding to each component is weighted according to the specific gravity of the component in the liquid, so that a converted uniform density value is obtained.
Referring to fig. 4, the method of obtaining the liquid density model may include steps S401 to S402.
Step S401: and acquiring component and density labeling data of the second group of liquid samples.
Step S402: and training to obtain the liquid density model by using a deep learning mode according to the component and density labeling data of the second group of liquid samples.
According to the steps of the embodiment of the application, the component and density labeling data of a large number of liquid samples are obtained, and then the liquid density model is obtained in a deep learning mode, so that convenience is provided for subsequent density estimation, and the working time is saved.
Referring to FIG. 5, in a specific implementation, the method further includes steps S501-S502.
Step S501: image information of the container is acquired.
Step S502: and acquiring the volume of the target liquid according to the image information of the container.
Step S503: and acquiring the mass of the target liquid according to the density and the volume of the target liquid.
In the above steps of the embodiment of the present application, the volume of the target liquid is obtained through the image information, so that the mass of the target liquid is calculated according to the density and the volume.
In a specific implementation, the image information of the container is obtained using CT techniques. And acquiring image information of the container by CT technology, namely acquiring tomographic image data of the container according to CT image tomographic scanning parameters so as to obtain the volume of the target liquid.
Because the liquid is a special substance, the liquid is not convenient to take and place, and is easy to be contaminated when being contacted with instrument equipment, the embodiment of the application obtains a 2D image of target liquid by a computer vision technology, trains a deep learning model to obtain a liquid component model by adopting a big data analysis mode, identifies the components of the liquid and the specific gravity of the liquid by using the liquid component model, and evaluates to obtain the density by combining the specific gravity according to the obtained components; the method has the advantages of no need of contacting liquid, high detection efficiency and high accuracy.
Referring to fig. 6, the present application further provides a density estimation apparatus, and a specific implementation manner of the density estimation apparatus is consistent with the implementation manner and the achieved technical effect described in the foregoing method embodiment, and a part of the details are not repeated.
The device comprises: a 2D obtaining module 101, configured to obtain 2D image information of the target liquid; a component obtaining module 102, configured to obtain a component of the target liquid according to the 2D image information of the target liquid; a density obtaining module 103, configured to obtain a density of the target liquid according to the composition of the target liquid.
Referring to fig. 7, in a specific implementation, the component obtaining module 102 may include: a first specimen acquisition unit 201 for acquiring 2D image information and component labeling data of a first group of liquid specimens; the first model training unit 202 is configured to train to obtain a liquid component model by using a deep learning manner according to the 2D image information and the component labeling data of the first group of liquid samples; a component determining unit 203 for determining a component of the target liquid according to the 2D image information of the target liquid and the liquid component model.
Referring to fig. 8, in a specific implementation, the density acquisition module 103 may include: a first density acquisition unit 301 configured to acquire, when a component of the target liquid is a single component, a density corresponding to the component as a density of the target liquid; a second density obtaining unit 302 configured to obtain, when the composition of the target liquid is not a single composition, the density of the target liquid based on the composition and the liquid density model.
Referring to fig. 9 and 10, in a specific implementation, the apparatus may further include a density model obtaining module 104, where the density model obtaining module 104 includes: a second specimen acquisition unit 401 for acquiring component and density labeling data of a second group of liquid specimens; and a second model training unit 402, configured to train to obtain the liquid density model by using a deep learning manner according to the components and density labeling data of the second group of liquid samples.
Referring to fig. 11, in a specific implementation, the apparatus may further include: a container image obtaining module 105, configured to obtain image information of the container; a volume obtaining module 106, configured to obtain a volume of the target liquid according to the image information of the container; and the mass obtaining module 107 is configured to obtain the mass of the target liquid according to the density and the volume of the target liquid.
In one implementation, the image information of the container is obtained using CT techniques.
Referring to fig. 12, an embodiment of the present application further provides an electronic device 200, where the electronic device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.
The memory 210 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)211 and/or cache memory 212, and may further include Read Only Memory (ROM) 213.
The memory 210 further stores a computer program, and the computer program can be executed by the processor 220, so that the processor 220 executes the steps of the density estimation method in the embodiment of the present application, and a specific implementation manner of the method is consistent with the implementation manner and the achieved technical effect described in the embodiments of the method, and a part of the contents are not described again.
Accordingly, processor 220 may execute the computer programs described above, as well as may execute programs/utilities 214.
The electronic device 200 may also communicate with one or more external devices 240, such as a keyboard, pointing device, Bluetooth device, etc., and may also communicate with one or more devices capable of interacting with the electronic device 200, and/or with any devices (e.g., routers, modems, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium is used for storing a computer program, and when the computer program is executed, the steps of the density estimation method in the embodiment of the present application are implemented, and a specific implementation manner of the method is consistent with the implementation manner and the achieved technical effect described in the embodiment of the foregoing method, and some contents are not described again.
Fig. 13 shows a program product 300 for implementing the method provided by the embodiment, which may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not so limited, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Program product 300 may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 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 for aspects 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 and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).
The foregoing description and drawings are only for purposes of illustrating the preferred embodiments of the present application and are not intended to limit the present application, which is, therefore, to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present application.
Claims (12)
1. A density estimation method for estimating a density of a target liquid contained in a container, the method comprising:
acquiring 2D image information of the target liquid;
acquiring 2D image information and component labeling data of a first group of liquid samples;
training to obtain a liquid component model by using a deep learning mode according to the 2D image information and the component marking data of the first group of liquid samples;
determining the composition of the target liquid according to the 2D image information of the target liquid and the liquid composition model;
and acquiring the density of the target liquid according to the composition of the target liquid.
2. The density estimation method according to claim 1, wherein the obtaining the density of the target liquid from the composition of the target liquid includes:
when the component of the target liquid is a single component, acquiring the density corresponding to the component as the density of the target liquid;
when the composition of the target liquid is not a single composition, the density of the target liquid is obtained from the composition and liquid density model.
3. The density estimation method according to claim 2, wherein the method of obtaining the liquid density model includes:
acquiring component and density labeling data of a second group of liquid samples;
and training to obtain the liquid density model by using a deep learning mode according to the component and density labeling data of the second group of liquid samples.
4. The density estimation method according to claim 1, characterized in that the method further comprises:
acquiring image information of the container;
acquiring the volume of the target liquid according to the image information of the container;
and acquiring the mass of the target liquid according to the density and the volume of the target liquid.
5. The density estimation method of claim 4, wherein the image information of the container is obtained using a CT technique.
6. A density estimation device for estimating a density of a target liquid contained in a container, the device comprising:
the 2D acquisition module is used for acquiring 2D image information of the target liquid;
a composition acquisition module comprising:
a first specimen acquisition unit for acquiring 2D image information and component labeling data of a first group of liquid specimens;
the first model training unit is used for training a liquid component model by using a deep learning mode according to the 2D image information and the component marking data of the first group of liquid samples;
a component determination unit for determining a component of the target liquid based on the 2D image information of the target liquid and the liquid component model;
and the density acquisition module is used for acquiring the density of the target liquid according to the components of the target liquid.
7. The density estimation apparatus according to claim 6, wherein the density acquisition module includes:
a first density acquisition unit configured to acquire, when a component of the target liquid is a single component, a density corresponding to the component as a density of the target liquid.
A second density obtaining unit for obtaining a density of the target liquid based on the composition and a liquid density model when the composition of the target liquid is not a single composition.
8. The density estimation apparatus according to claim 7, further comprising a density model acquisition module including:
the second sample acquisition unit is used for acquiring the component and density labeling data of a second group of liquid samples;
and the second model training unit is used for training to obtain the liquid density model by using a deep learning mode according to the components and density marking data of the second group of liquid samples.
9. The density estimation apparatus according to claim 6, characterized in that the apparatus further comprises:
the container image acquisition module is used for acquiring the image information of the container;
the volume acquisition module is used for acquiring the volume of the target liquid according to the image information of the container;
and the mass acquisition module is used for acquiring the mass of the target liquid according to the density and the volume of the target liquid.
10. The density estimation apparatus according to claim 9, wherein the image information of the container is obtained using a CT technique.
11. An electronic device, characterized in that the electronic device comprises a memory storing a computer program and a processor implementing the steps of the method according to any of claims 1-5 when the processor executes the computer program.
12. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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| CN101900589A (en) * | 2010-04-29 | 2010-12-01 | 中国石油大学(华东) | Air-entrainment liquid flow measuring method based on mass flowmeter |
| US20190377979A1 (en) * | 2017-08-30 | 2019-12-12 | Tencent Technology (Shenzhen) Company Limited | Image description generation method, model training method, device and storage medium |
| CN110688928A (en) * | 2019-09-20 | 2020-01-14 | 北京海益同展信息科技有限公司 | Model training method and device, electronic equipment and computer readable storage medium |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101900589A (en) * | 2010-04-29 | 2010-12-01 | 中国石油大学(华东) | Air-entrainment liquid flow measuring method based on mass flowmeter |
| US20190377979A1 (en) * | 2017-08-30 | 2019-12-12 | Tencent Technology (Shenzhen) Company Limited | Image description generation method, model training method, device and storage medium |
| CN110688928A (en) * | 2019-09-20 | 2020-01-14 | 北京海益同展信息科技有限公司 | Model training method and device, electronic equipment and computer readable storage medium |
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