CN114859309A - Animal quality measuring equipment and method based on millimeter wave radar - Google Patents
Animal quality measuring equipment and method based on millimeter wave radar Download PDFInfo
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- CN114859309A CN114859309A CN202110166249.XA CN202110166249A CN114859309A CN 114859309 A CN114859309 A CN 114859309A CN 202110166249 A CN202110166249 A CN 202110166249A CN 114859309 A CN114859309 A CN 114859309A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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Abstract
The invention discloses an animal quality measuring device and method based on a millimeter wave radar. The method comprises the steps of collecting surface contour data of a distant animal by using a millimeter wave radar sensor; the data are sent to a back-end data processing center after various kinds of preprocessing, the animal weight is calculated according to a neural network algorithm and a mathematical model established by the data processing center, and the animal weight is returned to a front-end display interface or sent to a remote information center to help an animal feeder make a reasonable decision. The invention does not need to contact animals when measuring the weight of the animals, greatly simplifies the procedure of measuring the weight of the animals, has small volume, can flexibly measure different types of animals and provides convenience for human beings.
Description
Technical Field
The invention relates to the technical field of information, in particular to an animal quality measuring device and method based on a millimeter wave radar.
Background
In modern life, especially in large modern farms and zoos, the weight and mass of animals are often important criteria for the assessment of their health status. The weight of the animals can be mastered in real time, and an important reference basis is provided for scientific and standardized breeding.
Existing electronic scales all require the animal to stand directly or indirectly on a weight scale to acquire their mass. This has a lot of inconvenience in practical operation. Such as: the animals are not actively matched with measuring personnel to measure, or some animals are too violent and dangerous, and the like. On the other hand, when the animal is grabbed for weighing, the animal can move ceaselessly, and the measurement accuracy is influenced.
Disclosure of Invention
In order to solve the above problems, the present invention provides a millimeter wave radar-based animal weighing scale, comprising: the signal transceiver module is used for periodically transmitting microwave radar signals to the side surface of a pre-measured animal body and receiving radar echo signals rebounded by the side surface of the animal; and the data processing module is used for carrying out animal weight calculation according to the internally established mathematical model and the measured radar data to obtain the animal weight.
The purpose of the invention can be realized by the following technical scheme:
an animal weighing scale based on a millimeter wave radar comprises the following steps:
firstly, data acquisition is carried out on different types of animals by using a millimeter wave radar, and meanwhile, the weight of the animals is collected by using an electronic scale.
And secondly, establishing mathematical models corresponding to different types of animal side areas, distances from the animals to the millimeter wave radar and animal qualities through machine learning, and storing the mathematical models in a database.
And step three, acquiring the side area of the animal to be measured by using the millimeter wave radar in the actual use process, and transmitting the data back to the back-end data processing center.
And fourthly, the data processing center analyzes and calculates the transmitted data according to a mathematical model and a neural network algorithm.
And fifthly, the data processing center returns the data result to the front-end display interface.
Furthermore, the data acquisition module is integrated on the equipment and comprises a millimeter wave radar sensor for monitoring the animal object, wherein the working frequency of the millimeter wave radar sensor is 30-300GHz and comprises a dot-frequency millimeter wave phase-controlled oscillator, a directional coupler, a circulator, a balanced mixer, a radar receiving and transmitting antenna and a signal processor, and the acquired data can be transmitted to the cloud information processing center through a 4G or 5G network transmission module. The data processing module may also be integrated in the device for directly displaying data. Further, the data processing unit includes: the system comprises a first preprocessing subunit, a second preprocessing subunit, a transformation subunit and a mathematical analysis subunit; the first preprocessing subunit is connected with the signal transceiving module and is used for multiplying a microwave radar signal transmitted in a sampling period by a received radar echo signal and performing low-pass filtering processing on a multiplied result; the second preprocessing subunit is connected with the first preprocessing subunit and used for converting the analog signals subjected to the low-pass filtering processing into digital signals; the conversion subunit is connected with the second preprocessing subunit and is used for converting the digital signals into range-doppler images of the animals; and the mathematical analysis subunit is connected with the transformation subunit and is used for performing mathematical analysis on the processed data so as to convert the processed data into the corresponding animal weight.
Wherein, the specific steps of the transformation subunit transforming the digital signal into the doppler image of the animal volume are as follows: sequentially carrying out first fast Fourier transform and static background noise filtering processing on the digital signal; then, radar echo signals obtained in a plurality of previous sampling periods adjacent to the current sampling period are respectively processed by the first preprocessing subunit and the second preprocessing subunit in sequence to obtain a plurality of previous digital signals corresponding to the previous sampling periods one by one; respectively and sequentially carrying out first fast Fourier transform and static background noise filtering processing on the front digital signals; and combining the current signals subjected to static background noise filtering and a plurality of previous signals subjected to static background noise filtering in a periodic manner into a signal combination in a matrix form, and performing second fast Fourier transform on the signal combination in the matrix form in the dimension of a sampling period label to obtain the range Doppler image of the animal volume. A Moving Target Indicator (Moving Target Indicator) may be used to perform static background noise filtering processing, where the static background noise refers to an echo signal received by the signal receiving apparatus when no animal exists.
Optionally, the system further comprises a data display module. The data acquisition module, the data analysis module and the data display module are packaged together to form an independent device. The real-time measurement and real-time display of a user can be facilitated.
Has the advantages that: the animal weighing scale based on millimeter wave radar sensing can realize real-time animal weighing detection, is flexible to install and various in form, can be connected with other intelligent management platforms, and enhances usability.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a millimeter wave radar based animal scale of the present invention;
FIG. 2 is an example of the body weight of an animal of the present invention.
Fig. 3 is a simulation block diagram of the system.
Fig. 4 is a block diagram of method steps.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. As shown in fig. 1-4.
An animal weighing scale based on a millimeter wave radar comprises the following steps:
firstly, data acquisition is carried out on different types of animals by using a millimeter wave radar, and meanwhile, the weight of the animals is collected by using an electronic scale.
And secondly, establishing mathematical models corresponding to different types of animal side areas, distances from the animals to the millimeter wave radar and animal qualities through machine learning, and storing the mathematical models in a database.
And step three, acquiring the side area of the animal to be measured by using the millimeter wave radar in the actual use process, and transmitting the data back to the back-end data processing center.
And fourthly, the data processing center analyzes and calculates the transmitted data according to a mathematical model and a neural network algorithm.
And fifthly, the data processing center returns the data result to the front-end display interface.
Furthermore, the data acquisition module is integrated on the equipment and comprises a millimeter wave radar sensor for monitoring the animal object, wherein the working frequency of the millimeter wave radar sensor is 30-300GHz and comprises a dot-frequency millimeter wave phase-controlled oscillator, a directional coupler, a circulator, a balanced mixer, a radar receiving and transmitting antenna and a signal processor, and the acquired data can be transmitted to the cloud information processing center through a 4G or 5G network transmission module. The data processing module may also be integrated in the device for directly displaying data. Further, the data processing unit includes: the system comprises a first preprocessing subunit, a second preprocessing subunit, a transformation subunit and a mathematical analysis subunit; the first preprocessing subunit is connected with the signal transceiving module and is used for multiplying a microwave radar signal transmitted in a sampling period by a received radar echo signal and performing low-pass filtering processing on a multiplied result; the second preprocessing subunit is connected with the first preprocessing subunit and used for converting the analog signals subjected to the low-pass filtering processing into digital signals; the conversion subunit is connected with the second preprocessing subunit and is used for converting the digital signals into range-doppler images of the animals; and the mathematical analysis subunit is connected with the transformation subunit and is used for performing mathematical analysis on the processed data so as to convert the processed data into the corresponding animal weight.
Wherein, the specific steps of the transformation subunit transforming the digital signal into the doppler image of the animal volume are as follows: sequentially carrying out first fast Fourier transform and static background noise filtering processing on the digital signal; then, radar echo signals obtained in a plurality of previous sampling periods adjacent to the current sampling period are respectively processed by the first preprocessing subunit and the second preprocessing subunit in sequence to obtain a plurality of previous digital signals corresponding to the previous sampling periods one by one; respectively and sequentially carrying out first fast Fourier transform and static background noise filtering processing on the front digital signals; and combining the current signals subjected to static background noise filtering and a plurality of previous signals subjected to static background noise filtering in a periodic manner into a signal combination in a matrix form, and performing second fast Fourier transform on the signal combination in the matrix form in the dimension of a sampling period label to obtain the range Doppler image of the animal volume. A Moving Target Indicator (Moving Target Indicator) may be used to perform static background noise filtering processing, where the static background noise refers to an echo signal received by the signal receiving apparatus when no animal exists.
Optionally, the system further comprises a data display module. The data acquisition module, the data analysis module and the data display module are packaged together to form an independent device. The real-time measurement and real-time display of a user can be facilitated.
After the animal weighing scale equipment is powered on, the data analysis module in the equipment and the millimeter wave radar sensor enter a working state, the millimeter wave radar starts to emit point-frequency signals, the point-frequency signals are emitted by the radar to irradiate the body of an animal to be measured, and the return time of the waves irradiating the body of the animal is inconsistent with the return time of the waves irradiating the ground, so that the equipment can identify the volume of the animal. After a signal receiving device on the radar receives the signal, the next step is carried out; the data transmission circuit transmits the data to the cloud information processing center through a 4G/5G network or directly transmits the data to the back-end data processing module.
The data processing module starts signal processing after receiving the data. And after the information is preprocessed, entering a core processing link. And analyzing the volume of the animal and the distance from the animal to the radar according to the echo signals. The mass of the animal is estimated by a distance volume mass model established by a learning algorithm.
Specifically, in the training phase of the quality recognition model: firstly, obtaining range Doppler images from different animals to radar according to a sampling period to calculate a plurality of characteristic quantity combinations for a subsequent quality estimation algorithm, wherein the characteristic quantity combinations include but are not limited to average frequency deviation of microwave radar signals, total energy of radar echo signals and moving speed of animals; secondly, performing dimensionality reduction processing on the acquired signal characteristics by adopting a Principal Component Analysis (Principal Component Analysis) algorithm to obtain a group of fixed characteristic combinations, and pushing the characteristic combination names to a quality estimation module for memorizing. Finally, the collected data set, i.e. the training sample set, is trained using a learning algorithm (e.g. a random forest algorithm) to build an animal quality estimation model. The training sample comprises received echo signals and corresponding animal quality, the characteristic combination quantity of training data needs to be calculated in the training process, an animal quality estimation model is finally established after the model converges, and the animal quality model is stored in the processing module. In this way, in the process of estimating the animal quality, the feature extraction subunit directly performs feature extraction on the range-doppler image of the animal according to the feature combination name; the recognition unit estimates the animal mass using the stored animal mass estimation model based on the combination of the features extracted by the feature extraction subunit.
When the user needs to weigh another kind of animal, the model is retrained according to the process.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A method for measuring body weight of an animal based on a millimeter wave radar is characterized by comprising the following steps:
firstly, data acquisition is carried out on different types of animals by using a millimeter wave radar, and meanwhile, the weight of the animals is collected by using an electronic scale;
establishing mathematical models corresponding to different types of animal side areas, distances from the animals to the millimeter wave radar and animal qualities through machine learning, and storing the mathematical models in a database;
thirdly, acquiring the side area of the animal to be measured by using the millimeter wave radar in the actual use process, and transmitting the data back to the back-end data processing center;
analyzing and calculating the transmitted data by the data processing center according to a mathematical model and a neural network algorithm;
and fifthly, the data processing center returns the data result to the front-end display interface.
2. An intelligent millimeter wave radar animal weight measuring device is characterized in that when the weight of an animal is measured, the animal to be measured does not need to be contacted in a close range, the whole system is divided into two parts, one part is a millimeter wave radar serving as a data acquisition end, the working frequency of a sensor of the radar is 30-300GHz and comprises a point-frequency millimeter wave phase-controlled oscillator, a directional coupler, a circulator, a balance mixer, a radar receiving and transmitting antenna and a signal processor, the other part is a rear-end data processing center, the unit can be established at any position and can be established in a cloud-end data processing center, the unit and the millimeter wave radar can be integrated into the same small device, the use is convenient and rapid, and data can be transmitted back to a needed place to provide decision reference for an animal feeder.
3. The intelligent millimeter wave radar animal weight measuring device of claim 2, wherein the back-end data processing center comprises a machine learning algorithm, the first part comprises a feature recognition and SVM classification algorithm for recognizing the angle of the animal photographed, the second part comprises a regression prediction algorithm for estimating the weight of the animal photographed at different angles. The mathematical model uses a machine learning algorithm, the machine learning algorithm is trained through big data, the five processes of screening, preprocessing, feature extraction, model application and result return are carried out on data collected by a radar, and the accuracy of the mathematical model can be guaranteed through the use of the big data.
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| CN202110166249.XA CN114859309A (en) | 2021-02-04 | 2021-02-04 | Animal quality measuring equipment and method based on millimeter wave radar |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116687386A (en) * | 2023-08-07 | 2023-09-05 | 青岛市畜牧工作站(青岛市畜牧兽医研究所) | Radar detection system and method for comprehensive calibration of cattle body shape data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116687386A (en) * | 2023-08-07 | 2023-09-05 | 青岛市畜牧工作站(青岛市畜牧兽医研究所) | Radar detection system and method for comprehensive calibration of cattle body shape data |
| CN116687386B (en) * | 2023-08-07 | 2023-10-31 | 青岛市畜牧工作站(青岛市畜牧兽医研究所) | Radar detection system and method for comprehensive calibration of cattle body shape data |
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