CN111652717A - Animal husbandry credit risk assessment method and device - Google Patents
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Abstract
The invention discloses a credit risk assessment method and a credit risk assessment device for animal husbandry, wherein the method comprises the following steps: acquiring monitoring data of livestock and surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data; inputting the monitoring data into a trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data; inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information. The invention can carry out accurate risk assessment on the user in real time, and is beneficial to the bank to implement risk control.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a credit risk assessment method and device for animal husbandry.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the increase of the added value of the market of the products of the scattered organic animal husbandry, the vigorous support of animal husbandry has become an important way for helping the economic lag areas to be out of poverty and become rich. In recent years, in order to promote income increase of farmers, the banking industry has been forced to implement popular financial services. The premise of realizing the general finance is to accurately evaluate the credit risk level of the user, thereby being beneficial to realizing the risk management and control.
However, currently, no good credit risk assessment method exists for risk management and control of animal husbandry. Banks are usually evaluated by third-party experts, and risk management and control are realized according to credit risk evaluation structures of the third-party experts. However, since such methods are manually evaluated by experts, they must rely on the experience and ability of the experts, and different experts have different standards, different results may be obtained even if credit risk evaluation is performed on the same user, and thus the risk evaluation result for the user may not be accurate. Meanwhile, the method is low in efficiency due to the manual evaluation of experts, generally only can be used for qualification examination before loan, and the credit risk evaluation of the user cannot be adjusted in real time according to the actual condition, so that the bank cannot implement risk control in real time, and the method is not beneficial to providing general financial services for the user.
Disclosure of Invention
The embodiment of the invention provides a credit risk assessment method for animal husbandry, which is used for solving the technical problem that a bank in the prior art cannot accurately assess credit risk of an animal husbandry user in real time, and comprises the following steps:
acquiring monitoring data of livestock and surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
inputting the monitoring data into a trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data;
inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information.
The embodiment of the invention also provides a credit risk assessment device for animal husbandry, which is used for solving the technical problem that a bank can not accurately assess credit risk of an animal husbandry user in real time in the prior art, and comprises the following components:
the data acquisition module is used for acquiring monitoring data of the livestock and the surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
the livestock state prediction module is used for inputting the monitoring data into the trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data;
the credit risk evaluation module is used for inputting the livestock state prediction result and the credit information of the user into the trained credit risk evaluation model and acquiring the credit risk evaluation grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the animal husbandry credit risk assessment method.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above animal husbandry credit risk assessment method.
In the embodiment of the invention, monitoring data of livestock and surrounding environment thereof are obtained based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data; compared with the technical scheme that a bank cannot carry out credit risk assessment on the animal husbandry user in real time in the prior art, monitoring data of a large number of livestock and the surrounding environment of the livestock can be obtained in real time through the Internet of things, sufficient data basis is provided for credit risk assessment of the bank on the user, the monitoring data are further input into a trained seasonal morbidity and mortality prediction model, and livestock state prediction results are obtained; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data; secondly, inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information. Compared with a third-party expert evaluation mode, the user credit risk evaluation grade obtained based on the machine learning and neural network model is more accurate, the user credit risk evaluation grade can be continuously adjusted according to real-time monitoring data, the bank can implement risk control, and the bank can provide general financial services for the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic flow chart of a method for assessing animal husbandry credit risk according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method of training a seasonal morbidity and mortality prediction model according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an embodiment of the method for assessing animal husbandry credit risk shown in FIG. 1 according to the present invention;
fig. 4 is a schematic structural diagram of an animal husbandry credit risk assessment device according to an embodiment of the present invention;
fig. 5 is a diagram of an embodiment of the animal husbandry credit risk assessment apparatus shown in fig. 4 according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The embodiment of the invention provides a credit risk assessment method for animal husbandry, which is used for solving the technical problem that a bank cannot accurately assess credit risk of an animal husbandry user in real time in the prior art. Fig. 1 is a schematic flow chart of a method for evaluating credit risk of animal husbandry according to an embodiment of the present invention. As shown in fig. 1, the animal husbandry credit risk assessment method according to the present invention may include:
102, acquiring monitoring data of livestock and surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
As can be known from the process shown in fig. 1, in the embodiment of the present invention, monitoring data of livestock and their surroundings is obtained based on the internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data; compared with the technical scheme that a bank cannot carry out credit risk assessment on the animal husbandry user in real time in the prior art, monitoring data of a large number of livestock and the surrounding environment of the livestock can be obtained in real time through the Internet of things, sufficient data basis is provided for credit risk assessment of the bank on the user, the monitoring data are further input into a trained seasonal morbidity and mortality prediction model, and livestock state prediction results are obtained; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data; secondly, inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk evaluation model is a neural network model trained by using historical livestock state results and user credit information. Compared with a third-party expert evaluation mode, the user credit risk evaluation grade obtained based on the machine learning and neural network model is more accurate, the user credit risk evaluation grade can be continuously adjusted according to real-time monitoring data, the bank can implement risk control, and the bank can provide general financial services for the user.
In specific implementation, monitoring data of livestock and surrounding environment thereof can be obtained based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data.
In an embodiment, monitoring data of the livestock and their surroundings may be collected by a sensor device. In an embodiment, the sensor device may comprise: such as intelligent cameras, weather devices, space sensors, live bird and livestock intelligent wearable equipment and the like.
In an embodiment, after the monitoring data of the livestock and the surrounding environment thereof are obtained, the monitoring data of the livestock and the surrounding environment thereof can be preprocessed through an edge network algorithm. By the edge network algorithm, invalid data can be removed, and the data transmission efficiency is improved. In an embodiment, the monitoring data of the livestock and its surroundings may be preprocessed by an MCU (micro controller Unit) chip provided in the sensor.
In the embodiment, the monitoring data of the livestock and the surrounding environment thereof can be acquired by Long Range Radio (Long distance Radio) technology in an unlicensed network frequency band Range. The data is acquired through the LoRa technology in the unauthorized network frequency range, so that the safety of the data used by a bank can be ensured, and the data is prevented from being stolen.
In the embodiment, a short-distance local area network can be established by using a ZigBee network in a small-range environment (such as indoors), so that monitoring data of livestock and the surrounding environment thereof can be acquired; and uploading the acquired data through LoRa.
In specific implementation, after the monitoring data are obtained, the monitoring data can be input into a trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal incidence and mortality prediction model is a machine learning model trained using historical monitoring data. In an embodiment, inputting the monitoring data into the trained seasonal morbidity and mortality prediction model may further include: and inputting the preprocessed monitoring data into a trained seasonal morbidity and mortality prediction model.
FIG. 2 is a schematic flow chart of training a seasonal incidence and mortality prediction model in an embodiment of the present invention. As shown in fig. 2, the step of training the seasonal incidence and mortality prediction model may include:
In specific implementation, after the livestock state prediction result is obtained, the livestock state prediction result and the credit information of the user can be input into a trained credit risk assessment model to obtain a credit risk assessment grade of the user; the credit risk evaluation model is a neural network model trained by using historical livestock state results and user credit information.
Fig. 3 is a diagram illustrating an embodiment of the animal husbandry credit risk assessment method shown in fig. 1 according to the present invention. As shown in fig. 3, the animal husbandry credit risk assessment method may further include:
The embodiment of the invention also provides a credit risk assessment device for animal husbandry, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to the animal husbandry credit risk assessment method, the implementation of the device can be referred to the implementation of the animal husbandry credit risk assessment method, and repeated parts are not repeated.
Fig. 4 is a schematic structural diagram of the animal husbandry credit risk assessment device in the embodiment of the invention. As shown in fig. 4, the animal husbandry credit risk assessment apparatus according to the embodiment of the present invention may include:
a data obtaining module 402, configured to obtain monitoring data of the livestock and the surrounding environment thereof based on the internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
a livestock state prediction module 404, configured to input the monitoring data into the trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data;
the credit risk evaluation module 406 is used for inputting the livestock state prediction result and the credit information of the user into the trained credit risk evaluation model to obtain a credit risk evaluation grade of the user; the credit risk evaluation model is a neural network model trained by using historical livestock state results and user credit information.
In an embodiment, the livestock status prediction module 404 may further include:
the model training unit is used for acquiring historical monitoring data and extracting a feature vector of the historical monitoring data; training a seasonal disease incidence and mortality prediction model according to the feature vectors of the historical monitoring data and the labels corresponding to the feature vectors of the historical monitoring data; wherein the tag is used to indicate whether the livestock is actually seasonal, sexually transmitted or dead.
In an embodiment, the animal husbandry credit risk assessment apparatus may further include:
the preprocessing module is used for preprocessing the monitoring data of the livestock and the surrounding environment thereof through an edge network algorithm after the data acquisition module acquires the monitoring data of the livestock and the surrounding environment thereof;
the livestock state prediction module is specifically used for:
and inputting the preprocessed monitoring data into a trained seasonal morbidity and mortality prediction model.
Fig. 5 is a diagram of an embodiment of the animal husbandry credit risk assessment apparatus shown in fig. 4 according to the present invention. As shown in fig. 5, the animal husbandry credit risk assessment apparatus may further include:
a credit risk assessment result adjustment module 502, configured to determine whether the livestock is lost according to the livestock position data and the electronic fence data; if the livestock is lost, sending early warning information to a user; acquiring feedback information of the user on the early warning information, and adjusting the credit risk evaluation level of the user according to the feedback information; wherein the feedback information includes: and (5) confirming whether the livestock is true or not.
In an embodiment, the data obtaining module 402 may be specifically configured to:
and acquiring monitoring data of the livestock and the surrounding environment thereof by a long-distance radio LoRa technology in an unauthorized network frequency range.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the animal husbandry credit risk assessment method.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above animal husbandry credit risk assessment method.
In the embodiment of the invention, monitoring data of livestock and surrounding environment thereof are obtained based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data; compared with the technical scheme that a bank cannot carry out credit risk assessment on the animal husbandry user in real time in the prior art, monitoring data of a large number of livestock and the surrounding environment of the livestock can be obtained in real time through the Internet of things, sufficient data basis is provided for credit risk assessment of the bank on the user, the monitoring data are further input into a trained seasonal morbidity and mortality prediction model, and livestock state prediction results are obtained; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data; secondly, inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk evaluation model is a neural network model trained by using historical livestock state results and user credit information. Compared with a third-party expert evaluation mode, the user credit risk evaluation grade obtained based on the machine learning and neural network model is more accurate, the user credit risk evaluation grade can be continuously adjusted according to real-time monitoring data, the bank can implement risk control, and the bank can provide general financial services for the user.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A method for animal husbandry credit risk assessment, comprising:
acquiring monitoring data of livestock and surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
inputting the monitoring data into a trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; wherein the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data;
inputting the livestock state prediction result and the credit information of the user into a trained credit risk assessment model, and acquiring a credit risk assessment grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information.
2. The method of claim 1, wherein the seasonal incidence and mortality prediction model is trained as follows:
acquiring historical monitoring data, and extracting a feature vector of the historical monitoring data;
training a seasonal disease incidence and mortality prediction model according to the feature vectors of the historical monitoring data and the labels corresponding to the feature vectors of the historical monitoring data; wherein the tag is used to indicate whether the livestock is actually seasonal, or dead.
3. The method of claim 1, further comprising, after acquiring the monitoring data for the livestock and their surroundings:
preprocessing the monitoring data of the livestock and the surrounding environment thereof through an edge network algorithm;
inputting the monitoring data into a trained seasonal morbidity and mortality prediction model, comprising:
and inputting the preprocessed monitoring data into a trained seasonal morbidity and mortality prediction model.
4. The method of claim 1, further comprising:
judging whether the livestock are lost or not according to the livestock position data and the electronic fence data;
if the livestock is lost, sending early warning information to a user;
acquiring feedback information of the user on the early warning information, and adjusting the credit risk evaluation level of the user according to the feedback information; wherein the feedback information comprises: and (5) confirming whether the livestock is true or not.
5. The method of claim 1, wherein obtaining the monitoring data of the livestock and their surroundings based on the internet of things comprises:
and acquiring monitoring data of the livestock and the surrounding environment thereof by a long-distance radio LoRa technology in an unauthorized network frequency range.
6. An animal husbandry credit risk assessment device, comprising:
the data acquisition module is used for acquiring monitoring data of the livestock and the surrounding environment thereof based on the Internet of things; the monitoring data includes: livestock position data, livestock heart rate data, electronic fence data, temperature and humidity data and air pressure data;
the livestock state prediction module is used for inputting the monitoring data into the trained seasonal morbidity and mortality prediction model to obtain a livestock state prediction result; the seasonal morbidity and mortality prediction model is a machine learning model trained by using historical monitoring data;
the credit risk evaluation module is used for inputting the livestock state prediction result and the credit information of the user into the trained credit risk evaluation model and acquiring the credit risk evaluation grade of the user; the credit risk assessment model is a neural network model trained by using historical livestock state results and user credit information.
7. The apparatus of claim 6, wherein the livestock status prediction module further comprises:
the model training unit is used for acquiring historical monitoring data and extracting a feature vector of the historical monitoring data; training a seasonal disease incidence and mortality prediction model according to the feature vectors of the historical monitoring data and the labels corresponding to the feature vectors of the historical monitoring data; wherein the label is whether the livestock actually have seasonal disease and/or death.
8. The apparatus of claim 6, further comprising:
the preprocessing module is used for preprocessing the monitoring data of the livestock and the surrounding environment thereof through an edge network algorithm after the data acquisition module acquires the monitoring data of the livestock and the surrounding environment thereof;
the livestock state prediction module is specifically configured to:
and inputting the preprocessed monitoring data into a trained seasonal morbidity and mortality prediction model.
9. The apparatus of claim 6, further comprising:
the credit risk assessment result adjusting module is used for judging whether the livestock is lost or not according to the livestock position data and the electronic fence data; if the livestock is lost, sending early warning information to a user; acquiring feedback information of the user on the early warning information, and adjusting the credit risk evaluation level of the user according to the feedback information; wherein the feedback information includes: and (5) confirming whether the livestock is true or not.
10. The apparatus of claim 6, wherein the data acquisition module is specifically configured to:
and acquiring monitoring data of the livestock and the surrounding environment thereof by a long-distance radio LoRa technology in an unauthorized network frequency range.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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| CN113807954A (en) * | 2021-09-29 | 2021-12-17 | 四川睿尔琪科技有限公司 | Wind control platform for live stock mortgage and risk control management method |
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