CN111369152A - Agricultural land value evaluation optimization method, device and equipment and readable storage medium - Google Patents
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Abstract
The application discloses an agricultural land value evaluation optimization method, device, equipment and readable storage medium, wherein the agricultural land value evaluation optimization method comprises the following steps: the method comprises the steps of obtaining a land image to be assessed corresponding to the land to be assessed, inputting the land image to be assessed into a preset image classification model to classify the land image to be assessed, obtaining an image classification result, determining a land assessment value corresponding to the land to be assessed based on the image classification result, periodically collecting images of time points corresponding to the land to be assessed, inputting the images of the time points into a preset image recognition model to obtain a land change condition corresponding to the land to be assessed, and adjusting the land assessment value based on the land change condition. The application solves the technical problem of low agricultural land value evaluation efficiency.
Description
Technical Field
The application relates to the technical field of artificial intelligence of financial technology (Fintech), in particular to an agricultural land value evaluation optimization method, device and equipment and a readable storage medium.
Background
With the continuous development of financial technologies, especially internet technology and finance, more and more technologies (such as distributed, Blockchain, artificial intelligence and the like) are applied to the financial field, but the financial industry also puts higher requirements on the technologies, such as higher requirements on the distribution of backlog of the financial industry.
With the continuous development of computer software and artificial intelligence, the application of artificial intelligence is more and more extensive, at present, in many current loans related to agricultural planting, before the loans, value evaluation needs to be performed on agricultural land applied by an agricultural borrower to evaluate the value of the agricultural land, and after the loans are released to the agricultural borrower based on the value of the agricultural land, the agricultural land of the agricultural borrower needs to be inspected for whether corresponding crop planting exists or not, the planting condition of the agricultural land and whether the agricultural borrower can complete crop planting in an expected time or not, etc. in order to further evaluate the value of the agricultural land, and the inspection needs to be performed on the spot by an evaluator to perform the inspection on the agricultural land, so that the manpower and the time required by the agricultural land value evaluation are higher, and the efficiency of the agricultural land value evaluation is lower, therefore, the technical problem of low agricultural land value evaluation efficiency exists in the prior art.
Disclosure of Invention
The application mainly aims to provide an agricultural land value evaluation optimization method, device and equipment and a readable storage medium, and aims to solve the technical problem that in the prior art, the agricultural land value evaluation efficiency is low.
In order to achieve the above object, the present application provides an agricultural land value evaluation optimization method applied to an agricultural land value evaluation optimization apparatus, the agricultural land value evaluation optimization method including:
acquiring a land image to be evaluated corresponding to the land to be evaluated, and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result;
determining a land evaluation value corresponding to the land to be evaluated based on the image classification result;
and periodically acquiring each time point image corresponding to the land to be evaluated, inputting each time point image into a preset image recognition model, obtaining a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition.
Optionally, each of the time point images includes a first time point image and a second time point image, the preset image recognition model includes a convolutional neural network,
the step of inputting each time point image into a preset image recognition model to obtain the land change condition corresponding to the land to be assessed comprises the following steps:
inputting each time point image into a preset image recognition model to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix;
and inputting the difference matrix into the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix to obtain the land change condition.
Optionally, the frame difference processing is performed on the first time point image and the second time point image, and the obtaining a difference matrix includes:
respectively acquiring a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image;
and carrying out subtraction operation on the first pixel matrix and the second pixel matrix to obtain the difference matrix.
Optionally, the step of inputting the land image to be evaluated into a preset image classification model to classify the land image to be evaluated to obtain an image classification result includes:
inputting the land image to be evaluated into a preset image classification model so as to carry out convolution and pooling alternative processing on the land image to be evaluated for preset times, and obtaining a convolution pooling processing result;
and fully connecting the convolution pooling processing results to obtain image classification vectors, and obtaining the image classification results from the image classification vectors.
Optionally, the step of determining the land assessment value corresponding to the land to be assessed based on the image classification result comprises:
determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result;
acquiring user data corresponding to the land to be assessed, scoring the land to be assessed based on the user data to obtain a first scoring result, and recording the first scoring result into a land value assessment reference factor;
determining the land valuation value based on the land valuation reference factor and the initial land valuation value.
Optionally, the step of adjusting the land valuation value based on the land change condition comprises:
grading the land to be evaluated based on the land change condition to obtain a second grading result;
and the second grading result is added into a land value evaluation reference factor, and the land evaluation value is adjusted according to the land value evaluation reference factor.
Optionally, the land image to be assessed comprises a satellite image,
the step of obtaining the land image to be evaluated corresponding to the land to be evaluated and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result comprises the following steps:
receiving agricultural land coordinate information submitted by a target user corresponding to the land to be assessed, and determining the effectiveness of the agricultural land coordinate information;
if the agricultural land coordinate information is valid, shooting the satellite image based on the agricultural land coordinate information;
and if the agricultural land coordinate information is invalid, returning invalid information to the target user.
The present application also provides an agricultural land value evaluation optimization device, the agricultural land value evaluation optimization device is a virtual device, and the agricultural land value evaluation optimization device is applied to agricultural land value evaluation optimization equipment, the agricultural land value evaluation optimization device includes:
the image classification module is used for acquiring a land image to be evaluated corresponding to the land to be evaluated, and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result;
the payment module is used for determining the land evaluation value corresponding to the land to be evaluated based on the image classification result;
and the adjusting module is used for periodically acquiring images of each time point corresponding to the land to be evaluated, inputting the images of each time point into a preset image recognition model, obtaining a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition.
Optionally, the adjusting module includes:
the frame difference processing unit is used for inputting each time point image into a preset image recognition model so as to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix;
and the acquisition unit is used for inputting the differential matrix into the convolutional neural network so as to carry out convolution and pooling alternative processing on the differential matrix and obtain the land change condition.
Optionally, the frame difference processing unit includes:
an obtaining subunit, configured to obtain a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image respectively;
and the subtraction operation unit is used for carrying out subtraction operation on the first pixel matrix and the second pixel matrix to obtain the difference matrix.
Optionally, the image classification module comprises:
the convolution and pooling alternate processing unit is used for inputting the land image to be evaluated into a preset image classification model so as to perform convolution and pooling alternate processing on the land image to be evaluated for preset times to obtain a convolution pooling processing result;
and the full connection unit is used for performing full connection on the convolution pooling processing result to obtain an image classification vector and obtaining the image classification result in the image classification vector.
Optionally, the deposit module includes:
the determining unit is used for determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result;
the land evaluation system comprises a first scoring unit, a second scoring unit and a third scoring unit, wherein the first scoring unit is used for acquiring user data corresponding to the land to be evaluated, scoring the land to be evaluated based on the user data to acquire a first scoring result, and recording the first scoring result into a land value evaluation reference factor;
and the paying unit is used for determining the land evaluation value based on the land evaluation reference factor and the initial land evaluation value.
Optionally, the adjusting module further comprises:
the second scoring unit is used for scoring the land to be scored based on the land change condition to obtain a second scoring result;
and the adjusting unit is used for recording the second grading result into a land value evaluation reference factor and adjusting the land evaluation value according to the land value evaluation reference factor.
Optionally, the agricultural land value assessment optimizing device further comprises:
the judging module is used for receiving agricultural land coordinate information submitted by a target user corresponding to the land to be evaluated and determining the effectiveness of the agricultural land coordinate information;
the shooting module is used for shooting the satellite image based on the agricultural land coordinate information if the agricultural land coordinate information is effective;
and the error reporting module is used for returning invalid information to the target user if the agricultural land coordinate information is invalid.
The present application further provides an agricultural land value evaluation optimization apparatus, which is an entity apparatus, the agricultural land value evaluation optimization apparatus including: a memory, a processor, and a program of the agricultural land value assessment optimization method stored on the memory and executable on the processor, the program of the agricultural land value assessment optimization method when executed by the processor may implement the steps of the agricultural land value assessment optimization method as described above.
The present application also provides a readable storage medium having stored thereon a program for implementing the agricultural land value estimation optimization method, which when executed by a processor, implements the steps of the agricultural land value estimation optimization method as described above.
The land evaluation method comprises the steps of obtaining a land image to be evaluated corresponding to the land to be evaluated, inputting the land image to be evaluated into a preset image classification model, classifying the land image to be evaluated to obtain an image classification result, and determining a land evaluation value corresponding to the land to be evaluated based on the image classification result, and then regularly collecting images of time points corresponding to the land to be evaluated, inputting the images of the time points into the preset image recognition model to obtain a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition. That is, the method and the device can determine the land evaluation value of the land to be evaluated by acquiring the image of the land to be evaluated and inputting the image into the preset image classification model to classify the land to be evaluated to obtain the image classification result, and then determine whether the corresponding crops are planted in the land to be evaluated, the planting condition of the land to be evaluated and whether the target user can complete the planting of the crops in the expected time through the preset image recognition model by regularly acquiring the image of the land to be evaluated, so as to adjust the land evaluation value based on the land variation condition. That is, the application provides an agricultural land value evaluation method, the agricultural land value can be evaluated through a preset image classification model and a preset image identification model which are obtained based on deep learning, and accurate evaluation of agricultural land loan is realized under the condition that field investigation is not needed, so that the efficiency of agricultural land value evaluation is improved, and the technical problem of low efficiency of agricultural land value evaluation is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a first embodiment of the agricultural land value assessment optimization method of the present application;
FIG. 2 is a schematic flow chart of a second embodiment of the agricultural land value assessment optimization method of the present application;
fig. 3 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In a first embodiment of the agricultural land value evaluation and optimization method of the present application, referring to fig. 1, the agricultural land value evaluation and optimization method includes:
step S10, acquiring a land image to be assessed corresponding to the land to be assessed, and inputting the land image to be assessed into a preset image classification model to classify the land image to be assessed to obtain an image classification result;
in this embodiment, it should be noted that the preset image classification model is a machine learning model that has been trained based on deep learning, and the land image to be evaluated includes a satellite captured image, a camera captured image, and the like.
The method comprises the steps of obtaining a land image to be evaluated corresponding to a land to be evaluated, inputting the land image to be evaluated into a preset image classification model to classify the land image to be evaluated to obtain an image classification result, and specifically, obtaining the land image to be evaluated corresponding to the land to be evaluated in a preset shooting mode, wherein the preset shooting mode comprises shooting modes such as satellite shooting, unmanned aerial vehicle shooting and mobile phone shooting, further inputting the land image to be evaluated into the preset image classification model, performing data processing on a pixel matrix to be evaluated corresponding to the land image to be evaluated based on a data processing layer in the preset image classification model, and outputting the image classification result, wherein the data processing layer comprises a convolution layer, a pooling layer, a full connection layer and the like, and the data processing comprises convolution processing, pooling processing, full connection and the like, the convolution layer is used for performing the convolution processing, the pooling layer is used for performing the pooling processing, and the full-connection layer is used for performing the full-connection.
It should be noted that the convolution processing process may be understood as: the statistical characteristics of one part of the image features are the same as those of other parts, namely, the statistical characteristics learned in the part can also appear in the other part, so that the learned statistical characteristics are used as a detector and applied to any place of the image features, namely, the statistical characteristics learned by the small-range image are convoluted with the image features of the original large-size image, mathematically, the convolution can be that the characteristic matrix of the corresponding image is multiplied by a plurality of detection matrixes in advance and finally summed to obtain a convolution processing result, the pooling processing comprises modes of maximum pooling, mean pooling and the like, specifically, the convolution processing result is firstly divided into a plurality of pixel matrixes with preset sizes, if the maximum pooling is carried out, the pixel matrixes are replaced by the maximum pixel values of the pixel matrixes, and then new image matrixes are obtained, that is, a pooling processing result is obtained, the full connection can be regarded as a special convolution processing, the special convolution processing result is a one-dimensional vector corresponding to the obtained image, that is, the pixel matrix corresponding to the image is converted into a one-dimensional vector through the full connection, and the one-dimensional vector includes combination information of all features of the image corresponding to the one-dimensional vector.
In step S10, the step of inputting the land image to be evaluated into a preset image classification model to classify the land image to be evaluated to obtain an image classification result includes:
step S11, inputting the land image to be evaluated into a preset image classification model to perform convolution and pooling alternative processing on the land image to be evaluated for a preset number of times to obtain a convolution pooling processing result;
in this embodiment, a unique image matrix to be evaluated exists corresponding to the land image to be evaluated, wherein the image matrix to be evaluated is a pixel matrix, and the pixel matrix is composed of a plurality of pixel values.
Inputting the land image to be assessed into a preset image classification model to perform convolution and pooling alternate processing for preset times on the land image to be assessed to obtain a convolution pooling processing result, specifically, inputting the land image to be assessed into the preset image classification model to perform convolution processing on the image matrix to be assessed corresponding to the land image to be assessed to obtain a convolution processing result, further performing pooling processing on the convolution processing result to obtain a pooling processing result, further performing the convolution processing and the pooling processing repeatedly and alternately on the pooling processing result until the alternate times of the convolution processing and the pooling processing reach the preset times, and then obtaining the convolution pooling processing result.
And step S12, fully connecting the convolution pooling processing results to obtain image classification vectors, and obtaining the image classification results from the image classification vectors.
In this embodiment, the convolution pooling processing results are fully connected to obtain an image classification vector, and the image classification result is obtained in the image classification vector, specifically, the convolution pooling processing results are fully connected to convert the convolution processing results into corresponding one-dimensional feature vectors, where the one-dimensional feature vectors are the image classification vectors, and the one-dimensional feature vectors include one or more feature codes, each feature code corresponds to a semantic meaning, and then an image classification feature code is extracted from the image classification vectors, and then the image classification result is queried based on the image classification feature code.
Wherein the land image to be assessed comprises a satellite image,
the step of obtaining the land image to be evaluated corresponding to the land to be evaluated and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result comprises the following steps:
step A10, receiving agricultural land coordinate information submitted by a target user corresponding to the land to be assessed, and determining the effectiveness of the agricultural land coordinate information;
in this embodiment, agricultural land coordinate information submitted by a target user corresponding to the land to be assessed is received, validity of the agricultural land coordinate information is determined, specifically, the agricultural land coordinate information submitted by the target user corresponding to the land to be assessed is received to obtain block longitude and latitude information and block city longitude and latitude information in the agricultural land coordinate information, further, activity range longitude and latitude information corresponding to an activity range of the loan user is obtained, and further, the validity of the agricultural land coordinate information is determined through a preset validity determination equation set based on the block longitude and latitude information, the block city longitude and latitude information and the activity range longitude and latitude information, wherein the preset validity determination equation set is as follows:
0<x<180
0＜y＜90
(x-XX_x)2+(y-XX_y)2＜d12
(x-YY_x)2+(y-YY_y)2＜d22
the longitude and latitude corresponding to the longitude and latitude information of the parcel are (x, y), the longitude and latitude corresponding to the longitude and latitude information of the city where the parcel is located are (XX _ x, XX _ y), the longitude and latitude of the longitude and latitude information of the activity range are (YY _ x, YY _ y), and d1To preset a first distance, d2The loan user information processing method comprises the steps of presetting a first distance, wherein the preset first distance is a distance used for guaranteeing that the distance between the agricultural land and a city where the agricultural land is located is smaller than a preset first distance threshold, and the preset second distance is a distance used for guaranteeing that the distance between the agricultural land and the loan user is smaller than a preset second distance threshold.
Step A20, if the agricultural land coordinate information is valid, shooting the satellite image based on the agricultural land coordinate information;
in this embodiment, if the agricultural land coordinate information is valid, determining an agricultural land position based on the agricultural land coordinate information, and shooting the satellite image in a satellite shooting manner.
And A30, if the agricultural land coordinate information is invalid, returning invalid information to the target user.
In this embodiment, if the agricultural land coordinate information is invalid, returning invalid information to the target user, specifically, if the agricultural land coordinate information is invalid, returning an error code to the loan user to promote the loan user to resubmit the agricultural land coordinate information.
Step S20, determining the land assessment value corresponding to the land to be assessed based on the image classification result;
in this embodiment, it should be noted that the land assessment value may be used to make a deposit plan for the target user, where the deposit plan includes deposit period number, deposit amount range, and the like, and the image classification result includes classification results such as wasteland, 5-year non-planting, 3-year non-planting, 1-year non-planting, and continuous planting.
Determining a land evaluation value corresponding to the land to be evaluated based on the image classification result, specifically, selecting and determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result, and acquiring a credit score of a target user corresponding to the land to be evaluated, wherein the credit score is related to client information of a loan record, a repayment record, a personal asset and the like of the target user, and then the credit score is counted into the land evaluation reference factor, and further the land evaluation value is determined based on the value evaluation factor and the initial land evaluation value, for example, if the image classification result is a wasteland, the corresponding initial land value is determined to be 10000, and further the credit score is acquired to be 80, then the credit score is counted into the payout reference factor, and the value of the payout reference factor is acquired to be 0.8, the land valuation value is 8000.
The step of determining the land assessment value corresponding to the land to be assessed based on the image classification result comprises the following steps:
step S21, determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result;
in this embodiment, an initial land assessment value corresponding to the land to be assessed is determined based on the image classification result, specifically, an initial land assessment value corresponding to the result code is selected from a preset database based on a result code corresponding to the image classification result, where the result code is an identifier of the image classification result, and the result code includes a character string, a number, and the like.
Step S22, obtaining user data corresponding to the land to be assessed, scoring the land to be assessed based on the user data to obtain a first scoring result, and recording the first scoring result into a land value assessment reference factor;
in this embodiment, it should be noted that the user profile includes user information such as loan records, payment records, and personal property of the target user.
The method comprises the steps of obtaining user data corresponding to the land to be assessed, scoring the land to be assessed based on the user data, obtaining a first scoring result, and recording the first scoring result into a land value assessment reference factor, specifically, obtaining user data of a target user corresponding to the land to be assessed, inputting the user data into a preset scoring model to score the target user, obtaining a user scoring result, taking the user scoring result as the first scoring result of the land to be assessed, and further recording the first scoring result into the land value assessment reference factor to update an actual value of the land value assessment reference factor.
Step S23, determining the land valuation value based on the land valuation reference factor and the initial land valuation value.
In the present embodiment, the land evaluation value is determined based on the land value evaluation reference factor and the initial land evaluation value, and specifically, a product of the actual value and the initial land evaluation value is calculated based on the actual value of the land value evaluation reference factor, and the product is taken as the land evaluation value.
Step S30, periodically collecting images of each time point corresponding to the land to be assessed, inputting the images of each time point into a preset image recognition model, obtaining land change conditions corresponding to the land to be assessed, and adjusting the land assessment value based on the land change conditions.
In this embodiment, it should be noted that the land change condition includes a planting condition and a crop growth condition, where the planting condition includes whether crops are planted in the land or not, the crop growth condition includes growth conditions such as good growth and poor growth, and the preset image recognition model is a machine learning model that has been trained based on deep learning.
Periodically acquiring each time point image corresponding to the land to be assessed, inputting each time point image into a preset image identification model to obtain a land change condition corresponding to the land to be assessed, adjusting the land assessment value based on the land change condition, and specifically, periodically acquiring each time point image corresponding to the land to be assessed, wherein each time point image at least comprises a first time point image and a second time point image, inputting each time point image into a preset image identification model to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix, and further inputting the difference matrix into a convolutional neural network in the image identification model to perform convolution and pooling alternating processing on the difference matrix for preset times to obtain convolution and pooling alternating processing results, and carrying out full connection on the convolution and pooling alternative processing results to obtain an image identification vector, further obtaining each image identification feature code in the image identification, further inquiring the land change condition based on each image identification feature code, further recording the land change condition into a land value evaluation reference factor, and adjusting the land evaluation value based on the land value evaluation reference factor.
Wherein, in step S30, the step of adjusting the land estimation value based on the land change condition includes:
step S31, scoring the land to be scored based on the land change condition to obtain a second scoring result;
in this embodiment, the land to be assessed is scored based on the land change condition, a second scoring result is obtained, specifically, a land change condition code corresponding to the land change condition is obtained, and the corresponding second scoring result is queried according to the land change condition code, where the land change condition code is an identifier of the land change condition.
And step S32, recording the second grading result into a land value evaluation reference factor, and adjusting the land evaluation value according to the land value evaluation reference factor.
In this embodiment, the second scoring result is included in a land value evaluation reference factor, and the land evaluation value is adjusted according to the land value evaluation reference factor, specifically, the second scoring result is included in the land value evaluation reference factor to update the land value evaluation reference factor, and then the product of the updated land value evaluation reference factor and the land evaluation value is calculated to adjust the land evaluation value, for example, if the second scoring result is 110 and the land value evaluation reference factor is 1, the second scoring result is included in the land value evaluation reference factor to obtain a land value evaluation reference factor of 1.1, and if the land evaluation value is 10000, the adjusted land evaluation value is 11000.
In the embodiment, the land image to be assessed corresponding to the land to be assessed is obtained, the land image to be assessed is input into a preset image classification model to classify the land image to be assessed, an image classification result is obtained, the land assessment value corresponding to the land to be assessed is determined based on the image classification result, time point images corresponding to the land to be assessed are collected periodically, the time point images are input into a preset image recognition model to obtain the land change condition corresponding to the land to be assessed, and the land assessment value is adjusted based on the land change condition. That is, in the embodiment, the land to be evaluated of the land to be evaluated is obtained by obtaining the image of the land to be evaluated and inputting the image to be evaluated into the preset image classification model, so as to classify the land to be evaluated, the image classification result is obtained, the land evaluation value of the land to be evaluated can be determined according to the image classification result, and further, the image of the land to be evaluated is periodically collected, and the preset image recognition model is used to determine whether the corresponding crop planting, the planting condition of the land to be evaluated, whether the target user can complete the planting of the crop within the expected time, and the like, so as to adjust the land evaluation value based on the land change condition. That is, the embodiment provides an agricultural land value assessment method, by means of the preset image classification model and the preset image recognition model obtained based on the deep learning, the agricultural land value can be assessed, and then the agricultural land loan is accurately assessed under the condition that field investigation is not needed, so that the agricultural land value assessment efficiency is improved, and the technical problem of low agricultural land value assessment efficiency is solved.
Further, referring to fig. 2, in another embodiment of the present application based on the first embodiment of the present application, in step S30, each of the time point images includes a first time point image and a second time point image, the preset image recognition model includes a convolutional neural network,
the step of inputting each time point image into a preset image recognition model to obtain the land change condition corresponding to the land to be assessed comprises the following steps:
step A10, inputting each time point image into a preset image recognition model, so as to perform frame difference processing on the first time point image and the second time point image, and obtain a difference matrix;
in this embodiment, it should be noted that the time point image at least includes a first time point image and a second time point image, and a time interval between the capturing of the first time point image and the capturing of the second time point image is a preset time period, and the preset time period may be set by a user, for example, if the preset time period is set to be one month, an image captured on a first day in the preset time period is the first time point image, and an image captured on a second day in the preset time period is the second time point image.
And inputting each time point image into a preset image recognition model to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix, and specifically, inputting each time point image into a preset image recognition model to perform frame difference processing on a pixel matrix corresponding to the first time point image and a pixel matrix corresponding to the second time point image to obtain the difference matrix.
Wherein the step of performing frame difference processing on the first time point image and the second time point image to obtain a difference matrix comprises:
step a11, obtaining a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image respectively;
in this embodiment, a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image are respectively obtained, specifically, the first time point image and the second time point image are respectively input to a preset image transformation model, and the first pixel matrix corresponding to the first time point image and the second pixel matrix corresponding to the second time point image are output, where the preset image transformation model may be implemented based on MATLAB (matrix & laboratory).
Step a12, performing subtraction on the first pixel matrix and the second pixel matrix to obtain the difference matrix.
In this embodiment, the first pixel matrix and the second pixel matrix are subtracted to obtain the difference matrix, and specifically, the difference matrix is obtained by subtracting corresponding pixel values in the first pixel matrix and the second pixel matrix.
And A20, inputting the difference matrix into the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix to obtain the land change condition.
In this embodiment, the difference matrix is input to the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix to obtain the land change condition, specifically, the difference matrix is input to the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix for a preset number of times to obtain convolution and pooling processing results, and then the convolution and pooling processing results are fully connected to obtain a land change condition vector, and then the land change condition is queried based on the feature codes in the land change condition vector, for example, assuming that the land change condition vector is (1, 1), where 0 represents that the agricultural land is determined to be planted with crops as corn and 1 represents that the growth condition of corn is good.
In the embodiment, each time point image is input into a preset image recognition model to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix, and the difference matrix is input into the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix to obtain the land change condition. That is, the agricultural land image of each time point is collected, the frame difference is carried out on the agricultural land image, the differential matrix is obtained, the differential matrix is input into the preset image recognition image, the land change condition can be output, the value evaluation of the lended agricultural land is realized on the premise of not carrying out field investigation, the loan risk is reduced, the condition that the agricultural land value evaluation efficiency is lowered due to manual investigation after lending is avoided, and therefore a foundation is laid for solving the technical problem of low agricultural land value evaluation efficiency.
Referring to fig. 3, fig. 3 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 3, the agricultural land value evaluation optimizing apparatus may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the agricultural land value assessment optimizing device may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
Those skilled in the art will appreciate that the configuration of the agricultural land value estimation optimization apparatus shown in fig. 3 does not constitute a limitation of the agricultural land value estimation optimization apparatus, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 3, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, and an agricultural land value evaluation optimization program. The operating system is a program for managing and controlling hardware and software resources of the agricultural land value assessment optimization device, and supports the operation of the agricultural land value assessment optimization program and other software and/or programs. The network communication module is used for realizing communication among the components in the memory 1005 and communication with other hardware and software in the agricultural land value evaluation optimization system.
In the agricultural land value estimation optimization apparatus shown in fig. 3, the processor 1001 is configured to execute an agricultural land value estimation optimization program stored in the memory 1005 to implement the steps of the agricultural land value estimation optimization method according to any one of the above.
The specific implementation mode of the agricultural land value evaluation and optimization device is basically the same as that of each embodiment of the agricultural land value evaluation and optimization method, and details are not repeated here.
The embodiment of the present application further provides an agricultural land value evaluation and optimization device, where the agricultural land value evaluation and optimization device is applied to an agricultural land value evaluation and optimization device, and the agricultural land value evaluation and optimization device includes:
the image classification module is used for acquiring a land image to be evaluated corresponding to the land to be evaluated, and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result;
the payment module is used for determining the land evaluation value corresponding to the land to be evaluated based on the image classification result;
and the adjusting module is used for periodically acquiring images of each time point corresponding to the land to be evaluated, inputting the images of each time point into a preset image recognition model, obtaining a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition.
Optionally, the adjusting module includes:
the frame difference processing unit is used for inputting each time point image into a preset image recognition model so as to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix;
and the acquisition unit is used for inputting the differential matrix into the convolutional neural network so as to carry out convolution and pooling alternative processing on the differential matrix and obtain the land change condition.
Optionally, the frame difference processing unit includes:
an obtaining subunit, configured to obtain a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image respectively;
and the subtraction operation unit is used for carrying out subtraction operation on the first pixel matrix and the second pixel matrix to obtain the difference matrix.
Optionally, the image classification module comprises:
the convolution and pooling alternate processing unit is used for inputting the land image to be evaluated into a preset image classification model so as to perform convolution and pooling alternate processing on the land image to be evaluated for preset times to obtain a convolution pooling processing result;
and the full connection unit is used for performing full connection on the convolution pooling processing result to obtain an image classification vector and obtaining the image classification result in the image classification vector.
Optionally, the deposit module includes:
the determining unit is used for determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result;
the land evaluation system comprises a first scoring unit, a second scoring unit and a third scoring unit, wherein the first scoring unit is used for acquiring user data corresponding to the land to be evaluated, scoring the land to be evaluated based on the user data to acquire a first scoring result, and recording the first scoring result into a land value evaluation reference factor;
and the paying unit is used for determining the land evaluation value based on the land evaluation reference factor and the initial land evaluation value.
Optionally, the adjusting module further comprises:
the second scoring unit is used for scoring the land to be scored based on the land change condition to obtain a second scoring result;
and the adjusting unit is used for recording the second grading result into a land value evaluation reference factor and adjusting the land evaluation value according to the land value evaluation reference factor.
Optionally, the agricultural land value assessment optimizing device further comprises:
the judging module is used for receiving agricultural land coordinate information submitted by a target user corresponding to the land to be evaluated and determining the effectiveness of the agricultural land coordinate information;
the shooting module is used for shooting the satellite image based on the agricultural land coordinate information if the agricultural land coordinate information is effective;
and the error reporting module is used for returning invalid information to the target user if the agricultural land coordinate information is invalid.
The specific implementation manner of the agricultural land value evaluation and optimization device is basically the same as that of each embodiment of the agricultural land value evaluation and optimization method, and is not described herein again.
The embodiment of the application provides a readable storage medium, and the readable storage medium stores one or more programs, which can be executed by one or more processors for realizing the steps of the agricultural land value assessment optimization method of any one of the above.
The specific implementation manner of the readable storage medium of the application is basically the same as that of each embodiment of the agricultural land value evaluation and optimization method, and is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. An agricultural land value evaluation optimization method is characterized by comprising the following steps:
acquiring a land image to be evaluated corresponding to the land to be evaluated, and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result;
determining a land evaluation value corresponding to the land to be evaluated based on the image classification result;
and periodically acquiring each time point image corresponding to the land to be evaluated, inputting each time point image into a preset image recognition model, obtaining a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition.
2. The agricultural land value estimation optimization method according to claim 1, wherein each of the time-point images includes a first time-point image and a second time-point image, the preset image recognition model includes a convolutional neural network,
the step of inputting each time point image into a preset image recognition model to obtain the land change condition corresponding to the land to be assessed comprises the following steps:
inputting each time point image into a preset image recognition model to perform frame difference processing on the first time point image and the second time point image to obtain a difference matrix;
and inputting the difference matrix into the convolutional neural network to perform convolution and pooling alternative processing on the difference matrix to obtain the land change condition.
3. The agricultural land value evaluation optimization method according to claim 2, wherein the step of performing frame difference processing on the first time point image and the second time point image to obtain a difference matrix comprises:
respectively acquiring a first pixel matrix corresponding to the first time point image and a second pixel matrix corresponding to the second time point image;
and carrying out subtraction operation on the first pixel matrix and the second pixel matrix to obtain the difference matrix.
4. The agricultural land value evaluation optimization method of claim 1, wherein the step of inputting the land image to be evaluated into a preset image classification model to classify the land image to be evaluated to obtain an image classification result comprises the following steps:
inputting the land image to be evaluated into a preset image classification model so as to carry out convolution and pooling alternative processing on the land image to be evaluated for preset times, and obtaining a convolution pooling processing result;
and fully connecting the convolution pooling processing results to obtain image classification vectors, and obtaining the image classification results from the image classification vectors.
5. The agricultural land value assessment optimization method according to claim 1, wherein the step of determining the land assessment value corresponding to the land to be assessed based on the image classification result comprises:
determining an initial land evaluation value corresponding to the land to be evaluated based on the image classification result;
acquiring user data corresponding to the land to be assessed, scoring the land to be assessed based on the user data to obtain a first scoring result, and recording the first scoring result into a land value assessment reference factor;
determining the land valuation value based on the land valuation reference factor and the initial land valuation value.
6. The agricultural land value assessment optimization method of claim 1, wherein said step of adjusting said land assessment value based on said land change condition comprises:
grading the land to be evaluated based on the land change condition to obtain a second grading result;
and the second grading result is added into a land value evaluation reference factor, and the land evaluation value is adjusted according to the land value evaluation reference factor.
7. The agricultural land value estimation optimization method of claim 1, wherein the land image to be assessed includes a satellite image,
the step of obtaining the land image to be evaluated corresponding to the land to be evaluated and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result comprises the following steps:
receiving agricultural land coordinate information submitted by a target user corresponding to the land to be assessed, and determining the effectiveness of the agricultural land coordinate information;
if the agricultural land coordinate information is valid, shooting the satellite image based on the agricultural land coordinate information;
and if the agricultural land coordinate information is invalid, returning invalid information to the target user.
8. An agricultural land value evaluation optimizing apparatus, characterized by comprising:
the image classification module is used for acquiring a land image to be evaluated corresponding to the land to be evaluated, and inputting the land image to be evaluated into a preset image classification model so as to classify the land image to be evaluated and obtain an image classification result;
the payment module is used for determining the land evaluation value corresponding to the land to be evaluated based on the image classification result;
and the adjusting module is used for periodically acquiring images of each time point corresponding to the land to be evaluated, inputting the images of each time point into a preset image recognition model, obtaining a land change condition corresponding to the land to be evaluated, and adjusting the land evaluation value based on the land change condition.
9. An agricultural land value evaluation optimizing apparatus, characterized by comprising: a memory, a processor, and a program stored on the memory for implementing the agricultural land value assessment optimization method,
the memory is used for storing a program for realizing the agricultural land value evaluation optimization method;
the processor is configured to execute a program for implementing the agricultural land value estimation optimization method to implement the steps of the agricultural land value estimation optimization method according to any one of claims 1 to 7.
10. A readable storage medium, characterized in that the readable storage medium has stored thereon a program for implementing an agricultural land value estimation optimization method, which is executed by a processor to implement the steps of the agricultural land value estimation optimization method according to any one of claims 1 to 7.
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