CN117876125A

CN117876125A - Data evaluation method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN117876125A
Application number: CN202410038186.3A
Authority: CN
Inventors: 刘泽平
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2024-01-09
Filing date: 2024-01-09
Publication date: 2024-04-12

Abstract

The application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a data evaluation method based on artificial intelligence, which comprises the following steps: after a planting insurance claim settlement request corresponding to a target disaster area is acquired, receiving farmland image data which is acquired by an unmanned aerial vehicle and corresponds to the target disaster area; performing image preprocessing on farmland image data to obtain target image data; extracting features of the target image data to obtain crop feature data; invoking a loss analysis model to generate loss degree evaluation data respectively corresponding to various crops contained in the target image data; a claims value is generated based on the loss level evaluation data. The application also provides a data evaluation device, computer equipment and a storage medium based on the artificial intelligence. In addition, the present application relates to blockchain technology in which claim values may be stored. The method and the device can be applied to agricultural risk claim settlement scenes in the financial field, and can effectively improve the damage assessment processing efficiency and the damage assessment processing accuracy of planting risks.

Description

Data evaluation method, device, equipment and storage medium based on artificial intelligence

Technical Field

The present application relates to the field of artificial intelligence development technology and financial technology, and in particular, to an artificial intelligence-based data evaluation method, apparatus, computer device, and storage medium.

Background

In the field of agricultural insurance, when a natural disaster occurs, a certain influence is usually caused on the planting industry, and for the under-covered planting households, an insurance company performs compensation or responsibility giving actions according to contract regulations. Loss assessment of planting risk is a complex and time-consuming process. Conventional methods for assessing loss of planting risk used by insurance companies typically require manual field investigation and assessment. However, this loss assessment approach includes the following drawbacks: 1. the efficiency is low: human assessment requires a lot of time and human resources, and especially after a disaster has occurred, the area of farmland to be assessed may be very large, which makes the claim settlement process very inefficient. 2. The cost is high: human field investigation and evaluation requires a large amount of manpower and material resources, which makes the cost of the claim process very high. 3. Data collection is difficult: in some situations, such as after a disaster, it may be very difficult to manually collect data, thereby affecting the accuracy and timeliness of the loss assessment.

Disclosure of Invention

The embodiment of the application aims to provide a data evaluation method, a device, computer equipment and a storage medium based on artificial intelligence, so as to solve the technical problems of low processing efficiency, high cost and difficult data collection of the traditional planting risk loss evaluation mode adopted by the existing insurance company.

In order to solve the above technical problems, the embodiments of the present application provide a data evaluation method based on artificial intelligence, which adopts the following technical scheme:

after a planting insurance claim settlement request corresponding to a target disaster area is acquired, receiving farmland image data which is acquired by an unmanned aerial vehicle and corresponds to the target disaster area;

performing image preprocessing on the farmland image data to obtain corresponding target image data;

extracting features of the target image data to obtain corresponding crop feature data;

invoking a preset loss analysis model to perform loss evaluation analysis on the crop characteristic data to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data;

and generating claim settlement values respectively corresponding to the various crops based on the loss degree evaluation data of the various crops.

Further, the step of performing image preprocessing on the farmland image data to obtain corresponding target image data specifically includes:

performing image correction on the farmland image data to obtain corresponding first image data;

noise filtering is carried out on the first image data to obtain corresponding second image data;

performing image enhancement on the third image data to obtain corresponding third image data;

and taking the third image data as the target image data.

Further, the step of extracting features from the target image data to obtain corresponding crop feature data specifically includes:

carrying out feature extraction processing on the target image data with respect to the morphology of crops to obtain corresponding morphological features;

performing feature extraction processing on the target image data with respect to the colors of crops to obtain corresponding color features;

performing feature extraction processing on crop textures on the target image data to obtain corresponding texture features;

the crop feature data is generated based on the morphological feature, the color feature, and the texture feature.

Further, before the step of calling a preset loss analysis model to perform loss evaluation analysis on the crop feature data to obtain loss degree evaluation data corresponding to each crop contained in the target image data, the method further includes:

Acquiring pre-acquired historical farmland disaster-affected image data;

constructing sample data based on the historical farmland disaster image data;

calling a preset initial neural network model;

and performing model training and adjustment processing on the initial neural network model based on the sample data to obtain the loss analysis model meeting the preset model construction requirement.

Further, the step of generating the claim values corresponding to the respective crops based on the loss degree evaluation data of the respective crops specifically includes:

obtaining crop types respectively corresponding to the various crops;

obtaining an estimated loss calculation formula corresponding to each crop type;

and carrying out corresponding calculation processing on the loss degree evaluation data of the various crops based on the loss assessment calculation formula, and generating claim settlement values corresponding to the various crops respectively.

Further, after the step of calling a preset loss analysis model to perform loss evaluation analysis on the crop feature data to obtain loss degree evaluation data corresponding to each crop contained in the target image data, the method further includes:

Acquiring a preset visual processing strategy;

processing loss degree evaluation data corresponding to each crop on the basis of the visual processing strategy to obtain processed display data;

and carrying out display processing on the display data.

Further, after the step of generating the claim values corresponding to the respective crops based on the loss degree evaluation data of the respective crops, the method further comprises:

generating corresponding claim analysis reports based on the claim values corresponding to the crops respectively;

acquiring communication information of target claims settlement personnel;

and sending the claim analysis report to the target claim settlement personnel based on the communication information.

In order to solve the above technical problems, the embodiments of the present application further provide an artificial intelligence based data evaluation device, which adopts the following technical scheme:

the receiving module is used for receiving farmland image data which is acquired by the unmanned aerial vehicle and corresponds to the target disaster area after acquiring a planting insurance claim request which corresponds to the target disaster area;

the preprocessing module is used for carrying out image preprocessing on the farmland image data to obtain corresponding target image data;

The extraction module is used for carrying out feature extraction on the target image data to obtain corresponding crop feature data;

the analysis module is used for calling a preset loss analysis model to carry out loss evaluation analysis on the crop characteristic data to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data;

and the first generation module is used for generating claim settlement values respectively corresponding to the various crops based on the loss degree evaluation data of the various crops.

In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:

In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

after obtaining a planting insurance claim request corresponding to a target disaster area, the embodiment of the application firstly receives farmland image data which is collected by an unmanned aerial vehicle and corresponds to the target disaster area; then carrying out image preprocessing on the farmland image data to obtain corresponding target image data; then, extracting features of the target image data to obtain corresponding crop feature data; subsequently, a preset loss analysis model is called to carry out loss evaluation analysis on the crop characteristic data, so as to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data; and finally, generating the claim settlement values respectively corresponding to the various crops based on the loss degree evaluation data of the various crops. According to the method and the device, the farmland image data corresponding to the target disaster area can be rapidly and accurately acquired based on the unmanned aerial vehicle, after the farmland image data is subjected to image preprocessing and feature extraction to obtain the crop feature data, the crop feature data is subjected to loss evaluation analysis processing through the use of the loss analysis model, loss degree evaluation data respectively corresponding to various crops contained in the target image data can be rapidly and accurately determined, loss degree evaluation data of the crops can be further obtained, claim settlement values respectively corresponding to the crops can be rapidly generated, the damage settlement processing efficiency of planting risks can be effectively improved, the cost of damage settlement processing is reduced, and the error of human evaluation is reduced. The accuracy of the generated claim settlement value is effectively ensured.

Drawings

For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of an artificial intelligence based data evaluation method according to the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based data evaluation device according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the data evaluation method based on artificial intelligence provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the data evaluation device based on artificial intelligence is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of an artificial intelligence based data evaluation method according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The data evaluation method based on the artificial intelligence can be applied to any scene needing planting risk assessment, and the data evaluation method based on the artificial intelligence can be applied to products of the scenes, for example, planting risk assessment in the field of financial insurance. The artificial intelligence-based data evaluation method comprises the following steps:

Step S201, after obtaining a planting insurance claim request corresponding to a target disaster area, receiving farmland image data corresponding to the target disaster area, which is collected by an unmanned aerial vehicle.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data evaluation method based on artificial intelligence operates may acquire the farmland image data through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. If flood, drought, insect damage and other disasters are suffered in the farmland area, the farmland area becomes a disaster-stricken area. The unmanned aerial vehicle can be used for carrying a high-resolution camera for aerial photography, and a large amount of image data of a target disaster area can be obtained to obtain the farmland image data. The unmanned aerial vehicle can rapidly cover a large-area farmland, provide real-time image and data feedback, and help agricultural insurance companies to better know the current conditions and potential risks of crops. This helps take measures in time, reduce losses, and speed up the claims process. The application of unmanned aerial vehicle technology brings new possibilities for agricultural insurance claims. The unmanned aerial vehicle can rapidly and accurately monitor a large-area farmland and acquire high-definition images and accurate data, so that the efficiency and accuracy of agricultural risk assessment and loss assessment are greatly improved, better protection and service can be provided for farmers, and sustainable development of the agricultural industry is promoted.

Step S202, performing image preprocessing on the farmland image data to obtain corresponding target image data.

In this embodiment, the above-mentioned specific implementation process of performing image preprocessing on the farmland image data to obtain corresponding target image data will be described in further detail in the following specific embodiments, which will not be described herein.

And step S203, extracting the characteristics of the target image data to obtain corresponding crop characteristic data.

In this embodiment, the specific implementation process of extracting the features of the target image data to obtain the corresponding crop feature data is described in further detail in the following specific embodiments, which will not be described herein.

And step S204, invoking a preset loss analysis model to perform loss evaluation analysis on the crop characteristic data to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data.

In this embodiment, the crop feature data may be input into the loss analysis model to perform loss evaluation analysis on the crop feature data by the loss analysis model, and loss degree evaluation data corresponding to each of the crops included in the target image data may be output. The model construction process of the loss analysis model will be described in further detail in the following specific embodiments, which will not be described herein.

And step S205, generating claim settlement values corresponding to the various crops respectively based on the loss degree evaluation data of the various crops.

In this embodiment, the foregoing implementation procedure will be described in further detail in the following embodiments, which will not be described herein.

After acquiring a planting insurance claim request corresponding to a target disaster area, the method firstly receives farmland image data which is acquired by an unmanned aerial vehicle and corresponds to the target disaster area; then carrying out image preprocessing on the farmland image data to obtain corresponding target image data; then, extracting features of the target image data to obtain corresponding crop feature data; subsequently, a preset loss analysis model is called to carry out loss evaluation analysis on the crop characteristic data, so as to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data; and finally, generating the claim settlement values respectively corresponding to the various crops based on the loss degree evaluation data of the various crops. According to the method, the farmland image data corresponding to the target disaster area can be rapidly and accurately acquired based on the unmanned aerial vehicle, after the farmland image data is subjected to image preprocessing and feature extraction to obtain the crop feature data, the crop feature data is subjected to loss evaluation analysis processing through the use of the loss analysis model, loss degree evaluation data respectively corresponding to various crops contained in the target image data can be rapidly and accurately determined, and then the loss degree evaluation data of the crops can be respectively determined, so that claim settlement values respectively corresponding to the crops can be rapidly generated, the damage processing efficiency of planting risks can be effectively improved, the cost of damage determination processing is reduced, and the error of human evaluation is reduced. The accuracy of the generated claim settlement value is effectively ensured.

In some alternative implementations, step S202 includes the steps of:

and carrying out image correction on the farmland image data to obtain corresponding first image data.

In this embodiment, the image correction may be performed on the farmland image data by calling a commonly used image correction tool, so as to obtain corresponding first image data.

And carrying out noise filtering on the first image data to obtain corresponding second image data.

In this embodiment, the noise filtering tool may be called to perform noise filtering on the farmland image data, so as to obtain corresponding second image data.

And carrying out image enhancement on the second image data to obtain corresponding third image data.

In this embodiment, the image enhancement may be performed on the farmland image data by calling a commonly used image enhancement tool, so as to obtain corresponding third image data.

And taking the third image data as the target image data.

The method comprises the steps of obtaining corresponding first image data by carrying out image correction on farmland image data; then, noise filtering is carried out on the first image data to obtain corresponding second image data; then, carrying out image enhancement on the second image data to obtain corresponding third image data; and taking the third image data as the target image data. According to the method and the device, the image correction, noise filtering and image enhancement processing are carried out on the farmland image data to obtain the target image data, so that the image quality and usability of the farmland image data can be effectively improved, and the data accuracy of the generated target image data is ensured.

In some alternative implementations of the present embodiment, step S203 includes the steps of:

and carrying out feature extraction processing on the target image data with respect to the morphology of crops to obtain corresponding morphological features.

In this embodiment, the morphological feature extraction may be performed on the crop included in the target image data to obtain the corresponding morphological feature.

And carrying out feature extraction processing on the target image data with respect to the colors of crops to obtain corresponding color features.

In this embodiment, the color feature extraction may be performed on the crop included in the target image data to obtain the corresponding color feature.

And carrying out feature extraction processing on the target image data with respect to crop textures to obtain corresponding texture features.

In this embodiment, the corresponding texture features may be obtained by extracting texture features of the crop included in the target image data.

In this embodiment, the morphological feature, the color feature and the texture feature may be integrated to obtain a corresponding integrated feature set, and the integrated feature set may be used as the crop feature data.

The method comprises the steps of extracting and processing characteristics of the morphology of crops from target image data to obtain corresponding morphological characteristics; then, carrying out feature extraction processing on the target image data with respect to the colors of crops to obtain corresponding color features; then, carrying out feature extraction processing on the target image data with respect to crop textures to obtain corresponding texture features; the crop feature data is then generated based on the morphological feature, the color feature, and the texture feature. According to the method and the device, the characteristic extraction processing on the shape, the color and the texture of the crops is carried out on the target image data, so that the characteristic data which is used for carrying out the loss evaluation analysis according with the loss analysis model can be quickly and accurately generated, and the accuracy of the generated characteristic data of the crops is ensured.

In some alternative implementations, before step S204, the electronic device may further perform the following steps:

acquiring pre-acquired historical farmland disaster-affected image data.

In this embodiment, the historical farmland disaster image data may be image data of a farmland in which a disaster has occurred in a historical time period collected in advance according to an unmanned aerial vehicle technology. The value of the historical time period is not particularly limited, and may be set according to actual service usage requirements, for example, may be set within the previous year from the current time.

And constructing sample data based on the historical farmland disaster image data.

In this embodiment, the initial sample data may be constructed by performing image preprocessing and feature extraction processing on the historical farmland disaster image data. The process of performing image preprocessing and feature extraction processing on the historical farmland disaster-affected image data may refer to the process of performing image preprocessing on the farmland image data and performing feature extraction on the target image data, which are not described in detail herein. And then, carrying out labeling processing of adding loss degree labels on the initial sample data to obtain the sample data. Specifically, the loss degree information refers to difference information of the original form of the historical crop in the historical farmland disaster-stricken image data and the original form of the crop in the pre-disaster picture, if the difference information of the historical crop in the historical farmland disaster-stricken image data and the original form of the crop in the pre-disaster picture is smaller, the loss degree of the historical farmland disaster-stricken image data can be determined to be mild, and a mild loss degree label is marked on the historical crop; if the difference information of the original forms of the historical crops and the crops in the pre-disaster pictures in the historical farmland disaster-stricken image data is large, determining that the loss degree of the historical farmland disaster-stricken image data is moderate, and labeling the historical crops with moderate loss degree labels; if the difference information of the historical crop in the historical farmland disaster-affected image data and the original form of the crop in the pre-disaster picture is extremely large, the loss degree of the historical farmland disaster-affected image data can be determined to be severe, and the historical crop is marked with a severe loss degree label.

And calling a preset initial neural network model.

In this embodiment, the initial neural network model may be a deep learning model.

In this embodiment, the sample data may be first divided into training data and test data according to a predetermined division ratio. Model training is carried out on the initial neural network model by using the training data, so that the initial neural network model learns the corresponding relation between the historical crops in the historical farmland disaster image data in the sample data and the loss degree label of the historical crops, and the trained initial neural network model is obtained. And subsequently, using the test data to adjust model parameters of the trained initial neural network model until the evaluation index of the trained initial neural network model is greater than a preset evaluation threshold, and taking the adjusted initial neural network model as the loss analysis model. The numerical selection of the above-mentioned dividing ratio is not particularly limited, and may be, for example, 7:3. The selection of the evaluation index is not particularly limited, and may include, for example, accuracy and the like.

The method comprises the steps of acquiring pre-acquired historical farmland disaster-affected image data; then constructing sample data based on the historical farmland disaster-affected image data; then calling a preset initial neural network model; and performing model training and adjustment processing on the initial neural network model based on the sample data to obtain the loss analysis model meeting the preset model construction requirement. According to the method and the device, the sample data are built according to the collected historical farmland disaster image data in advance, and then the model training and the adjustment processing are carried out on the initial neural network model by using the sample data, so that the loss analysis model meeting the preset model building requirement is obtained, the model building process of the loss analysis model is completed, the model effect and the prediction accuracy of the obtained loss analysis model are effectively ensured, and the building efficiency of the loss analysis model is improved.

In some alternative implementations, step S205 includes the steps of:

and obtaining crop types respectively corresponding to the various crops.

In the present embodiment, crop types corresponding to respective crops can be obtained by performing crop type analysis on the respective crops included in the target image data. By way of example, the crop may include wheat, rice, corn, and the like.

And obtaining an estimated loss calculation formula corresponding to each crop type.

In this embodiment, for different types of crops, an damage assessment calculation formula suitable for performing damage assessment treatment on damaged crops of different crop types is constructed in advance according to actual requirements of claim settlement business of planting risks.

In this embodiment, for each crop, the loss evaluation data corresponding to the crop is calculated using an estimated loss calculation formula corresponding to the crop type to which the crop belongs, and the calculated calculation result is used as the claim value corresponding to the crop.

The application obtains crop types respectively corresponding to various crops; then obtaining an estimated loss calculation formula corresponding to each crop type respectively; and carrying out corresponding calculation processing on the loss degree evaluation data of the various crops based on the loss assessment calculation formula, and generating claim settlement values corresponding to the various crops respectively. According to the method and the device, the damage assessment calculation formulas corresponding to the crop types are determined according to the crop types of the crops, and further corresponding calculation processing is carried out on the damage degree evaluation data of the crops based on the use of the damage assessment calculation formulas, so that the claim settlement values corresponding to the crops can be rapidly and accurately generated, the processing efficiency of damage assessment processing of the crops in a target disaster area is effectively improved, and the accuracy of the generated claim settlement values is ensured.

In some optional implementations of this embodiment, after step S204, the electronic device may further perform the following steps:

and acquiring a preset visual processing strategy.

In this embodiment, the selection of the visualization processing policy is not specifically limited, and may be set according to actual display requirements. By way of example, the above-described visualization processing strategies may include generating a loss thermodynamic diagram, a loss area statistics table, and the like.

And processing the loss degree evaluation data corresponding to each crop on the basis of the visual processing strategy to obtain processed display data.

In this embodiment, if the visual processing policy is a processing policy for generating a loss thermodynamic diagram, data processing is performed on loss degree evaluation data corresponding to each of the crops to construct a corresponding loss thermodynamic diagram, and the loss thermodynamic diagram is used as the display data after the processing. And if the visualized processing strategy is a processing strategy for generating a loss area statistical table, carrying out data processing on loss degree evaluation data corresponding to each crop so as to construct a corresponding loss area statistical table, and taking the loss area statistical table as the processed display data.

And carrying out display processing on the display data.

In this embodiment, the display mode of the display data is not limited, and for example, a billboard display mode, a web page display mode and the like can be adopted.

The method comprises the steps of obtaining a preset visual processing strategy; then, processing loss degree evaluation data corresponding to each crop on the basis of the visual processing strategy to obtain processed display data; and carrying out display processing on the display data. According to the method and the system for processing the crop characteristic data, after the preset loss analysis model is called to conduct loss evaluation analysis on the crop characteristic data to obtain loss degree evaluation data corresponding to various crops contained in the target image data, the loss degree evaluation data corresponding to various crops are further processed based on the preset visual processing strategy to obtain processed display data, and further the display data are displayed, so that relevant business personnel can conveniently and fast check and understand the loss degree evaluation data corresponding to various crops contained in farmland image data corresponding to a disaster area, further the relevant business users can be assisted to rapidly complete processing of planting insurance claim settlement requests, and improvement of claim settlement processing efficiency of relevant business personnel and use experience of the relevant business personnel are facilitated.

In some optional implementations of this embodiment, after step S205, the electronic device may further perform the following steps:

and generating corresponding claim analysis reports based on the claim values respectively corresponding to the crops.

In this embodiment, the claim analysis report may be generated by inputting the claim values corresponding to the respective crops to the respective positions in the preset claim analysis report template. The claim analysis report template is a report template file constructed according to the actual claim analysis service requirement, the content of the claim analysis report template is not particularly limited, and the claim analysis report template can be written and generated by corresponding claim analysis service operation and maintenance personnel.

And acquiring communication information of the target claimant.

In this embodiment, the target claimant corresponds to a claimant responsible for the target disaster area, and the communication information may include information such as a mailbox address or a phone number.

In this embodiment, the claim analysis report may be sent to the communication terminal of the target claim settlement person according to the communication information.

Generating corresponding claim analysis reports based on claim values respectively corresponding to various crops; then obtaining communication information of target claims settlement personnel; and then, based on the communication information, sending the claim analysis report to the target claim settlement personnel. After the processing of generating the claim settlement values corresponding to the crops respectively based on the loss degree evaluation data of the crops is executed, the corresponding claim settlement analysis report is intelligently generated based on the claim settlement values corresponding to the crops respectively, and then the claim settlement analysis report is sent to the target claim settlement personnel, so that relevant business users can quickly and conveniently check and understand the claim settlement values corresponding to the crops respectively contained in farmland image data associated with the target disaster area contained in the claim settlement analysis report, further the target claim settlement personnel can be assisted to quickly complete the processing of planting insurance claim settlement requests, and the improvement of the claim settlement processing efficiency of the target claim settlement personnel and the use experience of the target claim settlement personnel are facilitated.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It is emphasized that the claims values may also be stored in nodes of a blockchain in order to further ensure privacy and security of the claims values.

The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based data evaluation apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus is particularly applicable to various electronic devices.

As shown in fig. 3, the artificial intelligence based data evaluating apparatus 300 according to the present embodiment includes: a receiving module 301, a preprocessing module 302, an extracting module 303, an analyzing module 304 and a first generating module 305. Wherein:

the receiving module 301 is configured to receive farmland image data collected by an unmanned aerial vehicle and corresponding to a target disaster area after obtaining a planting risk claim request corresponding to the target disaster area;

the preprocessing module 302 is configured to perform image preprocessing on the farmland image data to obtain corresponding target image data;

the extracting module 303 is configured to perform feature extraction on the target image data to obtain corresponding crop feature data;

the analysis module 304 is configured to invoke a preset loss analysis model to perform loss evaluation analysis on the crop feature data, so as to obtain loss degree evaluation data corresponding to each crop contained in the target image data;

A first generation module 305, configured to generate claim values corresponding to the respective crops based on the loss degree evaluation data of the respective crops.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based data evaluation method in the foregoing embodiment one by one, and are not described herein again.

In some alternative implementations of the present embodiment, the preprocessing module 302 includes:

the first processing sub-module is used for carrying out image correction on the farmland image data to obtain corresponding first image data;

the second processing sub-module is used for carrying out noise filtering on the first image data to obtain corresponding second image data;

the third processing sub-module is used for carrying out image enhancement on the second image data to obtain corresponding third image data;

and the determining submodule is used for taking the third image data as the target image data.

In some alternative implementations of the present embodiment, the extracting module 303 includes:

The first extraction submodule is used for carrying out feature extraction processing on the target image data with respect to the morphology of crops to obtain corresponding morphological features;

the second extraction sub-module is used for carrying out feature extraction processing on the target image data with respect to the colors of crops to obtain corresponding color features;

the third extraction sub-module is used for carrying out feature extraction processing on crop textures on the target image data to obtain corresponding texture features;

a first generation sub-module for generating the crop feature data based on the morphological feature, the color feature, and the texture feature.

In some optional implementations of the present embodiment, the artificial intelligence based data evaluating apparatus further includes:

the first acquisition module is used for acquiring historical farmland disaster-affected image data acquired in advance;

the construction module is used for constructing sample data based on the historical farmland disaster-affected image data;

the calling module is used for calling a preset initial neural network model;

And the second generation module is used for carrying out model training and adjustment processing on the initial neural network model based on the sample data to obtain the loss analysis model meeting the preset model construction requirement.

In some alternative implementations of the present embodiment, the first generation module 305:

the first acquisition submodule is used for acquiring crop types corresponding to various crops respectively;

the second acquisition submodule is used for acquiring loss assessment calculation formulas corresponding to the crop types respectively;

and the second generation sub-module is used for carrying out corresponding calculation processing on the loss degree evaluation data of the various crops based on the loss assessment calculation formula and generating claim settlement values respectively corresponding to the various crops.

The second acquisition module is used for acquiring a preset visual processing strategy;

the processing module is used for processing loss degree evaluation data corresponding to each crop on the basis of the visual processing strategy to obtain processed display data;

and the display module is used for displaying the display data.

the third generation module is used for generating corresponding claim analysis reports based on the claim values corresponding to the various crops respectively;

the third acquisition module is used for acquiring communication information of the target claimant;

and the sending module is used for sending the claim analysis report to the target claim settlement personnel based on the communication information.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application SpecificIntegrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an artificial intelligence based data evaluation method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the artificial intelligence based data evaluation method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, after a planting risk claim request corresponding to a target disaster area is acquired, farmland image data which is acquired by an unmanned aerial vehicle and corresponds to the target disaster area is received first; then carrying out image preprocessing on the farmland image data to obtain corresponding target image data; then, extracting features of the target image data to obtain corresponding crop feature data; subsequently, a preset loss analysis model is called to carry out loss evaluation analysis on the crop characteristic data, so as to obtain loss degree evaluation data respectively corresponding to various crops contained in the target image data; and finally, generating the claim settlement values respectively corresponding to the various crops based on the loss degree evaluation data of the various crops. According to the method and the device, the farmland image data corresponding to the target disaster area can be rapidly and accurately acquired based on the unmanned aerial vehicle, after the farmland image data is subjected to image preprocessing and feature extraction to obtain the crop feature data, the crop feature data is subjected to loss evaluation analysis processing through the use of the loss analysis model, loss degree evaluation data respectively corresponding to various crops contained in the target image data can be rapidly and accurately determined, loss degree evaluation data of the crops can be further obtained, claim settlement values respectively corresponding to the crops can be rapidly generated, the damage settlement processing efficiency of planting risks can be effectively improved, the cost of damage settlement processing is reduced, and the error of human evaluation is reduced. The accuracy of the generated claim settlement value is effectively ensured.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of an artificial intelligence-based data evaluation method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.

Claims

1. A data evaluation method based on artificial intelligence, comprising the steps of:

2. The artificial intelligence based data evaluation method according to claim 1, wherein the step of performing image preprocessing on the farmland image data to obtain corresponding target image data specifically comprises:

Performing image enhancement on the second image data to obtain corresponding third image data;

and taking the third image data as the target image data.

3. The artificial intelligence based data evaluation method according to claim 1, wherein the step of extracting features from the target image data to obtain corresponding crop feature data specifically comprises:

4. The artificial intelligence based data evaluation method according to claim 1, further comprising, before the step of calling a predetermined loss analysis model to perform loss evaluation analysis on the crop feature data to obtain loss degree evaluation data respectively corresponding to the respective crops included in the target image data:

Acquiring pre-acquired historical farmland disaster-affected image data;

constructing sample data based on the historical farmland disaster image data;

calling a preset initial neural network model;

5. The artificial intelligence based data evaluation method according to claim 1, wherein the step of generating a claim value corresponding to each of the crops based on the loss degree evaluation data of each of the crops, specifically comprises:

obtaining crop types respectively corresponding to the various crops;

6. The artificial intelligence based data evaluation method according to claim 1, further comprising, after the step of calling a predetermined loss analysis model to perform loss evaluation analysis on the crop feature data to obtain loss degree evaluation data respectively corresponding to the respective crops included in the target image data:

Acquiring a preset visual processing strategy;

and carrying out display processing on the display data.

7. The artificial intelligence based data evaluation method according to claim 1, further comprising, after the step of generating the claim values corresponding to the respective crops based on the loss degree evaluation data of the respective crops:

acquiring communication information of target claims settlement personnel;

8. An artificial intelligence based data assessment device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based data assessment method according to any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based data assessment method according to any of claims 1 to 7.