Risk assessment method and device based on artificial intelligence, equipment and storage medium

Abstract

The invention belongs to the field of artificial intelligence and the field of financial science and technology, and relates to an artificial intelligence-based risk assessment method, which comprises the steps of obtaining target meteorological data and image data of crops in a planting area; analyzing and processing the target meteorological data and the image data based on a crop growth model to generate corresponding growth state data; evaluating the growth state data based on a risk evaluation algorithm to generate a corresponding risk evaluation result; generating early warning information corresponding to the risk assessment result; and pushing the early warning information to the corresponding target personnel. The invention further provides a risk assessment device based on artificial intelligence, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the risk assessment result can be stored in a block chain. The method can be applied to a crop risk assessment scene in the financial field, and based on the use of a crop growth model and a risk assessment algorithm, the processing efficiency and processing precision of crop risk assessment are effectively improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence development technology and financial technology, and in particular to artificial intelligence-based risk assessment methods, devices, computer equipment and storage media. Background Art

[0002] In agricultural production, crops are greatly affected by weather factors, and adverse weather conditions may lead to losses and reduced production. Therefore, farmers and agricultural professionals need to be warned of potential risks of crops in advance so that appropriate measures can be taken to reduce losses. However, the existing method of assessing the potential risks of crops mainly relies on manual surveys of crops and manual risk assessment. This method takes a long time to collect and analyze data, resulting in low processing efficiency and the inability to guarantee the accuracy of the generated risk assessment results. Summary of the invention

[0003] The purpose of the embodiments of the present application is to propose a risk assessment method, device, computer equipment and storage medium based on artificial intelligence to solve the existing method of assessing the potential risks of crops, which mainly relies on manual surveys of crops and manual risk assessments. This processing method requires a long time for data collection and analysis, resulting in low processing efficiency and the technical problem that the accuracy of the generated risk assessment results cannot be guaranteed.

[0004] In order to solve the above technical problems, the embodiment of the present application provides a risk assessment method based on artificial intelligence, which adopts the following technical solutions:

[0005] Obtain target meteorological data within a preset planting area;

[0006] Acquiring image data of crops in the planting area;

[0007] Analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop;

[0008] Performing evaluation and processing on the growth status data based on a preset risk evaluation algorithm to generate a risk evaluation result corresponding to the crop;

[0009] Generating early warning information corresponding to the risk assessment result;

[0010] The warning information is pushed to corresponding target personnel.

[0011] Furthermore, the step of obtaining target meteorological data in a preset planting area specifically includes:

[0012] Acquiring meteorological data corresponding to the planting area from a plurality of preset data sources;

[0013] Get the preset specified data type;

[0014] Filtering out designated data corresponding to the designated data type from the meteorological data;

[0015] The designated data is used as the target meteorological data.

[0016] Furthermore, before the step of analyzing and processing the target meteorological data and the image data based on the preset crop growth model to generate growth status data corresponding to the crop, the method further includes:

[0017] Obtain pre-collected meteorological datasets and crop image datasets;

[0018] Constructing a sample data set based on the meteorological data set and the crop image data set;

[0019] Call the preset machine learning model;

[0020] Training and testing the machine learning model based on the sample data set to obtain a specified model that meets preset model building conditions;

[0021] The designated model is used as the crop growth model.

[0022] Furthermore, the number of the machine learning models includes multiple ones; the step of training and testing the machine learning model based on the sample data set to obtain a specified model that meets the preset model building conditions specifically includes:

[0023] Dividing the sample data set into a training set and a test set;

[0024] Using the training set to train each of the machine learning models, respectively, to obtain a plurality of trained initial models;

[0025] Performing test processing on each of the initial models using the test set to obtain test data corresponding to each of the initial models;

[0026] Based on the test data, a target model is selected from all the initial models;

[0027] The target model is used as the designated model.

[0028] Furthermore, the step of selecting a target model from all the initial models based on the test data specifically includes:

[0029] Based on the test data, each of the initial models is optimized by a preset processing strategy to obtain an optimized first model;

[0030] Obtaining model effects of each of the first models;

[0031] Selecting a second model with the highest model effect from all the first models;

[0032] The second model is used as the target model.

[0033] Furthermore, the step of evaluating and processing the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop specifically includes:

[0034] Obtaining historical growth data corresponding to the crop;

[0035] Performing data analysis on the growth status data and the historical growth data based on the risk assessment algorithm to generate a risk level corresponding to the crop;

[0036] The risk level is used as the risk assessment result.

[0037] Furthermore, the step of pushing the warning information to the corresponding target personnel specifically includes:

[0038] Obtaining a designated personnel type corresponding to the target personnel;

[0039] Determining a designated communication method corresponding to the designated person type;

[0040] Based on the designated communication method, the warning information is pushed to the target person.

[0041] In order to solve the above technical problems, the embodiment of the present application also provides a risk assessment device based on artificial intelligence, which adopts the following technical solution:

[0042] A first acquisition module is used to acquire target meteorological data in a preset planting area;

[0043] A second acquisition module is used to acquire image data of crops in the planting area;

[0044] A first processing module, configured to analyze and process the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop;

[0045] A second processing module, configured to evaluate and process the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop;

[0046] A generation module, used to generate warning information corresponding to the risk assessment result;

[0047] The push module is used to push the warning information to the corresponding target personnel.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0049] Obtain target meteorological data within a preset planting area;

[0050] Acquiring image data of crops in the planting area;

[0051] Analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop;

[0052] Performing evaluation and processing on the growth status data based on a preset risk evaluation algorithm to generate a risk evaluation result corresponding to the crop;

[0053] Generating early warning information corresponding to the risk assessment result;

[0054] The warning information is pushed to corresponding target personnel.

[0055] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0056] Obtain target meteorological data within a preset planting area;

[0057] Acquiring image data of crops in the planting area;

[0058] Analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop;

[0059] Performing evaluation and processing on the growth status data based on a preset risk evaluation algorithm to generate a risk evaluation result corresponding to the crop;

[0060] Generating early warning information corresponding to the risk assessment result;

[0061] The warning information is pushed to corresponding target personnel.

[0062] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0063] The embodiment of the present application first obtains the target meteorological data in a preset planting area; then obtains the image data of the crops in the planting area; then analyzes and processes the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crops; subsequently evaluates and processes the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crops; finally generates warning information corresponding to the risk assessment result, and pushes the warning information to the corresponding target personnel. The embodiment of the present application obtains the target meteorological data in the planting area and the image data of the crops in the planting area, and then analyzes and processes the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crops, and evaluates and processes the growth status data based on a risk assessment algorithm to generate a risk assessment result corresponding to the crops, and subsequently generates warning information corresponding to the risk assessment result and pushes it to the corresponding target personnel. The present application processes the acquired target meteorological data and the image data based on the use of crop growth models and risk assessment algorithms, automatically realizes risk assessment processing for crops in the planting area, greatly reduces the processing workload of risk assessment, effectively improves the processing efficiency and processing accuracy of risk assessment for crops, and ensures the accuracy of the generated risk assessment results for crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0066] Figure 2 A flowchart of an embodiment of an artificial intelligence-based risk assessment method according to the present application;

[0067] Figure 3 is a schematic structural diagram of an embodiment of an artificial intelligence-based risk assessment device according to the present application;

[0068] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0070] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0071] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0072] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for 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 optical fiber cables, etc.

[0073] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0074] The terminal devices 101, 102, and 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, and the like.

[0075] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0076] It should be noted that the artificial intelligence-based risk assessment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the artificial intelligence-based risk assessment device is generally set in the server / terminal device.

[0077] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0078] Continue to refer Figure 2 , showing a flow chart of an embodiment of an artificial intelligence-based risk assessment method according to the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted. The artificial intelligence-based risk assessment method provided in the embodiment of the present application can be applied to any scenario where crop risk assessment is required, and the artificial intelligence-based risk assessment method can be applied to products in these scenarios, for example, crop risk assessment in the financial insurance field. The artificial intelligence-based risk assessment method comprises the following steps:

[0079] Step S201, obtaining target meteorological data in a preset planting area.

[0080] In this embodiment, the electronic device (e.g. Figure 1The server / terminal device shown in the figure) can obtain the target meteorological data in the planting area through a wired connection or a wireless connection. It should be pointed out that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, Wi MAX connection, Zigbee connection, UWB (Ultra Wi-Fi Band) connection, and other wireless connection methods currently known or to be developed in the future. The above-mentioned planting area is the planting area where the crops to be monitored are located. Among them, the specific implementation process of obtaining the target meteorological data in the preset planting area will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0081] Step S202, obtaining image data of crops in the planting area.

[0082] In this embodiment, image data of crops in the planting area may be collected by using remote sensing technology or drone technology.

[0083] Step S203: analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop.

[0084] In this embodiment, the target meteorological data and the image data can be input into the crop growth model, and the target meteorological data and the image data can be risk assessed by the crop growth model, and the growth status data corresponding to the crop can be output. The model construction process of the above-mentioned crop growth model will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0085] Step S204: evaluating and processing the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop.

[0086] In this embodiment, the specific implementation process of evaluating and processing the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0087] Step S205: Generate warning information corresponding to the risk assessment result.

[0088] In this embodiment, the risk assessment result can be filled into the corresponding position in the preset warning information template to generate warning information corresponding to the risk assessment result. The content of the warning information template can be written and generated according to the actual business needs of crop risk reminder.

[0089] Step S206: Push the warning information to the corresponding target personnel.

[0090] In this embodiment, the specific implementation process of pushing the warning information to the corresponding target personnel will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated in detail here.

[0091] The present application first obtains the target meteorological data in a preset planting area; then obtains the image data of the crops in the planting area; then analyzes and processes the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crops; subsequently evaluates and processes the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crops; finally generates warning information corresponding to the risk assessment result, and pushes the warning information to the corresponding target personnel. The present application obtains the target meteorological data in the planting area and the image data of the crops in the planting area, and then analyzes and processes the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crops, and evaluates and processes the growth status data based on a risk assessment algorithm to generate a risk assessment result corresponding to the crops, and subsequently generates warning information corresponding to the risk assessment result and pushes it to the corresponding target personnel. The present application processes the acquired target meteorological data and the image data based on the use of crop growth models and risk assessment algorithms, automatically realizes risk assessment processing for crops in the planting area, greatly reduces the processing workload of risk assessment, effectively improves the processing efficiency and processing accuracy of risk assessment for crops, and ensures the accuracy of the generated risk assessment results for crops.

[0092] In some optional implementations, step S201 includes the following steps:

[0093] Acquire meteorological data corresponding to the planting area from a plurality of preset data sources.

[0094] In this embodiment, the above data sources may at least include meteorological observation stations, satellites and other meteorological data sources.

[0095] Gets the specified data type of a preset.

[0096] In this embodiment, the above-mentioned specified data types may include data types such as temperature, humidity, precipitation, wind speed, etc.

[0097] Specified data corresponding to the specified data type is filtered out from the meteorological data.

[0098] In this embodiment, based on the specified data type, data such as temperature, humidity, precipitation, wind speed, etc. in the planting area corresponding to the specified data type can be filtered out from the meteorological data.

[0099] The designated data is used as the target meteorological data.

[0100] The present application obtains meteorological data corresponding to the planting area from a plurality of preset data sources; then obtains a preset designated data type; then filters out designated data corresponding to the designated data type from the meteorological data; and subsequently uses the designated data as the target meteorological data. The present application obtains meteorological data corresponding to the planting area from a plurality of data sources, and then filters out designated data corresponding to the preset designated data type from the meteorological data and uses the designated data as the target meteorological data, thereby effectively achieving rapid acquisition of the target meteorological data and ensuring data accuracy and data diversity of the obtained target meteorological data.

[0101] In some optional implementations of this embodiment, before the step of analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop, the electronic device may further perform the following steps:

[0102] Obtain pre-collected meteorological datasets and crop image datasets.

[0103] In this embodiment, the meteorological data set and the crop image data set may be initial data sets collected in advance within a preset time period and required for constructing a crop growth model. There is no limitation on the value of the preset time period, which can be determined according to actual business usage requirements, for example, it can be set to within two years before the current time.

[0104] A sample data set is constructed based on the meteorological data set and the crop image data set.

[0105] In this embodiment, the meteorological data set can be processed and analyzed by using data mining to extract relevant data related to the growth status analysis of crops, and then a corresponding sample data set can be constructed based on the relevant data and the crop image data set.

[0106] Call the preset machine learning model.

[0107] In this embodiment, there is no specific limitation on the model selection of the above-mentioned machine learning model, which can be determined according to the actual needs of crop growth status analysis, for example, a learning model can be used.

[0108] The machine learning model is trained and tested based on the sample data set to obtain a specified model that meets the preset model building conditions.

[0109] In this embodiment, the designated model can be a mathematical model pre-constructed based on existing crop physiological knowledge and experimental data, taking into account factors such as crop types, growth stages, and environmental conditions, and used to predict crop growth status data based on input meteorological data and crop growth stage data. The specific implementation process of training and testing the machine learning model based on the sample data set to obtain a designated model that meets the preset model construction conditions will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0110] The designated model is used as the crop growth model.

[0111] The present application obtains a pre-collected meteorological data set and a crop image data set; then constructs a sample data set based on the meteorological data set and the crop image data set; then calls a preset machine learning model; subsequently trains and tests the machine learning model based on the sample data set to obtain a specified model that meets the preset model building conditions; finally, the specified model is used as the crop growth model. The present application constructs a sample data set based on a pre-collected meteorological data set and a crop image data set; then trains and tests the machine learning model based on the sample data set to obtain a crop growth model that meets the preset model building conditions, thereby completing the model building process of the crop growth model, effectively ensuring the model effect and processing accuracy of the generated crop growth model, and improving the construction efficiency of the crop growth model.

[0112] In some optional implementations, the number of the machine learning models includes multiple; the training and testing of the machine learning model based on the sample data set to obtain a specified model that meets the preset model building conditions includes the following steps:

[0113] The sample data set is divided into a training set and a test set.

[0114] In this embodiment, the sample data set can be divided into a training set and a test set according to a preset division value. There is no specific limitation on the numerical value of the division value, which can be set according to actual business usage requirements, for example, 7:3.

[0115] The training set is used to train each of the machine learning models to obtain multiple trained initial models.

[0116] In this embodiment, the training set is used to train each of the machine learning models respectively, so that the machine learning model learns the relationship between meteorological data, crop growth stage data, and crop growth status data, thereby obtaining multiple initial models after training.

[0117] The initial models are tested respectively using the test set to obtain test data corresponding to the initial models.

[0118] In this embodiment, each of the initial models is tested using the test set to obtain test performance data of each of the initial models on the test set, namely the above-mentioned test data.

[0119] Based on the test data, a target model is screened out from all the initial models.

[0120] In this embodiment, the specific implementation process of selecting the target model from all the initial models based on the test data will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0121] The target model is used as the designated model.

[0122] The present application divides the sample data set into a training set and a test set; then uses the training set to train each of the machine learning models to obtain multiple initial models after training; then uses the test set to test each of the initial models to obtain test data corresponding to each of the initial models; subsequently, based on the test data, a target model is screened out from all of the initial models, and the target model is used as the designated model. The present application divides the sample data set into a training set and a test set, then uses the training set and the test set to train and test each of the initial models, and according to the test data corresponding to each of the initial models, a designated model that is subsequently used as a crop growth model is screened out from all of the initial models, thereby completing the model construction process of the crop growth model, and effectively ensuring the model effect and processing accuracy of the obtained crop growth model.

[0123] In some optional implementations, the step of selecting a target model from all the initial models based on the test data comprises the following steps:

[0124] Based on the test data, each of the initial models is optimized through a preset processing strategy to obtain an optimized first model.

[0125] In this embodiment, the above processing strategy may specifically adopt a cross-validation strategy, and the parameters of each of the initial models may be adjusted and optimized by using the cross-validation strategy, thereby obtaining an optimized first model with optimal parameter settings.

[0126] Obtain model effects of each of the first models.

[0127] In this embodiment, the above-mentioned model effect may specifically refer to the fitting effect of each of the first models.

[0128] A second model with the highest model effect is selected from all the first models.

[0129] In this embodiment, the second model with the highest model effect refers to the second model with the best fitting effect among all the first models.

[0130] The second model is used as the target model.

[0131] The present application optimizes each of the initial models based on the test data through a preset processing strategy to obtain an optimized first model; then obtains the model effect of each of the first models; then selects the second model with the highest model effect from all the first models; and subsequently uses the second model as the target model. The present application optimizes each of the initial models based on the test data through a preset processing strategy to obtain an optimized first model; then selects the second model with the highest model effect from all the first models as the final target model. Since the obtained target model has the best model effect, the accuracy of the generated target model is guaranteed, which is conducive to the subsequent use of the target model to effectively improve the accuracy of the assessment of the production status of crops.

[0132] In some optional implementations of this embodiment, step S204 includes the following steps:

[0133] Obtain historical growth data corresponding to the crop.

[0134] In this embodiment, the historical growth data corresponding to the crop can be queried from the crop database by querying the pre-built crop database, wherein the crop database is a pre-built database storing record data of growth data of multiple crops.

[0135] The growth status data and the historical growth data are analyzed based on the risk assessment algorithm to generate a risk level corresponding to the crop.

[0136] In this embodiment, the risk assessment algorithm evaluates the risk level of the current crops based on the growth status data and the historical growth data output by the crop growth model. It takes into account various possible risk factors, such as drought, pests and diseases, high temperature, etc., and outputs corresponding risk levels based on the severity of these factors and the sensitivity of the crops.

[0137] The risk level is used as the risk assessment result.

[0138] The present application obtains the historical growth data corresponding to the crop; then performs data analysis on the growth status data and the historical growth data based on the risk assessment algorithm to generate a risk level corresponding to the crop; and subsequently uses the risk level as the risk assessment result. The present application uses a risk assessment algorithm to perform data analysis on the growth status data output by the crop growth model and the acquired historical growth data of the crop, so as to achieve a quick and accurate prediction of the potential risks of the crop based on the growth status and historical growth data of the crop, thereby improving the processing efficiency of the risk assessment of the crop and ensuring the accuracy of the generated risk assessment results.

[0139] In some optional implementations of this embodiment, step S206 includes the following steps:

[0140] Get the designated person type corresponding to the target person.

[0141] In this embodiment, the target personnel may include farmers or agricultural professionals, and thus the designated personnel types may include types of farmers or agricultural professionals.

[0142] A designated communication method corresponding to the designated person type is determined.

[0143] In this embodiment, for different types of personnel, communication methods corresponding to the personnel types can be pre-set according to actual communication needs. The communication methods may include mobile phone applications, text messages, emails, and the like.

[0144] Based on the designated communication method, the warning information is pushed to the target person.

[0145] In this embodiment, the warning information can be pushed to the communication terminal corresponding to the target person by using the designated communication method. After receiving the warning information, the target person can make corresponding decisions according to the needs and potential risks of crops, such as adjusting irrigation plans, fertilization plans or pest control measures to reasonably allocate resources, thereby avoiding waste and unnecessary cost expenditures.

[0146] This application obtains the designated personnel type corresponding to the target personnel; then determines the designated communication method corresponding to the designated personnel type; and subsequently pushes the warning information to the target personnel based on the designated communication method. After generating the warning information corresponding to the risk assessment result, this application will also intelligently determine the designated communication method corresponding to the personnel type of the target personnel, and then push the warning information to the target personnel based on the designated communication method, so as to realize the intelligent and standardized push of the warning information for the target personnel, so that the target personnel can timely understand the potential risks of the current crops by consulting the warning information, and then take corresponding countermeasures to deal with the crops in time, which is conducive to improving the work experience and work efficiency of the target personnel, and reducing losses and increasing production.

[0147] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0148] It should be emphasized that in order to further ensure the privacy and security of the above risk assessment results, the above risk assessment results can also be stored in a blockchain node.

[0149] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0150] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0151] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0152] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0153] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0154] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a risk assessment device based on artificial intelligence. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0155] like Figure 3 As shown, the risk assessment device 300 based on artificial intelligence described in this embodiment includes: a first acquisition module 301, a second acquisition module 302, a first processing module 303, a second processing module 304, a generation module 305 and a push module 306. Among them:

[0156] The first acquisition module 301 is used to acquire target meteorological data in a preset planting area;

[0157] The second acquisition module 302 is used to acquire image data of crops in the planting area;

[0158] The first processing module 303 is used to analyze and process the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop;

[0159] A second processing module 304 is used to evaluate and process the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop;

[0160] A generating module 305, used to generate warning information corresponding to the risk assessment result;

[0161] The push module 306 is used to push the warning information to the corresponding target personnel.

[0162] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0163] In some optional implementations of this embodiment, the first acquisition module 301 includes:

[0164] A first acquisition submodule is used to acquire meteorological data corresponding to the planting area from a plurality of preset data sources;

[0165] The second acquisition submodule is used to acquire a preset specified data type;

[0166] A first screening submodule, used for screening out designated data corresponding to the designated data type from the meteorological data;

[0167] The first determination submodule is used to use the designated data as the target meteorological data.

[0168] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0169] In some optional implementations of this embodiment, the risk assessment device based on artificial intelligence also includes:

[0170] The third acquisition module is used to acquire the pre-collected meteorological data set and crop image data set;

[0171] A construction module, used to construct a sample data set based on the meteorological data set and the crop image data set;

[0172] The calling module is used to call the preset machine learning model;

[0173] A third processing module is used to train and test the machine learning model based on the sample data set to obtain a specified model that meets the preset model building conditions;

[0174] A determination module is used to use the specified model as the crop growth model.

[0175] In this embodiment, the operations performed by the above modules or units respectively correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0176] In some optional implementations of this embodiment, the number of the machine learning models includes multiple; the third processing module includes:

[0177] A division submodule, used for dividing the sample data set into a training set and a test set;

[0178] A training submodule, used to use the training set to train each of the machine learning models to obtain a plurality of trained initial models;

[0179] A testing submodule, used to perform testing on each of the initial models using the test set to obtain test data corresponding to each of the initial models;

[0180] A second screening submodule, configured to screen out a target model from all the initial models based on the test data;

[0181] The second determination submodule is used to use the target model as the designated model.

[0182] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0183] In some optional implementations of this embodiment, the second screening submodule includes:

[0184] A processing unit, configured to optimize each of the initial models based on the test data by a preset processing strategy to obtain an optimized first model;

[0185] An acquisition unit, used for acquiring a model effect of each of the first models;

[0186] A screening unit, used for screening out a second model with the highest model effect from all the first models;

[0187] A determination unit is used to use the second model as the target model.

[0188] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0189] In some optional implementations of this embodiment, the second processing module 304 includes:

[0190] A third acquisition submodule is used to acquire historical growth data corresponding to the crop;

[0191] An analysis submodule, configured to perform data analysis on the growth status data and the historical growth data based on the risk assessment algorithm to generate a risk level corresponding to the crop;

[0192] The third determination submodule is used to use the risk level as the risk assessment result.

[0193] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0194] In some optional implementations of this embodiment, the push module 306 includes:

[0195] A fourth acquisition submodule is used to acquire a designated personnel type corresponding to the target personnel;

[0196] A fourth determination submodule is used to determine a designated communication method corresponding to the designated person type;

[0197] The push submodule is used to push the warning information to the target person based on the specified communication method.

[0198] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based risk assessment method in the aforementioned implementation mode, and will not be repeated here.

[0199] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0200] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital processors (DSPs), embedded devices, etc.

[0201] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0202] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, 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, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of a risk assessment method based on artificial intelligence, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0203] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the artificial intelligence-based risk assessment method.

[0204] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0205] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0206] In an embodiment of the present application, first, target meteorological data in a preset planting area is obtained; then, image data of crops in the planting area is obtained; then, the target meteorological data and the image data are analyzed and processed based on a preset crop growth model to generate growth status data corresponding to the crops; subsequently, the growth status data is evaluated and processed based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crops; finally, warning information corresponding to the risk assessment result is generated, and the warning information is pushed to the corresponding target personnel. In an embodiment of the present application, target meteorological data in a planting area and image data of crops in a planting area are obtained, and then, based on a preset crop growth model, the target meteorological data and the image data are analyzed and processed to generate growth status data corresponding to the crops, and the growth status data is evaluated and processed based on a risk assessment algorithm to generate a risk assessment result corresponding to the crops, and subsequently, warning information corresponding to the risk assessment result is generated and pushed to the corresponding target personnel. The present application processes the acquired target meteorological data and the image data based on the use of crop growth models and risk assessment algorithms, automatically realizes risk assessment processing for crops in the planting area, greatly reduces the processing workload of risk assessment, effectively improves the processing efficiency and processing accuracy of risk assessment for crops, and ensures the accuracy of the generated risk assessment results for crops.

[0207] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned artificial intelligence-based risk assessment method.

[0208] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0209] In an embodiment of the present application, first, target meteorological data in a preset planting area is obtained; then, image data of crops in the planting area is obtained; then, the target meteorological data and the image data are analyzed and processed based on a preset crop growth model to generate growth status data corresponding to the crops; subsequently, the growth status data is evaluated and processed based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crops; finally, warning information corresponding to the risk assessment result is generated, and the warning information is pushed to the corresponding target personnel. In an embodiment of the present application, target meteorological data in a planting area and image data of crops in a planting area are obtained, and then, based on a preset crop growth model, the target meteorological data and the image data are analyzed and processed to generate growth status data corresponding to the crops, and the growth status data is evaluated and processed based on a risk assessment algorithm to generate a risk assessment result corresponding to the crops, and subsequently, warning information corresponding to the risk assessment result is generated and pushed to the corresponding target personnel. The present application processes the acquired target meteorological data and the image data based on the use of crop growth models and risk assessment algorithms, automatically realizes risk assessment processing for crops in the planting area, greatly reduces the processing workload of risk assessment, effectively improves the processing efficiency and processing accuracy of risk assessment for crops, and ensures the accuracy of the generated risk assessment results for crops.

[0210] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0211] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A risk assessment method based on artificial intelligence, characterized in that: The steps include: Obtain target meteorological data within a preset planting area; Acquiring image data of crops in the planting area; Analyzing and processing the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop; Performing evaluation and processing on the growth status data based on a preset risk evaluation algorithm to generate a risk evaluation result corresponding to the crop; Generating early warning information corresponding to the risk assessment result; The warning information is pushed to corresponding target personnel.

2. The artificial intelligence-based risk assessment method according to claim 1, characterized in that: The step of obtaining target meteorological data in a preset planting area specifically includes: Acquiring meteorological data corresponding to the planting area from a plurality of preset data sources; Get the preset specified data type; Filtering out designated data corresponding to the designated data type from the meteorological data; The designated data is used as the target meteorological data.

3. The artificial intelligence-based risk assessment method according to claim 1, characterized in that: Before the step of analyzing and processing the target meteorological data and the image data based on the preset crop growth model to generate growth status data corresponding to the crop, the method further includes: Obtain pre-collected meteorological datasets and crop image datasets; Constructing a sample data set based on the meteorological data set and the crop image data set; Call the preset machine learning model; Training and testing the machine learning model based on the sample data set to obtain a specified model that meets preset model building conditions; The designated model is used as the crop growth model.

4. The artificial intelligence-based risk assessment method according to claim 3, characterized in that: The number of the machine learning models includes multiple ones; the step of training and testing the machine learning model based on the sample data set to obtain a specified model that meets the preset model building conditions specifically includes: Dividing the sample data set into a training set and a test set; Using the training set to train each of the machine learning models, respectively, to obtain a plurality of trained initial models; Performing test processing on each of the initial models using the test set to obtain test data corresponding to each of the initial models; Based on the test data, a target model is selected from all the initial models; The target model is used as the designated model.

5. The artificial intelligence-based risk assessment method according to claim 4, characterized in that: The step of selecting a target model from all the initial models based on the test data specifically includes: Based on the test data, each of the initial models is optimized by a preset processing strategy to obtain an optimized first model; Obtaining model effects of each of the first models; Selecting a second model with the highest model effect from all the first models; The second model is used as the target model.

6. The artificial intelligence-based risk assessment method according to claim 1, characterized in that: The step of evaluating and processing the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop specifically includes: Obtaining historical growth data corresponding to the crop; Performing data analysis on the growth status data and the historical growth data based on the risk assessment algorithm to generate a risk level corresponding to the crop; The risk level is used as the risk assessment result.

7. The artificial intelligence-based risk assessment method according to claim 1, characterized in that: The step of pushing the warning information to the corresponding target personnel specifically includes: Obtaining a designated personnel type corresponding to the target personnel; Determining a designated communication method corresponding to the designated person type; Based on the designated communication method, the warning information is pushed to the target person.

8. A risk assessment device based on artificial intelligence, characterized in that: include: A first acquisition module is used to acquire target meteorological data in a preset planting area; A second acquisition module is used to acquire image data of crops in the planting area; A first processing module, configured to analyze and process the target meteorological data and the image data based on a preset crop growth model to generate growth status data corresponding to the crop; A second processing module, configured to evaluate and process the growth status data based on a preset risk assessment algorithm to generate a risk assessment result corresponding to the crop; A generation module, used to generate warning information corresponding to the risk assessment result; The push module is used to push the warning information to the corresponding target personnel.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the risk assessment method based on artificial intelligence as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the artificial intelligence-based risk assessment method according to any one of claims 1 to 7.

Images