Tree-based risk assessment method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a tree-based risk assessment method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring tree growth data and growth environment data of a target tree; detecting tree attribute information based on the tree structure sub-data and the tree form sub-data; evaluating the tree structure subdata and the growth environment data based on a health risk evaluation model to obtain health risk evaluation data; evaluating the growth environment data and the tree attribute information based on an environment risk evaluation model to obtain environment risk evaluation data; evaluating the growth environment data, the tree structure sub-data and the tree attribute information based on a natural disaster evaluation model to obtain natural risk evaluation data; and performing risk type assessment based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data. According to the embodiment of the invention, the risk assessment efficiency and accuracy of trees can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a tree-based risk assessment method and device, electronic device, and storage medium. Background Art

[0002] Accurate risk assessment of trees is an important basis for tree insurance, and tree insurance means providing protection against risks such as natural disasters and accidents that important trees may suffer through insurance, so as to reduce the economic losses caused by disasters to tree owners. However, related technologies for tree risk assessment usually rely on manual observation and experience, and it is impossible to obtain complete data on trees. In addition, due to the lack of professionals in tree risk assessment, the efficiency and accuracy of tree risk assessment are low. Therefore, how to provide a method that can improve the efficiency and accuracy of tree risk assessment has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a tree-based risk assessment method and device, electronic device, and storage medium, which can improve the efficiency and accuracy of tree risk assessment.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a tree-based risk assessment method, the method comprising:

[0005] Acquire tree growth data and growth environment data of a target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, wherein the tree structure sub-data is used to indicate the multidimensional spatial structure of the target tree, and the tree morphology sub-data is used to indicate the current biological morphology of the target tree;

[0006] Perform tree attribute detection on the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information;

[0007] Performing health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data;

[0008] Performing growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data;

[0009] Based on a preset natural disaster assessment model, natural risk assessment processing is performed on the growth environment data, the tree structure sub-data and the tree attribute information to obtain natural risk assessment data;

[0010] The risk type of the target tree is assessed based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data.

[0011] In some embodiments, the tree attribute information includes a target tree species type and a target tree age, and the tree attribute detection of the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain the tree attribute information includes:

[0012] Performing tree species detection on the tree morphology sub-data based on a preset tree species detection model to obtain the target tree species type;

[0013] Selecting a target tree age detection model from pre-trained candidate tree age detection models based on the target tree species type;

[0014] The tree age is detected on the tree structure sub-data based on the target tree age detection model to obtain the target tree age.

[0015] In some embodiments, the target tree age detection model includes a tree ring prediction sub-model and a correction sub-model, and performing tree age detection on the tree structure sub-data based on the target tree age detection model to obtain the target tree age includes:

[0016] Determining a growth restriction weight of the target tree based on the growth environment data;

[0017] Acquiring tree diameter information from the tree structure sub-data;

[0018] Performing annual ring prediction on the tree diameter based on the annual ring prediction sub-model to obtain the number of tree rings;

[0019] The growth restriction weight and the tree ring number are used to predict the tree age based on the correction sub-model to obtain the target tree age.

[0020] In some embodiments, the health risk assessment processing of the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data includes:

[0021] Extracting index data from the tree structure sub-data to obtain tree potential index data;

[0022] Extracting index data from the growth environment data to obtain growth environment index data;

[0023] Determining a first index weight of the tree vigor index data and a second index weight of the growth environment index data;

[0024] Performing weighted calculation based on the tree potential index data, the first index weight, the growth environment index data and the second index weight to obtain an index status score;

[0025] The health risk matching is performed on the indicator status score based on a preset health risk matching rule to obtain the health risk assessment data.

[0026] In some embodiments, the performing growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data includes:

[0027] Matching growth data of similar tree species from historical tree growth data based on the tree attribute information;

[0028] Obtaining environmental data and environmental risk data of similar tree species from the growth data of similar tree species;

[0029] Performing environmental data difference calculation on the environmental data of the same tree species and the growth environment data to obtain growth environment difference data;

[0030] A growth environment risk assessment is performed based on the growth environment difference data and the tree species environmental risk data to determine the environmental risk assessment data.

[0031] In some embodiments, the risk type assessment of the target tree based on the tree attribute information, the health risk assessment data, the environmental risk assessment data, and the natural risk assessment data includes:

[0032] Determine a weight parameter combination corresponding to the tree attribute information, the health risk assessment data, the environmental risk assessment data, and the natural risk assessment data;

[0033] Performing weighted calculation on the tree attributes, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data based on the weight parameter combination to obtain target risk assessment data;

[0034] A target risk type of the target tree is determined based on the target risk assessment data.

[0035] In some embodiments, the method further comprises:

[0036] Determine a risk claim assessment value for the target tree based on the target risk assessment data and the target risk type;

[0037] Obtaining a claim settlement request for the target tree, and obtaining tree damage data of the target tree based on the claim settlement request;

[0038] Performing damage degree detection on the tree damage data based on a preset tree damage detection model to determine a target damage degree of the target tree;

[0039] Determining risk damage matching data of the target tree based on the target risk assessment data and the target damage degree;

[0040] A target claim value is determined based on the risk-damage matching data and the risk claim assessment value.

[0041] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a tree-based risk assessment device, the device comprising:

[0042] an acquisition module, used for acquiring tree growth data and growth environment data of a target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, wherein the tree structure sub-data is used for indicating the multi-dimensional spatial structure of the target tree, and the tree morphology sub-data is used for indicating the current biological morphology of the target tree;

[0043] A detection module, configured to detect tree attributes of the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information;

[0044] A health risk assessment module, used for performing health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data;

[0045] An environmental risk assessment module, used to perform growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data;

[0046] A natural risk assessment module, used for performing natural risk assessment processing on the growth environment data, the tree structure sub-data and the tree attribute information based on a preset natural disaster assessment model to obtain natural risk assessment data;

[0047] A type assessment module is used to perform risk type assessment on the target tree based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data.

[0048] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0049] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0050] The tree-based risk assessment method and device, electronic device, and storage medium proposed in the embodiments of the present application obtain tree growth data and growth environment data of the target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, the tree structure sub-data is used to indicate the multidimensional spatial structure of the target tree, and the tree morphology sub-data is used to indicate the current biological morphology of the target tree; tree attribute detection is performed on the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information; health risk assessment is performed on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data; growth environment risk assessment is performed on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data; natural risk assessment is performed on the growth environment data, the tree structure sub-data, and the tree attribute information based on a preset natural disaster assessment model to obtain natural risk assessment data; risk type assessment is performed on the target tree based on the tree attribute information, health risk assessment data, environmental risk assessment data, and natural risk assessment data. Compared with the related technology, which has low efficiency and accuracy in tree risk assessment due to the lack of professionals in tree risk assessment, the present application can obtain more complete and multi-dimensional risk assessment data related to target trees by combining health risk assessment models, environmental risk assessment models and natural disaster risk assessment models. That is, by combining artificial intelligence models, automated risk assessment can be achieved, thereby improving the efficiency and accuracy of tree risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a first flow chart of a tree-based risk assessment method provided by an embodiment of the present application;

[0052] Figure 2 yes Figure 1 A flowchart of step S120 in FIG.

[0053] Figure 3 yes Figure 2 A flowchart of step S230 in FIG.

[0054] Figure 4 yes Figure 1 A flowchart of step S130 in FIG.

[0055] Figure 5 yes Figure 1 A flowchart of step S140 in FIG.

[0056] Figure 6 yes Figure 1 A flowchart of step S160 in FIG.

[0057] Figure 7 is a second flow chart of the tree-based risk assessment method provided in an embodiment of the present application;

[0058] Figure 8 is a structural schematic diagram of a tree-based risk assessment device provided in an embodiment of the present application;

[0059] Fig. 9 It is a hardware structure diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0063] First, some nouns involved in this application are analyzed:

[0064] Artificial Intelligence (AI): is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a 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.

[0065] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to using cameras and computers to replace human eyes to identify, track and measure targets, and further perform image processing to make computer processing into images that are more suitable for human observation or transmission to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image segmentation, image semantic understanding, image retrieval, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and mapping, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0066] Light Detection and Ranging (LiDAR): is a remote sensing technology that measures the distance, position and shape of a target object by emitting a laser beam and receiving the reflected light. The LiDAR system works mainly by emitting laser pulses and measuring the time difference between these pulses from emission to being reflected back to the receiver. Since laser pulses travel at the speed of light, by calculating the time difference, the system can accurately calculate the distance between the LiDAR and the target object.

[0067] Accurate risk assessment of trees is an important basis for tree insurance, and tree insurance refers to the use of insurance to provide protection for risks such as natural disasters and accidents that important trees (such as traditional ancient trees) may suffer, so as to reduce the economic losses of tree owners caused by disasters. However, related technologies for tree risk assessment usually rely on simple tools and techniques, manual observation and experience, which are easily affected by human factors and cannot understand the complete data of the trees, thus affecting the accuracy of the assessment. In addition, due to the lack of professionals in tree risk assessment, the efficiency and accuracy of tree risk assessment are low. Therefore, how to provide a method that can improve the efficiency and accuracy of tree risk assessment has become a technical problem that needs to be solved urgently.

[0068] Based on this, the embodiments of the present application provide a tree-based risk assessment method and device, an electronic device, and a storage medium, which can improve the efficiency and accuracy of tree risk assessment.

[0069] 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.

[0070] 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.

[0071] The tree-based risk assessment method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The tree-based risk assessment method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms; the software can be an application that implements a tree-based risk assessment method, etc., but is not limited to the above forms.

[0072] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network personal computers (PC), minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0073] See also Figure 1 , Figure 1 is an optional flow chart of a tree-based risk assessment method provided in an embodiment of the present application. In some embodiments of the present application, Figure 1 The method may specifically include but is not limited to steps S110 to S160. Figure 1 These six steps are introduced in detail.

[0074] Step S110, obtaining tree growth data and growth environment data of the target tree;

[0075] Step S120, performing tree attribute detection on the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information;

[0076] Step S130, performing health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data;

[0077] Step S140, performing growth environment risk assessment processing on the growth environment data and tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data;

[0078] Step S150, performing natural risk assessment processing on the growth environment data, tree structure sub-data and tree attribute information based on a preset natural disaster assessment model to obtain natural risk assessment data;

[0079] Step S160, performing risk type assessment on the target tree based on the tree attribute information, health risk assessment data, environmental risk assessment data and natural risk assessment data.

[0080] In steps S110 to S160 of some embodiments, the present application can perform accurate health detection and assessment of trees by adopting AI models based on machine intelligence and big data of environmental factors, thereby improving the efficiency and accuracy of risk assessment of trees.

[0081] In step S110 of some embodiments, the target tree refers to a tree that needs to be risk assessed. For example, in the insurance field of financial technology, the target tree may refer to an ancient tree that needs to be insured, or a plurality of fruit trees owned by the insured. Tree growth data refers to the growth data of the target tree before insurance is collected by non-destructive equipment such as high-definition photos, LiDAR, and drones. Tree growth data includes tree structure sub-data and tree morphology sub-data. The tree structure sub-data is used to indicate the multi-dimensional spatial structure of the target tree, for example, indicators such as tree height, tree diameter, dead branch ratio, tree top dieback rate, and tree inclination detected in LiDAR. In other words, the tree structure sub-data can be calculated and analyzed by three-dimensional point cloud data obtained by LiDAR technology, drones, etc., to describe the geometric shape and spatial structure characteristics of the tree. The tree morphology sub-data is used to indicate the current biological morphology of the target tree. The tree morphology sub-data may include the color of leaves, external shape features (such as leaves, bark, flowers, fruits, etc. of trees) and texture, etc.

[0082] It should be noted that growth environment data can also be collected through non-destructive equipment such as high-definition photos, LiDAR, and drones, and the growth environment data can specifically include soil moisture, temperature, light conditions, surrounding vegetation conditions, and the impact of human activities of the target trees collected using environmental sensors.

[0083] It should be noted that when collecting relevant data of target trees, the collected data can be cleaned and preprocessed to eliminate invalid data in order to obtain more effective tree growth data and growth environment data.

[0084] In the above embodiments, the present application can utilize sensors, LiDAR, drones and other technologies to obtain more complete and accurate tree growth data and growth environment data of target trees in real time, and can achieve long-term and more accurate health detection and data analysis of target trees.

[0085] In step S120 of some embodiments, since there are many types of trees, and different trees have different growth tolerance and growth requirements during the growth process, in order to more accurately assess the risk of trees, the present application will further perform tree attribute detection on the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information. The tree attribute information includes the target tree species type and the target tree age. The target tree species type is used to indicate the specific type of the target tree, such as type A tree, type B tree, etc. The target tree age is used to indicate the length of time the target tree has currently grown.

[0086] It should be noted that the tree age detection methods used in related technologies, such as annual ring counting and core wood sampling, are destructive and may cause damage to trees that need to be protected. In addition, tree species and age detection requires a high level of expertise, and there is currently a shortage of professionals, making it impossible to efficiently evaluate multiple insured trees. Based on this, the present application can collect data through image recognition and multispectral imaging technology, and combine pre-trained tree species detection models and tree age detection models to detect the species and age of target trees, thereby achieving non-destructive species and age detection of target trees, which is more suitable for current needs.

[0087] See also Figure 2 , Figure 2 is an optional flowchart of step S120 provided in an embodiment of the present application. In some embodiments of the present application, step S120 may specifically include steps S210 to S230. Figure 2 These three steps are introduced in detail.

[0088] Step S210, performing tree species detection on the tree morphology sub-data based on a preset tree species detection model to obtain a target tree species type;

[0089] Step S220, selecting a target tree age detection model from pre-trained candidate tree age detection models based on the target tree species type;

[0090] Step S230, performing tree age detection on the tree structure sub-data based on the target tree age detection model to obtain the target tree age.

[0091] In step S210 of some embodiments, the present application may analyze the tree morphology sub-data (such as leaf shape, bark texture, overall tree shape, etc.) of the target tree through a pre-trained tree species detection model to determine the target tree type of the target tree. This is because different tree species may have different sensitivities and resistances to environmental factors and natural disasters.

[0092] It should be noted that the present application can input the collected images containing tree morphology sub-data into a pre-trained tree species detection model, which has been pre-trained for various types of tree species based on the color, external shape characteristics and texture of healthy leaves, and can more accurately identify the target tree type. The tree species detection model can be constructed using structures such as an optical OCR model, a deep learning model, and a machine learning model.

[0093] In step S220 of some embodiments, since data of multiple dimensions may be needed to calculate the age of trees, different tree species may have their own unique growth patterns and age characteristics. Using one model to determine the age of trees of multiple different tree species types requires a large amount of calculation, which affects the efficiency of risk assessment. Based on this, the present application can set a corresponding candidate tree age detection model for each tree species type, and these candidate tree age detection models have been trained based on tree species related data of the same numerical type, which can better ensure the accuracy and reliability of tree age detection. Therefore, after determining the target tree species type, the present application can select a target tree age detection model from the pre-trained candidate tree age detection models based on the target tree species type, and the target tree age detection model is used to characterize the candidate tree age detection model of the tree species type corresponding to the target tree.

[0094] In step S230 of some embodiments, the tree structure sub-data is further input into the target tree age detection model to perform tree age detection to obtain the target tree age of the target tree. In other words, the present application can use the data such as trunk, crown area, height, etc. collected by LiDAR to non-destructively measure the tree age based on the target tree age detection model of the same tree species.

[0095] In the above embodiments, compared with the related art which usually adopts the method of drilling core material or intuitively counting annual rings to determine the tree age, the present application can avoid damage to the trees, does not rely on professionals, and can ensure the accuracy of tree age detection.

[0096] It should be noted that the target tree age detection model includes a tree ring prediction sub-model and a correction sub-model. The tree ring detection sub-model is used to perform preliminary detection of the age of the target trees, and the correction sub-model is used to consider other external factors to improve the accuracy of tree age detection.

[0097] See also Figure 3, Figure 3 is an optional flowchart of step S230 provided in an embodiment of the present application. In some embodiments of the present application, step S230 may specifically include steps S310 to S340. Figure 3 These four steps are introduced in detail.

[0098] Step S310, determining the growth restriction weight of the target tree based on the growth environment data;

[0099] Step S320, obtaining tree diameter information from the tree structure sub-data;

[0100] Step S330, performing annual ring prediction on the tree diameter based on the annual ring prediction sub-model to obtain the number of tree rings;

[0101] Step S340: predicting the tree age based on the growth restriction weight and the tree ring number based on the correction sub-model to obtain the target tree age.

[0102] In step S310 of some embodiments, since the degree of growth of the same tree species in different growth environments may vary, for example, the actual growth time of trees in the south is longer, while the actual growth time of the same trees in the north is shorter, so even if the growth time of the trees is the same, the external morphology of the trees will be different. In this way, it is necessary to combine the growth environment data of the trees to more accurately assess the age of the trees, that is, to consider the potential restrictions of the growth environment of the target trees on their growth. Specifically, the growth restriction weight of the target tree can be determined based on the growth environment data (such as soil quality, water supply, climatic conditions, light intensity, etc.). At this time, the growth restriction weight is used to characterize the degree of influence of the corresponding growth environment factors on the growth of the tree, so as to more accurately estimate the actual age of the tree.

[0103] In step S320 of some embodiments, the diameter information of the tree is extracted from the collected tree structure sub-data. The tree diameter information is one of the important indicators for evaluating the size and age of the tree, and can be obtained by measuring the widest part of the trunk. Specifically, the present application can obtain accurate three-dimensional structural information of the target tree by using LiDAR, thereby determining the tree diameter information.

[0104] In step S330 of some embodiments, a pre-established annual ring prediction sub-model is used in combination with the tree diameter information to estimate the number of annual rings of the target tree. Annual rings are the layers of growth of trees each year, and their number is usually directly related to the age of the tree. By analyzing the relationship between the tree diameter information and the number of annual rings through a model, the age of the tree can be indirectly inferred. Among them, the annual ring prediction sub-model used in the present application can be an annual ring and diameter regression estimation model established using relevant data of trees whose annual rings have been detected before. The annual ring prediction sub-model can select a suitable regression model (such as linear regression, polynomial regression, etc.), use the number of annual rings as the dependent variable and the diameter as the independent variable, and establish a regression equation. This equation will be based on its known diameter to predict the number of annual rings of unknown trees.

[0105] In step S340 of some embodiments, further, a correction sub-model is used to comprehensively consider the growth restriction weight and the number of tree rings to more accurately predict the age of the target tree. The correction sub-model can be adjusted based on previous data and experience to reflect the actual situation of tree growth under different growth environments. The target tree age obtained at this time takes into account the impact of environmental factors on tree growth, and is therefore more accurate and reliable than simple tree ring predictions. In other words, the present application can assign a corresponding growth restriction weight to each tree based on the growth environment data of each tree. This growth restriction weight can be determined based on the degree of influence of environmental factors on tree growth. For example, the growth of trees in harsh environments may be restricted, and their weights should be adjusted accordingly. Further, the growth restriction weight is applied to the previously established tree ring and diameter regression estimation model, and the model parameters are adjusted to reflect the impact of different growth environments on the relationship between tree rings and diameters, so as to obtain a more accurate target tree age.

[0106] In step S130 of some embodiments, when performing risk factor assessment, the present application may input the tree structure sub-data and the growth environment data into a preset health risk assessment model to perform health risk assessment processing on the tree structure sub-data to obtain health risk assessment data for assessing the health status of the tree. The health risk assessment data may represent the health risk level of the target tree, and the health risk level of the present application may be divided into healthy, sub-healthy, unhealthy, and disaster.

[0107] See also Figure 4 , Figure 4 is an optional flowchart of step S130 provided in the embodiment of the present application. In some embodiments of the present application, step S130 may specifically include steps S410 to S450. Figure 4 These five steps are introduced in detail.

[0108] Step S410, extracting index data from tree structure sub-data to obtain tree potential index data;

[0109] Step S420, extracting index data from the growth environment data to obtain growth environment index data;

[0110] Step S430, determining a first index weight of the tree potential index data and a second index weight of the growth environment index data;

[0111] Step S440, performing weighted calculation based on the tree potential index data, the first index weight, the growth environment index data and the second index weight to obtain an index status score;

[0112] Step S450, performing health risk matching on the indicator status score based on a preset health risk matching rule to obtain health risk assessment data.

[0113] In steps S410 to S450 of some embodiments, the health risk assessment model of the present application is equivalent to a multi-dimensional evaluation system, which determines the weights corresponding to these indicators, i.e., the first indicator weight of the tree potential indicator data and the second indicator weight of the growth environment indicator data, by comparing the tree potential indicator data corresponding to the tree structure sub-data such as tree height, tree diameter, dead branch ratio, tree top dieback rate, tree inclination, tree pest and disease situation, bark damage, etc. detected in Li DAR, and the growth environment indicator data corresponding to the growth environment data such as soil nutrition, moisture, and light collected by the environmental sensor. These data are weighted and calculated to obtain the indicator status score. Further, the indicator status score is matched with the health risk based on the preset health risk matching rules to obtain the health risk assessment data, i.e., the health risk level of the target tree. Among them, the health risk matching rules are used to characterize the mapping relationship between the indicator status score and the health risk level.

[0114] In step S140 of some embodiments, the present application can also perform growth environment risk assessment processing on growth environment data and tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data, that is, to grade growth environment risk levels for growth environment factors by comparing environmental data with data of similar tree species. Among them, environmental risk assessment may involve aspects such as soil pollution, climate change adaptability, and the impact of surrounding development activities, thereby obtaining quantitative or qualitative results of environmental risks.

[0115] See also Figure 5 , Figure 5 is an optional flowchart of step S140 provided in an embodiment of the present application. In some embodiments of the present application, step S140 may specifically include steps S510 to S540. Figure 5 These four steps are introduced in detail.

[0116] Step S510, matching the growth data of similar tree species from historical tree growth data based on tree attribute information;

[0117] Step S520, obtaining environmental data and environmental risk data of similar tree species from growth data of similar tree species;

[0118] Step S530, calculating the difference between the environmental data and the growth environment data of the same tree species to obtain growth environment difference data;

[0119] Step S540, performing a growth environment risk assessment based on the growth environment difference data and the tree species environmental risk data to determine environmental risk assessment data.

[0120] In steps S510 to S540 of some embodiments, when performing a growth environment risk assessment on a target tree, the present application can match the growth data of similar tree species from historical tree growth data based on tree attribute information, that is, find the growth data of other trees that are similar or of the same type as the trees currently being studied or observed, and perform an assessment based on the growth data of these other trees and its own growth environment data.

[0121] Specifically, the present application can access a historical tree growth database or data set, and based on the target tree species type and target tree age of the target tree as keywords or filtering conditions, search and match the growth data of the same tree species in the database to obtain the growth data of the same tree species. Further, extract the environmental data of the same tree species related to the growth of the same tree species (such as climate, soil quality, precipitation, sunshine time, etc.) and the tree species environmental risk data that may be faced (such as the incidence of pests and diseases, extreme weather events, pollution level, etc.). Further, the difference in growth environment can be determined by comparing the environmental data of the same tree species at different locations or times. Among them, the selected environmental data can be standardized to ensure the comparability of the data. Finally, the data obtained in the previous steps can be used to evaluate the current target tree's growth environment risk level or score. Among them, the present application can combine the growth environment difference data and the tree species environmental risk data to construct a risk assessment sub-model, and the use of the risk assessment sub-model can analyze the potential impact of various environmental factors on tree growth. According to the output of the model, determine the growth environment risk level or score of each environmental factor to the target tree. Combine the risk levels or scores of all factors to obtain an overall environmental risk assessment data.

[0122] In the above embodiments, the present application can conduct in-depth research on the performance of specific tree species in different growth environments and provide valuable suggestions and warnings for their future growth and reproduction.

[0123] In step S150 of some embodiments, the present application may also input the growth environment data, tree structure sub-data and tree attribute information into a preset natural disaster assessment model for natural risk assessment processing, that is, comprehensively consider the growth environment, tree structure and attribute information of the tree to assess the risk and type of natural disasters faced by the tree. The natural risk assessment may include risk assessment of extreme weather events such as storms, floods, droughts, fires, and impact analysis of geological disasters such as earthquakes.

[0124] It should be noted that in financial technology tree insurance claims, the training data for the natural disaster assessment model of this application can come from the risk factors summarized in past claims, such as: trees with poor wind resistance may be damaged when the wind force level reaches a certain level; trees growing on slopes, when the soil moisture content reaches a certain level, the slope is prone to landslides, mudslides, etc.

[0125] In the above embodiments, the present application evaluates the health risks, environmental risks, natural risks and other dimensions of target trees through different types of models, which can comprehensively evaluate the various risks faced by trees and improve the accuracy of risk assessment.

[0126] In step S160 of some embodiments, further, the present application can integrate all the data collected and analyzed in the previous steps to conduct a comprehensive risk type assessment on the target trees, aiming to identify and classify the various risks faced by the trees, such as health risks, environmental risks, natural disaster risks, etc., so as to take corresponding management measures to reduce or eliminate these risks.

[0127] See also Figure 6 , Figure 6 is an optional flowchart of step S160 provided in an embodiment of the present application. In some embodiments of the present application, step S160 may specifically include steps S610 to S630. Figure 6 These three steps are introduced in detail.

[0128] Step S610, determining a weight parameter combination corresponding to tree attribute information, health risk assessment data, environmental risk assessment data, and natural risk assessment data;

[0129] Step S620, performing weighted calculation on tree attributes, health risk assessment data, environmental risk assessment data and natural risk assessment data based on a combination of weight parameters to obtain target risk assessment data;

[0130] Step S630: determining the target risk type of the target tree based on the target risk assessment data.

[0131] In steps S610 to S630 of some embodiments, the present application can configure corresponding weights for tree attribute information, health risk assessment data, environmental risk assessment data, and natural risk assessment data according to actual needs to obtain a weight parameter combination. Further, based on the weight parameter combination, weighted calculations are performed on tree attributes, health risk assessment data, environmental risk assessment data, and natural risk assessment data to obtain target risk assessment data. Further, the target risk type of the target tree is determined based on the target risk assessment data and a preset data type mapping table. The target risk type is used to indicate the level of risk faced by the target tree. If the target risk type is higher, it means that the target tree is more likely to be damaged, and a higher price can be claimed for it, and the corresponding insurance cost is also higher.

[0132] It should be noted that after determining the target risk type of the target tree, the present application can conduct an insurance valuation for the target tree to issue a corresponding insurance policy. In this way, the rationality of insurance pricing can be ensured based on the lack of accurate health and risk assessment data.

[0133] It should be noted that this application can upload both the collected data and the calculated data to the management system, avoiding the data being scattered in different departments and systems, lacking unified management and integration, and making it difficult to form a complete data chain. In addition, it can enable data sharing and information exchange between different institutions and departments, thereby improving overall decision-making efficiency.

[0134] It should be noted that after determining the target risk type of the target tree, the application can also input the target risk type and the relevant data of the target tree into the preset tree maintenance recommendation model to provide targeted maintenance suggestions to the maintenance personnel of the target tree, thereby ensuring the safety of the target tree. In other words, the application can continuously detect and collect data regularly, use the model to conduct risk assessment and prediction, and can also provide AI-driven health maintenance information and risk warnings based on the latest assessment results.

[0135] See also Figure 7 , Figure 7 is another optional flow chart of the tree-based risk assessment method provided by the embodiment of the present application. In some embodiments of the present application, after step S160, the tree-based risk assessment method of the present application may also include but is not limited to steps S710 to S750. Figure 7 These five steps are introduced in detail.

[0136] Step S710, determining a risk claim assessment value for a target tree based on the target risk assessment data and the target risk type;

[0137] Step S720, obtaining a claim settlement request for the target tree, and obtaining tree damage data of the target tree based on the claim settlement request;

[0138] Step S730, performing damage degree detection on the tree damage data based on a preset tree damage detection model to determine a target damage degree of the target tree;

[0139] Step S740, determining risk damage matching data of the target tree based on the target risk assessment data and the target damage degree;

[0140] Step S750, determining the target claim value based on the risk damage matching data and the risk claim assessment value.

[0141] In step S710 of some embodiments, the risk claim assessment value refers to the maximum price that can be claimed for the target tree determined based on the target risk assessment data and the target risk type.

[0142] In step S720 of some embodiments, when the target tree is damaged, the owner of the target tree may apply for a claim to a claim settlement company, and then the tree damage data of the target tree may be obtained based on the claim settlement request. The tree damage data may be current data of the target tree collected by non-destructive equipment such as high-definition photos, Li DAR, and drones.

[0143] In step S730 of some embodiments, the present application may further utilize an AI model to perform damage degree detection, that is, perform damage degree detection on tree damage data based on a preset tree damage detection model to determine a target damage degree of the target tree.

[0144] In step S740 of some embodiments, the risk assessment data of the target tree and the determined target damage degree are further analyzed to generate risk damage matching data. The risk damage matching data can reflect the specific factors causing the damage to the target tree, i.e., man-made, natural disasters, etc.

[0145] In step S750 of some embodiments, further, a target claim value is determined based on the risk damage matching data and the risk claim assessment value. Specifically, the risk damage matching data and the previously calculated risk claim assessment value can be calculated according to a preset claim calculation rule to obtain a target claim value. The target claim value is used to represent a reasonable claim value based on relevant regulations, such as a claim amount.

[0146] It should be noted that this application can use past claims data to train AI models to identify situations such as dead branches, broken branches, lightning strikes, and fallen branches, and provide verification and compensation plans through specific detection of the situation. In addition, the establishment of the models mentioned in this application can be trained using structures such as random forests, support vector machines, and deep learning, and through feature screening, the identification of specified target features can be achieved.

[0147] It should be noted that the present application can also determine the repair plan and costs based on the risk damage matching data and the target damage degree by automatically comparing the expert database data, and generate repair suggestions that are automatically assigned to the system cooperation experts to manually determine the repair plan and calculate the repair costs. Furthermore, the present application can automatically generate a compensation plan based on the results of the expert's approval of the plan, and transfer it to the claims settlement post for verification. It can be seen that the claims process is complicated and involves multiple links and departments. The methods used in related technologies have long processing times and low efficiency. In addition, due to the lack of standardized damage assessment methods, disputes are prone to arise during the claims process. The present application realizes the entire claims process based on model automation and adopts standardized damage assessment methods to improve the efficiency and accuracy of the claims process.

[0148] It should be noted that the non-Company software tools or components appearing in the embodiments of the present application are merely examples and do not represent actual use.

[0149] A tree-based risk assessment method provided in an embodiment of the present application utilizes sensors, LiDAR, drones and other technologies to obtain more complete and accurate tree growth data and growth environment data of target trees in real time, which can achieve long-term and more accurate health detection and data analysis of target trees. The present application can also conduct scientific risk assessment and reasonable pricing based on big data analysis. In addition, the present application utilizes automated processes and AI technology to establish a unified data management platform to achieve centralized management and sharing of data, improve claims processing efficiency, and reduce disputes. Therefore, compared to the related technology, which lacks professionals in tree risk assessment, resulting in low efficiency and accuracy in tree risk assessment, the present application combines health risk assessment models, environmental risk assessment models and natural disaster risk assessment models to obtain more complete and multi-dimensional risk assessment data related to target trees, that is, by combining artificial intelligence models, automated risk assessment can be achieved, improving the efficiency and accuracy of tree risk assessment.

[0150] See also Figure 8 , Figure 8 : is a schematic diagram of the module structure of a tree-based risk assessment device provided in an embodiment of the present application. The device can implement the tree-based risk assessment method of the above embodiment. The device may specifically include:

[0151] An acquisition module 810 is used to acquire tree growth data and growth environment data of a target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, wherein the tree structure sub-data is used to indicate the multi-dimensional spatial structure of the target tree, and the tree morphology sub-data is used to indicate the current biological morphology of the target tree;

[0152] A detection module 820 is used to detect tree attributes of target trees based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information;

[0153] A health risk assessment module 830 is used to perform health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data;

[0154] The environmental risk assessment module 840 is used to perform growth environment risk assessment processing on the growth environment data and tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data;

[0155] The natural risk assessment module 850 is used to perform natural risk assessment processing on the growth environment data, tree structure sub-data and tree attribute information based on a preset natural disaster assessment model to obtain natural risk assessment data;

[0156] The type assessment module 860 is used to perform risk type assessment on the target tree based on the tree attribute information, health risk assessment data, environmental risk assessment data and natural risk assessment data.

[0157] It should be noted that the tree-based risk assessment device of the embodiment of the present application is used to implement the tree-based risk assessment method of the above-mentioned embodiment. The tree-based risk assessment device of the embodiment of the present application corresponds to the aforementioned tree-based risk assessment method. Please refer to the aforementioned tree-based risk assessment method for the specific processing process, which will not be repeated here.

[0158] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned tree-based risk assessment method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0159] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0160] The processor 910 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0161] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the tree-based risk assessment method of the embodiment of this application;

[0162] Input / output interface 930, used to implement information input and output;

[0163] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);

[0164] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0165] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0166] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned tree-based risk assessment method is implemented.

[0167] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0169] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0170] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0172] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0173] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0174] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0175] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0177] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0178] The preferred embodiments of the present invention are described with reference to the accompanying drawings, but the scope of the present invention is not limited by the above. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A tree-based risk assessment method, characterized in that: The method comprises: Acquire tree growth data and growth environment data of a target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, wherein the tree structure sub-data is used to indicate the multidimensional spatial structure of the target tree, and the tree morphology sub-data is used to indicate the current biological morphology of the target tree; Perform tree attribute detection on the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information; Performing health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data; Performing growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data; Based on a preset natural disaster assessment model, natural risk assessment processing is performed on the growth environment data, the tree structure sub-data and the tree attribute information to obtain natural risk assessment data; The risk type of the target tree is assessed based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data.

2. The method according to claim 1, characterized in that The tree attribute information includes a target tree species type and a target tree age. The tree attribute detection of the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain the tree attribute information includes: Performing tree species detection on the tree morphology sub-data based on a preset tree species detection model to obtain the target tree species type; Selecting a target tree age detection model from pre-trained candidate tree age detection models based on the target tree species type; The tree age is detected on the tree structure sub-data based on the target tree age detection model to obtain the target tree age.

3. The method according to claim 2, characterized in that The target tree age detection model includes a tree ring prediction sub-model and a correction sub-model. The tree age detection is performed on the tree structure sub-data based on the target tree age detection model to obtain the target tree age, including: Determining a growth restriction weight of the target tree based on the growth environment data; Acquiring tree diameter information from the tree structure sub-data; Performing annual ring prediction on the tree diameter based on the annual ring prediction sub-model to obtain the number of tree rings; The growth restriction weight and the tree ring number are used to predict the tree age based on the correction sub-model to obtain the target tree age.

4. The method according to claim 1, characterized in that The health risk assessment processing is performed on the tree structure sub-data and the growth environment data based on the preset health risk assessment model to obtain health risk assessment data, including: Extracting index data from the tree structure sub-data to obtain tree potential index data; Extracting index data from the growth environment data to obtain growth environment index data; Determining a first index weight of the tree vigor index data and a second index weight of the growth environment index data; Performing weighted calculation based on the tree potential index data, the first index weight, the growth environment index data and the second index weight to obtain an index status score; The health risk matching is performed on the indicator status score based on a preset health risk matching rule to obtain the health risk assessment data.

5. The method according to claim 1, characterized in that The step of performing growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data includes: Matching growth data of similar tree species from historical tree growth data based on the tree attribute information; Obtaining environmental data and environmental risk data of similar tree species from the growth data of similar tree species; Performing environmental data difference calculation on the environmental data of the same tree species and the growth environment data to obtain growth environment difference data; A growth environment risk assessment is performed based on the growth environment difference data and the tree species environmental risk data to determine the environmental risk assessment data.

6. The method according to any one of claims 1 to 5, characterized in that: The risk type assessment of the target tree based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data comprises: Determine a weight parameter combination corresponding to the tree attribute information, the health risk assessment data, the environmental risk assessment data, and the natural risk assessment data; Performing weighted calculation on the tree attributes, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data based on the weight parameter combination to obtain target risk assessment data; A target risk type of the target tree is determined based on the target risk assessment data.

7. The method according to claim 6, characterized in that The method further comprises: Determine a risk claim assessment value for the target tree based on the target risk assessment data and the target risk type; Obtaining a claim settlement request for the target tree, and obtaining tree damage data of the target tree based on the claim settlement request; Performing damage degree detection on the tree damage data based on a preset tree damage detection model to determine a target damage degree of the target tree; Determining risk damage matching data of the target tree based on the target risk assessment data and the target damage degree; A target claim value is determined based on the risk-damage matching data and the risk claim assessment value.

8. A tree-based risk assessment device, characterized in that: The device comprises: an acquisition module, used for acquiring tree growth data and growth environment data of a target tree, wherein the tree growth data includes tree structure sub-data and tree morphology sub-data, wherein the tree structure sub-data is used for indicating the multi-dimensional spatial structure of the target tree, and the tree morphology sub-data is used for indicating the current biological morphology of the target tree; A detection module, configured to detect tree attributes of the target tree based on the tree structure sub-data and the tree morphology sub-data to obtain tree attribute information; A health risk assessment module, used for performing health risk assessment processing on the tree structure sub-data and the growth environment data based on a preset health risk assessment model to obtain health risk assessment data; An environmental risk assessment module, used to perform growth environment risk assessment processing on the growth environment data and the tree attribute information based on a preset environmental risk assessment model to obtain environmental risk assessment data; A natural risk assessment module, used for performing natural risk assessment processing on the growth environment data, the tree structure sub-data and the tree attribute information based on a preset natural disaster assessment model to obtain natural risk assessment data; A type assessment module is used to perform risk type assessment on the target tree based on the tree attribute information, the health risk assessment data, the environmental risk assessment data and the natural risk assessment data.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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