A dynamic programming method for route planning in the field survey of plants based on species distribution

Abstract

The present invention discloses a method for dynamic route planning of plant field surveys based on species distribution, which relates to the technical field of plant surveys. A species distribution prediction model is established to obtain the distribution probability of the target plants to be surveyed in each distribution area, and high-potential distribution areas are divided and a distribution heat map is constructed. Based on the distribution heat map and the path optimization algorithm, a preliminary plant field survey route is planned. The position coordinates of the surveyor on the plant field survey route are located in real time, and it is judged whether an emergency event occurs at the current position coordinates. If so, the plant field survey route is re-planned based on the current position coordinates. If not, the original plant field survey route is maintained for the survey. After the survey of the target plants is completed, the final plant field survey route and survey data are uploaded to the Web end and the database respectively. A data processing node is established to interact with the Web end and the database to generate an area survey interactive layer.

Description

Technical Field

[0001] The present invention relates to the technical field of plant surveys, and specifically to a method for dynamically planning routes for field plant surveys based on species distribution. Background Art

[0002] When conducting field plant surveys, traditional methods often rely on the experience of surveyors and limited on-site investigation data, which leads to low survey efficiency and may result in large data deviations. In addition, due to the lack of effective prediction models and route planning, survey teams often face challenges in determining survey routes and it is difficult to achieve optimal allocation of resources and high efficiency of surveys. Currently, although there are some species distribution prediction models based on GIS, they often cannot respond in real time to unexpected situations in field surveys and lack the ability to effectively interact with the Web end and databases, which limits the sharing and subsequent analysis of field survey data. Therefore, current methods for field plant surveys urgently need to be improved to enhance the accuracy and efficiency of surveys. Summary of the Invention

[0003] To solve the above problems, the purpose of the present invention is to provide a method for dynamically planning routes for field plant surveys based on species distribution.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A method for dynamically planning routes for field plant surveys based on species distribution, including the following steps:

[0005] Step S1: Establish a species distribution prediction model based on existing species data and environmental variables, and obtain the distribution probabilities of the target plants to be surveyed in each distribution area through the species distribution prediction model;

[0006] Step S2: Divide high-potential distribution areas according to the distribution probabilities of the target plants in each distribution area, construct a distribution heat map, and plan a preliminary field plant survey route based on the distribution heat map and a path optimization algorithm;

[0007] Step S3: Real-time locate the position coordinates of the surveyor during the survey on the field plant survey route, and determine whether an unexpected situation event occurs at the current position coordinates. If so, re-plan a new field plant survey route based on the current position coordinates. If not, continue the survey along the original field plant survey route;

[0008] Step S4: After completing the survey of the target plants, upload the final field plant survey route and survey data to the Web end and the database respectively, establish data interaction between the data processing node and the Web end and the database, and then generate a regional survey interaction layer.

[0009] Further, the process of establishing a species distribution prediction model based on existing species data and environmental variables and obtaining the distribution probability of the target plants to be investigated in each distribution area through the species distribution prediction model includes:

[0010] Construct an initial convolutional neural network model through deep learning technology. Train the initial convolutional neural network model based on existing species data and environmental variables to predict the distribution areas corresponding to each known species and the distribution probabilities of each known species in their respective distribution areas. Compare the predicted distribution areas of each known species and the distribution probabilities in the corresponding distribution areas with their actual distribution areas and the actual distribution probabilities in each distribution area, and then obtain the prediction accuracy corresponding to the initial convolutional neural network model;

[0011] Denote the prediction accuracy as τ and set the accuracy threshold, denoted as ψ;

[0012] When τ≥ψ, convert the initial convolutional neural network model into the final species distribution prediction model, construct a species dataset corresponding to the target plants to be investigated, input the species dataset into the species distribution prediction model, and then obtain the distribution areas where the target plants are located and the distribution probabilities in each distribution area;

[0013] When τ<ψ, increase the data volume of existing species data and environmental variables, continue to train the initial convolutional neural network model, and after each model training, obtain the real-time prediction accuracy and judge it with the accuracy threshold.

[0014] Further, the process of dividing high-potential distribution areas based on the distribution probability of the target plants in each distribution area and constructing a distribution heat map includes:

[0015] Number several distribution areas corresponding to the target plants and denote them as i, where i = 1, 2, 3, ……, n, and n is a natural number greater than 0. Denote the distribution probability of the target plants in the distribution area numbered i as P[i], and set the calibration value corresponding to the high-potential distribution area, denoted as Γ;

[0016] Divide all distribution areas with P[i]≥Γ into high-potential distribution areas, screen out all distribution areas with P[i]<Γ, and use the distribution density of the target plants in each high-potential distribution area as its corresponding heat value. Map the heat values of the current target plants in each high-potential distribution area to a preset chart file, and then construct a distribution heat map corresponding to several high-potential distribution areas of the target plants.

[0017] Further, the process of planning a preliminary plant field survey route based on the distribution heat map and the path optimization algorithm includes:

[0018] Based on the heat values of each distribution area included in the distribution heat map, construct the route weights of the distribution areas, set the route starting point and the route ending point, use each distribution area as a route preselection point, and based on the path optimization algorithm, starting from the route starting point, calculate and obtain the route distances of all route preselection points adjacent to the route starting point, and then obtain the route value coefficients of the segmented routes formed between the route starting point and the route preselection points. Select the segmented route with the highest route value coefficient as the constituent route segment of the preliminary plant field survey route;

[0019] Continue to repeat the obtaining of the route value coefficients of the segmented routes formed between the route preselection points and select the segmented route with the highest route value coefficient as the constituent route segment of the preliminary plant field survey route. Repeat the above method until the segmented route formed by the route preselection points and the route ending point is constructed, then the preliminary plant field survey route is successfully planned.

[0020] Furthermore, the process of real-time positioning of the coordinates of the investigator's position during the survey on the plant field survey route and determining whether an emergency event occurs at the current position coordinates includes:

[0021] After the preliminary plant field survey route is planned, the investigator is equipped with a GPS positioning device and debugged. The investigator real-time locates the coordinates of his own position during the survey on the plant field survey route. When the position coordinates enter the distribution area corresponding to the target plant on the plant field survey route, it is determined whether an emergency event occurs in the current distribution area;

[0022] If so, based on the current position coordinates of the investigator, a new plant field survey route is re-planned. If not, after the investigator completes the survey of the target plants in the current distribution area, the original plant field survey route is continued for the survey.

[0023] Furthermore, the process of re-planning a new plant field survey route based on the current position coordinates includes:

[0024] For the distribution area where an emergency event occurs in the original plant field survey route, set the route preselection point corresponding to this distribution area in the plant field survey route to an unselectable state. Select all distribution areas adjacent to the current distribution area in the geographical location within the survey area, and construct the segmented routes between the current distribution area and each adjacent distribution area;

[0025] In the original plant field survey route, mark the next distribution area adjacent to the distribution area set to the unselectable state as the area to be selected. Construct the segmented routes between each new segmented route and the current area to be selected, and then splice them to generate several new survey area routes;

[0026] Select the shortest route among the new investigation area routes as the investigator's driving route, and then construct the final new plant field investigation route. The investigator will continue the investigation according to the new plant field investigation route.

[0027] Furthermore, after completing the investigation of the target plants, the process of uploading the final plant field investigation route and the investigation data to the Web end and the database respectively includes:

[0028] When the investigator completes the investigation of the target plants in all the distribution areas covered by the plant field investigation route from the starting point to the ending point of the route, encrypt and upload the final field investigation route passed by the investigator during the investigation to the set Web end. After binding the investigation data obtained for the target plants in each distribution area with the regional coordinates of the corresponding distribution area, encrypt and upload it to the database;

[0029] When the Web end receives the final plant field investigation route, or the database stores the investigation data corresponding to the target plants to be investigated, the Web end or the database will convert the data received or stored respectively into data streams in a preset data format. The data stream corresponding to the Web end is named the first data stream, and the data stream corresponding to the database is named the second data stream.

[0030] Furthermore, the process of establishing data interaction between the data processing node and the Web end and the database, and then generating the regional investigation interaction layer includes:

[0031] Establish a data processing node, obtain the communication protocols of the Web end and the database respectively, and send them to the data processing node. After the protocol stack set by the data processing node reads the communication protocols, judge whether the Web end and the database can directly perform data interaction with the current data processing node according to the communication protocols;

[0032] If so, create a temporary operation space, conduct program review on the first data stream and the second data stream, then judge whether there are any abnormalities in the first data stream and the second data stream, import the first data stream and the second data stream without abnormalities into the temporary operation space, generate a regional investigation layer, configure an interaction list for interactive operation permissions for the regional investigation layer, and then convert the regional investigation layer into the corresponding regional investigation interaction layer. Otherwise, locate the data points with abnormal data in the first data stream and the second data stream, and perform correction operations;

[0033] If not, the protocol stack will convert the communication protocol of the data processing node itself into an effective communication protocol capable of data interaction with the Web side and the database, and then continue to operate the first data stream and the second data stream, and generate a regional survey interaction layer.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: By establishing a species distribution prediction model, the distribution probability of the target plant can be accurately predicted based on the existing species data and environmental variables, thereby improving the accuracy of the survey; By dividing the high-potential distribution areas and constructing a distribution heat map, combined with the path optimization algorithm, an efficient field survey route for plants can be planned, reducing unnecessary survey work, improving the survey efficiency, real-time positioning the location of the surveyor, and re-planning the survey route based on the real-time situation, effectively coping with unexpected events in the field survey, ensuring the continuity and safety of the survey, and by uploading the survey route and data to the Web side and the database, and establishing a data processing node, the effective interaction and sharing of data are realized, facilitating subsequent data analysis and decision support. Brief Description of the Drawings

[0035] Figure 1 is a flowchart of the present invention. Detailed Embodiments

[0036] As Figure 1 shown, a method for dynamic route planning of plant field surveys based on species distribution includes the following steps:

[0037] Step S1: Establish a species distribution prediction model based on the existing species data and environmental variables, and obtain the distribution probability of the target plant to be surveyed in each distribution area through the species distribution prediction model;

[0038] Step S2: Divide the high-potential distribution areas according to the distribution probability of the target plant in each distribution area, construct a distribution heat map, and plan a preliminary plant field survey route based on the distribution heat map and the path optimization algorithm;

[0039] Step S3: Real-time locate the position coordinates of the surveyor during the survey on the plant field survey route, and determine whether an unexpected event occurs at the current position coordinates. If so, re-plan a new plant field survey route based on the current position coordinates. If not, continue the survey along the original plant field survey route;

[0040] Step S4: After completing the survey of the target plant, upload the final plant field survey route and survey data to the Web side and the database respectively, establish data interaction between the data processing node and the Web side and the database, and then generate a regional survey interaction layer.

[0041] It should be further noted that in the specific implementation process, the process of establishing a species distribution prediction model based on existing species data and environmental variables and obtaining the distribution probability of the target plants to be investigated in each distribution area includes:

[0042] The existing species data includes historical plant survey records, plant storage specimens, and remote sensing images, and the environmental variables include monthly temperature changes, monthly precipitation changes, monthly sunlight changes, soil types, and altitude;

[0043] Construct an initial convolutional neural network model through deep learning technology, train the initial convolutional neural network model according to the existing species data and environmental variables, predict the distribution areas corresponding to each known species, and the distribution probabilities corresponding to each known species in their respective distribution areas. Compare the predicted distribution areas of each known species and the distribution probabilities in the corresponding distribution areas with their actual distribution areas and the actual distribution probabilities in each distribution area, and then obtain the prediction accuracy rate corresponding to the initial convolutional neural network model;

[0044] Denote the prediction accuracy rate as τ, set the accuracy rate threshold, and denote the accuracy rate threshold as ψ;

[0045] When τ≥ψ, convert the initial convolutional neural network model into the final species distribution prediction model, construct a species data set corresponding to the target plants to be investigated, input the species data set into the species distribution prediction model, and then obtain the distribution areas where the target plants are located and the distribution probabilities in each distribution area;

[0046] When τ<ψ, increase the data volume of the existing species data and environmental variables, continue to train the initial convolutional neural network model, and after each model training is completed, obtain the real-time prediction accuracy rate and judge it with the accuracy rate threshold.

[0047] It should be noted that if the entire distribution areas where a certain known species is located and the distribution probabilities in each distribution area are the same as the actual distribution areas and the actual distribution probabilities in the corresponding distribution areas, it means that the prediction of the current known species is an accurate prediction. Otherwise, it is an incorrect prediction. Use the number of accurate predictions as the numerator and the total number of predictions as the denominator to obtain the prediction accuracy rate τ corresponding to the initial convolutional neural network model.

[0048] It should be further noted that in the specific implementation process, the process of dividing high-potential distribution areas according to the distribution probabilities of the target plants in each distribution area and constructing a distribution heat map includes:

[0049] Number several distribution areas corresponding to the target plant, and denote the numbers as i, where i = 1, 2, 3, ……, n, and n is a natural number greater than 0. Denote the distribution probability of the target plant corresponding to the distribution area numbered i as P[i];

[0050] Set the calibration value corresponding to the high-potential distribution area, and denote this calibration value as Γ;

[0051] For the currently investigated target plant, divide all distribution areas with P[i] ≥ Γ into high-potential distribution areas, screen out all distribution areas with P[i] < Γ, and take the distribution density of the target plant under each high-potential distribution area as its corresponding heat value. Map the heat value of the current target plant in each high-potential distribution area to a preset chart file, and then construct a distribution heat map corresponding to several high-potential distribution areas of the target plant.

[0052] The expression formula for the distribution density of the target plant is as follows:

[0053] ;

[0054] Among them, represents the identification symbol corresponding to the distribution density of the target plant, represents the number of individuals of the target plant in the distribution area, represents the area size of the distribution area.

[0055] It should be further noted that in the specific implementation process, the process of planning the initial plant field survey route based on the distribution heat map and the path optimization algorithm includes:

[0056] Based on the heat value of each distribution area included in the distribution heat map, construct the route weight corresponding to the corresponding distribution area with a positive correlation function relationship, set the route starting point and the route ending point, and take each distribution area as a route preselection point;

[0057] Based on the path optimization algorithm, starting from the route starting point, calculate the route distances of all route preselection points adjacent to the route starting point, and then obtain the route value coefficient of the segmented route formed between the route starting point and the route preselection point. Select the segmented route with the highest route value coefficient as the constituent route segment of the initial plant field survey route;

[0058] Continue to repeat the acquisition of the route value coefficient of the segmented route formed between the route preselection points, and select the segmented route with the highest route value coefficient as the constituent route segment of the initial plant field survey route. Repeat the above method until the segmented route formed by the route preselection points and the route ending point is constructed, then the initial plant field survey route is successfully planned;

[0059] Denote the route value coefficient as , then The expression of

[0060] ;

[0061] Among them, when the route weight is larger and the route distance is smaller, the value of the route value coefficient is higher, indicating that the priority level of the corresponding segmented route being selected is higher, and it can be used as a component of the preliminary plant field survey route.

[0062] It should be further noted that in the specific implementation process, the process of real-time positioning of the investigator's position coordinates during the plant field survey on the route and determining whether an emergency event occurs at the current position coordinates includes:

[0063] After the preliminary plant field survey route is planned, the investigator is equipped with a GPS positioning device, and after the GPS positioning device is debugged, the investigator real-time locates his own position coordinates during the survey on the plant field survey route;

[0064] When the position coordinates enter the distribution area of the target plant corresponding to the plant field survey route, determine whether an emergency event occurs in the current distribution area. If so, based on the current position coordinates of the investigator, re-plan a new plant field survey route. If not, after the investigator completes the survey of the target plant in the current distribution area, continue to conduct the survey along the original plant field survey route;

[0065] The emergency events include natural disasters such as floods, landslides, and debris flows in the current distribution area where the original field survey route may be blocked or dangerous, including encountering aggressive wild animals such as wasps, snakes, and wild boars in the current distribution area, which may cause damage to the survey equipment carried by the investigator or pose a threat to the life and health of the investigator himself, and also include encountering bad weather in the distribution area where the current plant field survey route is located, resulting in the inability to survey the target plants in the current distribution area.

[0066] It should be further noted that in the specific implementation process, the process of re-planning a new plant field survey route based on the current position coordinates includes:

[0067] For the distribution area where an unexpected situation occurs in the original plant field survey route, set the route pre-selection points corresponding to this distribution area in the plant field survey route to the non-selectable state, select all the distribution areas adjacent to the current distribution area in the geographical location within the survey area, and construct segmented routes between the current distribution area and each adjacent distribution area;

[0068] In the original plant field survey route, mark the next distribution area adjacent to the distribution area set to the non-selectable state as the area to be selected, construct segmented routes between each new segmented route and the current area to be selected, and then splice and generate several new survey area routes;

[0069] Select the shortest one among the new survey area routes as the driving route for the investigator, and then construct the final new plant field survey route, and let the investigator continue the survey according to the new plant field survey route.

[0070] It should be further noted that in the specific implementation process, the process of uploading the final plant field survey route and survey data to the Web end and the database respectively after completing the survey of the target plants includes:

[0071] When the investigator completes the survey of the target plants in all the distribution areas covered by the plant field survey route from the starting point to the ending point of the route, encrypt and upload the final field survey route passed by the investigator during the survey to the set Web end;

[0072] On the final plant field survey route, bind the survey data obtained from the corresponding survey of the target plants in each distribution area with the regional coordinates of the corresponding distribution area, and encrypt and upload the survey data after binding the regional coordinates to the database;

[0073] When the Web end receives the final plant field survey route, or when the database stores the survey data corresponding to the target plants to be surveyed in each distribution area on the final plant field survey route, the Web end or the database converts the data received or stored respectively into a data stream in a preset data format;

[0074] Among them, the data stream corresponding to the Web end is named the first data stream;

[0075] The data stream corresponding to the database is named the second data stream.

[0076] It should be further noted that in the specific implementation process, the process of establishing data interaction between the data processing node and the Web end and the database, and then generating the regional survey interaction layer includes:

[0077] Establish a data processing node and configure the node identity code corresponding to the data processing node;

[0078] The node identity code serves as the unique identity identifier of the data processing node;

[0079] Respectively obtain the communication protocols corresponding to the Web side and the database, and send their respective communication protocols to the data processing node. After the protocol stack set by the data processing node reads the communication protocols, it is judged whether the Web side and the database can directly perform data interaction with the current data processing node according to the communication protocols;

[0080] If so, the data processing node respectively obtains the first data stream in the Web side and the second data stream in the database, and creates a temporary operation space. After performing program review on the first data stream and the second data stream, it is judged whether there are abnormalities in the first data stream and the second data stream;

[0081] The content of the program review is as follows:

[0082] Judge whether the data formats of the first data stream and the second data stream respectively conform to the preset standard format, and judge whether there are missing, repeated, and incorrect data at several data points that make up the first data stream and the second data stream. If the data format does not conform to the standard format, or there are missing, repeated, and incorrect data at the data points, it is judged that the corresponding first data stream or second data stream has an abnormality, otherwise, there is no abnormality;

[0083] Import the first data stream and the second data stream without abnormalities into the temporary operation space. The temporary operation space synchronously processes the first data stream and the second data stream, and then generates a regional investigation layer. Configure an interaction list for interactive operations for the regional investigation layer, and then convert the regional investigation layer into a corresponding regional investigation interactive layer;

[0084] The content of the temporary operation space synchronously processing the first data stream and the second data stream is as follows:

[0085] Parse from the first data stream the distribution areas of several target plants covered by the final plant field investigation route, and use each distribution area as an operation object. Map all the operation objects to the preset blank layer according to their respective area coordinates, and then obtain the topological route of the final plant field investigation route on the blank layer;

[0086] Parse from the second data stream the investigation data of the target plants in all distribution areas on the final plant field investigation route, and bind the investigation data to the several distribution areas represented on the topological route according to the distribution positions of the distribution areas on the plant field investigation route;

[0087] Furthermore, survey data of target plants corresponding to several points on the topological route are formed;

[0088] Set a to-be-interacted trigger event for each point, and then generate a final regional survey interaction layer. When the user clicks on a certain point on the topological route in the regional survey interaction layer, the to-be-interacted trigger event corresponding to the current point is successfully triggered, and the interaction operation with the regional survey interaction layer is completed;

[0089] The regional survey interaction layer includes a data display layer and a user interaction layer;

[0090] The data display layer is used to visually display the survey data of target plants in each distribution area on the final plant field survey route, and set respective corresponding operation areas for the survey data visually displayed in each distribution area within the user interaction layer;

[0091] The user interaction layer is used to provide interaction operations for all users in the interaction list. When the user clicks on the survey data visually displayed in each operation area on the final plant field survey route in the user interaction layer, the detailed information corresponding to the survey data is further retrieved;

[0092] And provide the permission for all users in the interaction list to download and forward the survey data of the target plants;

[0093] For the abnormal first data stream and second data stream, locate the data points with abnormalities in the first data stream and second data stream, perform correction operations on the data at the abnormal data points, and then convert the abnormal first data stream and second data stream to be normal, and generate a corresponding regional survey interaction layer;

[0094] If not, the protocol stack of the data processing node converts the communication protocol of the data processing node itself into an effective communication protocol capable of data interaction with the Web end and the database, and then continues to operate on the first data stream and the second data stream to generate a regional survey interaction layer;

[0095] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for dynamic route planning for plant field survey based on species distribution, characterized in that: The following steps are involved: Step S1: Establish a species distribution prediction model based on existing species data and environmental variables, and obtain the distribution probability of the target plants to be investigated in each distribution area through the species distribution prediction model; Step S2: Divide the high potential distribution area according to the distribution probability of the target plant in each distribution area, construct a distribution heat map, and plan a preliminary plant field survey route based on the distribution heat map and path optimization algorithm; Step S3: real-time positioning of the position coordinates of the investigator on the plant field survey route, and determining whether an emergency event occurs at the current position coordinates. If so, a new plant field survey route is replanned based on the current position coordinates; if not, the original plant field survey route is maintained for the survey; Step S4: After completing the survey of the target plants, upload the final plant field survey route and survey data to the Web terminal and database respectively, establish a data processing node to interact with the Web terminal and database, and then generate a regional survey interaction layer.

2. A method for dynamic route planning for plant field survey based on species distribution according to claim 1, characterized in that: The process of establishing a species distribution prediction model based on existing species data and environmental variables and obtaining the distribution probability of the target plants to be investigated in each distribution area through the species distribution prediction model includes: The initial convolutional neural network model is constructed through deep learning technology. The initial convolutional neural network model is trained according to the existing species data and environmental variables to predict the distribution area corresponding to each known species and the distribution probability of each known species in its respective distribution area. The predicted distribution area of ​​each known species and the distribution probability in the corresponding distribution area are compared with the actual distribution area and the actual distribution probability in each distribution area, thereby obtaining the prediction accuracy corresponding to the initial convolutional neural network model. The prediction accuracy is recorded as τ, and the accuracy threshold is set and recorded as ψ; When τ ≥ ψ, the initial convolutional neural network model is converted into the final species distribution prediction model, and the species dataset corresponding to the target plant to be investigated is constructed. The species dataset is input into the species distribution prediction model to obtain the distribution area of ​​the target plant and the distribution probability in each distribution area. When τ<ψ, the amount of existing species data and environmental variables is increased, and the initial convolutional neural network model is continued to be trained. After each model training, the real-time prediction accuracy and accuracy threshold are obtained for judgment.

3. A method for dynamic route planning for plant field survey based on species distribution according to claim 2, characterized in that: The process of dividing the high potential distribution area according to the distribution probability of the target plant in each distribution area and constructing the distribution heat map includes: The distribution areas corresponding to the target plants are numbered and recorded as i, i = 1, 2, 3, ..., n, where n is a natural number greater than 0, and the distribution probability of the target plant in the distribution area numbered i is recorded as P[i], and the calibration value corresponding to the high potential distribution area is set and recorded as Γ; All distribution areas with P[i]≥Γ are divided into high potential distribution areas, and all distribution areas with P[i]<Γ are screened out. The distribution density of the target plant in each high potential distribution area is used as its corresponding thermal value. The thermal value of the current target plant in each high potential distribution area is mapped to a preset chart file, and then the distribution thermal map corresponding to several high potential distribution areas of the target plant is constructed.

4. The method for dynamic route planning of plant field survey based on species distribution according to claim 3, characterized in that: The process of planning a preliminary plant field survey route based on the distribution heat map and path optimization algorithm includes: Based on the heat value of each distribution area included in the distribution heat map, the route weight of the distribution area is constructed, the route starting point and the route ending point are set, and each distribution area is used as a route pre-selected point. Based on the path optimization algorithm, starting from the route starting point, the route distance of all route pre-selected points adjacent to the route starting point is calculated, and then the route value coefficient of the segmented route formed between the route starting point and the route pre-selected point is obtained, and the segmented route with the highest route value coefficient is selected as the component route segment of the preliminary plant field survey route; Continue to repeat the acquisition of the route value coefficient of the segmented route formed by the route pre-selected points and the route pre-selected points, and select the segmented route with the highest route value coefficient as the component route segment of the preliminary plant field investigation route. Repeat the above method until the segmented route formed by the route pre-selected points and the route termination points is completed, and the preliminary plant field investigation route is successfully planned.

5. The method for dynamic route planning of plant field survey based on species distribution according to claim 4, characterized in that: The process of real-time positioning the position coordinates of the investigator on the plant field investigation route and determining whether an emergency event occurs at the current position coordinates includes: After the preliminary planning of the plant field survey route is completed, the investigator is equipped with a GPS positioning device and debugged, and the investigator locates the coordinates of his / her own position on the plant field survey route in real time. When the position coordinates enter the distribution area corresponding to the target plant on the plant field survey route, it is determined whether an emergency event occurs in the current distribution area; If yes, a new plant field survey route is replanned based on the investigator's current location coordinates. If no, the investigator continues to follow the original plant field survey route after completing the survey of the target plants in the current distribution area.

6. A method for dynamic route planning for plant field survey based on species distribution according to claim 5, characterized in that: The process of replanning a new plant field survey route based on the current location coordinates includes: For the distribution area where the emergency event occurred in the original plant field survey route, the corresponding route pre-selected point of the distribution area in the plant field survey route is set to an unselectable state, and all the distribution areas geographically adjacent to the current distribution area in the survey area are selected to construct a segmented route between the current distribution area and each adjacent distribution area; The next distribution area adjacent to the distribution area set to be unselectable in the original plant field survey route is marked as a candidate area, and a segmented route between each new segmented route and the current candidate area is constructed, and then several new survey area routes are spliced ​​and generated; The shortest route in the newly surveyed area is selected as the investigator's driving route, and then a new plant field survey route is finally constructed, and the investigator continues the survey along the new plant field survey route.

7. A method for dynamic route planning for plant field survey based on species distribution according to claim 6, characterized in that: After completing the survey of the target plants, the process of uploading the final plant field survey route and survey data to the Web terminal and database includes: When the investigator completes the investigation of the target plants in all the distribution areas covered by the plant field investigation route from the starting point to the end point of the route, the final field investigation route taken by the investigator during the investigation is encrypted and uploaded to the set Web terminal, and the investigation data obtained from the corresponding investigation of the target plants in each distribution area is bound to the regional coordinates of the corresponding distribution area and encrypted and uploaded to the database; When the Web terminal receives the final plant field survey route, or the database stores the survey data corresponding to the target plant to be surveyed, the Web terminal or the database converts the corresponding received or stored data into a data stream in a preset data format. The data stream corresponding to the Web terminal is named the first data stream, and the data stream corresponding to the database is named the second data stream.

8. The method for dynamic route planning of plant field survey based on species distribution according to claim 7, characterized in that: The process of establishing a data processing node to interact with the Web client and database to generate a regional survey interactive layer includes: Establish a data processing node, obtain the communication protocols of the Web end and the database respectively, and send them to the data processing node. After the protocol stack set by the data processing node reads the communication protocol, it determines whether the Web end and the database can directly interact with the current data processing node according to the communication protocol; If so, a temporary operation space is created, and after program auditing the first data stream and the second data stream, it is determined whether there are abnormalities in the first data stream and the second data stream, and the first data stream and the second data stream without abnormalities are imported into the temporary operation space, and a regional survey layer is generated, and an interactive list of interactive operation permissions is configured for the regional survey layer, and then the regional survey layer is converted into a corresponding regional survey interactive layer; otherwise, the data of the data points with abnormalities in the first data stream and the second data stream are located, and correction operations are performed; If not, the protocol stack converts the data processing node's own communication protocol into an effective communication protocol that can interact with the Web side and the database, and then continues to operate the first data stream and the second data stream, and generates a regional survey interaction layer.

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