A quinoa cultivation management system and method

Abstract

The present invention provides a quinoa cultivation management system and method. The system includes: a monitoring module for monitoring cultivation abnormal events in the quinoa cultivation site; a matching module for matching analysis experts for the cultivation abnormal events; and an execution module for obtaining an abnormal handling solution for the cultivation abnormal events decided by the analysis experts and executing the solution. The quinoa cultivation management system and method of the present invention automatically monitor cultivation abnormal events in the quinoa cultivation site, eliminating the need for expert personnel to enter the quinoa cultivation site for manual inspection, reducing the consumption of labor costs. In addition, by matching analysis experts for cultivation abnormal events, the handling efficiency and accuracy of cultivation abnormal events are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quinoa cultivation management, and particularly relates to a quinoa cultivation management system and method. Background Art

[0002] Compared with cultivating other food crops, quinoa cultivation is more difficult. To ensure the smooth growth of quinoa during cultivation, quinoa needs to be cultivated and managed. Generally, when cultivating and managing quinoa, multiple expert personnel are set up. The expert personnel check for cultivation abnormal events in the quinoa cultivation site and handle the abnormalities. However, the labor cost consumed by the expert personnel in checking in the cultivation site is relatively large. In addition, due to the different types of cultivation abnormal events detected and the different fields of expertise of different expert personnel, there is a lack of a matching mechanism between the cultivation abnormal events and the expert personnel, which may lead to insufficient processing efficiency and accuracy of the cultivation abnormal events.

[0003] Therefore, a solution is urgently needed. Summary of the Invention

[0004] One of the objectives of the present invention is to provide a quinoa cultivation management system, which automatically monitors the cultivation abnormal events in the quinoa cultivation site, eliminates the need for expert personnel to enter the quinoa cultivation site for manual inspection, reduces the consumption of labor costs. In addition, it matches analysis experts for the cultivation abnormal events, improving the processing efficiency and accuracy of the cultivation abnormal events.

[0005] A quinoa cultivation management system provided by an embodiment of the present invention includes:

[0006] A monitoring module for monitoring the cultivation abnormal events in the quinoa cultivation site;

[0007] A matching module for matching analysis experts for the cultivation abnormal events;

[0008] An execution module for obtaining the abnormal handling scheme of the cultivation abnormal events decided by the analysis experts and executing the scheme.

[0009] Preferably, the quinoa cultivation management system further includes:

[0010] An auxiliary module for identifying the analysis difficulties encountered by the analysis experts when analyzing the cultivation abnormal events; and also for assisting the analysis experts in solving the analysis difficulties.

[0011] Preferably, the auxiliary module identifying the analysis difficulties encountered by the analysis experts when analyzing the cultivation abnormal events includes:

[0012] When the analysis experts enter the quinoa cultivation site, obtaining the movement trajectories generated by the analysis experts in the most recent preset time;

[0013] Generate an anomaly distribution map based on the cultivation anomaly event and the preset on-site map corresponding to the quinoa cultivation site;

[0014] Represent the movement trajectory within the anomaly distribution map to obtain a map trajectory;

[0015] Determine a local trajectory that meets the local trajectory conditions from the map trajectory;

[0016] Obtain the start and end times of the local trajectory;

[0017] Obtain the first-person view video generated by the analysis expert during the time period between the start and end times;

[0018] Determine the analysis difficulty based on the first-person view video;

[0019] Among them, the local trajectory conditions include:

[0020] The local trajectory completely represents that the analysis expert stays beside an abnormal area on the anomaly distribution map for more than a preset duration;

[0021] Or,

[0022] The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the anomaly distribution map more than a preset number of times;

[0023] Or,

[0024] The local trajectory completely represents that the analysis expert turns back to an abnormal area on the anomaly distribution map.

[0025] Preferably, the auxiliary module assists the analysis expert in solving the analysis difficulty, including:

[0026] Obtain the analysis operation sequence performed by the analysis expert when solving the analysis difficulty;

[0027] Determine the solution idea based on the analysis operation sequence;

[0028] Obtain the trigger value of the solution idea;

[0029] When the trigger value is greater than or equal to the preset trigger threshold, obtain the recommended solution idea for the analysis difficulty;

[0030] Determine the connectable ideas of the solution idea based on the recommended solution idea;

[0031] Determine a first idea node and the remaining second idea nodes that meet the idea node conditions from the connectable ideas;

[0032] Show the connectable train of thought to the analysis expert; when showing, the first train-of-thought node is continuously shown, and the second train-of-thought node is first hidden and then continuously shown when the display condition is met;

[0033] Among them, the train-of-thought node conditions include:

[0034] The first train-of-thought node is the head and tail train-of-thought nodes of the connectable train of thought;

[0035] And / or,

[0036] The importance value of the first train-of-thought node is greater than or equal to a preset importance threshold corresponding to the trigger value;

[0037] Among them, the display conditions include:

[0038] The updated train of thought for solving problems determined based on the updated analysis operation sequence completely represents that the analysis expert is about to analyze to the second train-of-thought node.

[0039] Preferably, the auxiliary module obtains the trigger value of the train of thought for solving problems, including:

[0040] Perform feature extraction on the train of thought for solving problems to obtain a plurality of feature data;

[0041] Match each of the feature data with each standard feature data in a preset standard feature database;

[0042] Whenever the feature data matches the standard feature data, obtain the preset index value corresponding to the matched standard feature data;

[0043] Accumulate and calculate the index values to obtain the trigger value.

[0044] A quinoa cultivation management method provided by an embodiment of the present invention includes:

[0045] Monitor cultivation abnormal events in the quinoa cultivation site;

[0046] Match an analysis expert for the cultivation abnormal event;

[0047] Obtain the abnormal handling plan for the cultivation abnormal event decided by the analysis expert and execute the plan.

[0048] Preferably, the quinoa cultivation management method further includes:

[0049] Identify the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormal event; assist the analysis expert in solving the analysis difficulties.

[0050] Preferably, the analysis difficulties encountered by the analysis expert in analyzing the cultivation abnormal event include:

[0051] When the analysis expert enters the quinoa cultivation site, obtain the movement trajectory generated by the analysis expert within the most recent preset time;

[0052] Based on the cultivation abnormal event and the preset site map corresponding to the quinoa cultivation site, generate an abnormal distribution map;

[0053] Represent the movement trajectory in the abnormal distribution map to obtain a map trajectory;

[0054] Determine a local trajectory that meets the local trajectory conditions from the map trajectory;

[0055] Obtain the start and end times of the local trajectory;

[0056] Obtain the first-person view video generated by the analysis expert during the time period between the start and end times;

[0057] Based on the first-person view video, determine the analysis difficulty;

[0058] Among them, the local trajectory conditions include:

[0059] The local trajectory completely represents that the analysis expert stays beside an abnormal area on the abnormal distribution map for more than a preset duration;

[0060] Or,

[0061] The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the abnormal distribution map for more than a preset number of times;

[0062] Or,

[0063] The local trajectory completely represents that the analysis expert turns back to an abnormal area on the abnormal distribution map.

[0064] Preferably, assisting the analysis expert in solving the analysis difficulty includes:

[0065] Obtain the analysis operation sequence performed by the analysis expert when solving the analysis difficulty;

[0066] Based on the analysis operation sequence, determine the solution idea;

[0067] Obtain the trigger value of the solution idea;

[0068] When the trigger value is greater than or equal to the preset trigger threshold, obtain the recommended solution idea for the analysis difficulty;

[0069] Based on the recommended solution idea, determine the connectable ideas of the solution idea;

[0070] Determine a first idea node that meets the idea node conditions and the remaining second idea nodes from the connectable ideas;

[0071] Display the connectable ideas to the analysis expert; when displaying, the first idea node is continuously displayed, and the second idea nodes are first hidden and then continuously displayed when they meet the display conditions;

[0072] Among them, the idea node conditions include:

[0073] The first idea node is the head and tail idea nodes of the connectable idea;

[0074] And / or,

[0075] The importance value of the first idea node is greater than or equal to a preset importance threshold corresponding to the trigger value;

[0076] Among them, the display conditions include:

[0077] The updated solution idea determined based on the updated analysis operation sequence completely represents that the analysis expert is about to analyze to the second idea node.

[0078] Preferably, obtaining the trigger value of the solution idea includes:

[0079] Perform feature extraction on the solution idea to obtain multiple feature data;

[0080] Match each of the feature data with each standard feature data in a preset standard feature database;

[0081] Whenever the feature data matches the standard feature data, obtain the preset index value corresponding to the matched standard feature data;

[0082] Accumulate and calculate the index values to obtain the trigger value.

[0083] Other features and advantages of the present invention will be described in the following description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0084] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Brief Description of the Drawings

[0085] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0086] Figure 1 It is a schematic diagram of a quinoa cultivation management system in an embodiment of the present invention;

[0087] Figure 2 It is a schematic diagram of a quinoa cultivation management method in an embodiment of the present invention. Detailed implementation manners

[0088] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0089] An embodiment of the present invention provides a quinoa cultivation management system, as Figure 1 shown, including:

[0090] A monitoring module 1, which is used to monitor cultivation abnormal events in the quinoa cultivation site;

[0091] A matching module 2, which is used to match analysis experts for the cultivation abnormal events;

[0092] An execution module 3, which is used to obtain the abnormal handling scheme of the cultivation abnormal events decided by the analysis experts and execute the scheme.

[0093] The quinoa cultivation site is the site for cultivating quinoa; the cultivation abnormal events are abnormal events such as difficult germination, slow growth, and pest and disease attacks that occur during the quinoa cultivation process; when monitoring the cultivation abnormal events in the quinoa cultivation site, Internet of Things technologies can be adopted. For example: controlling an unmanned aerial vehicle to cruise and photograph images of each cultivated quinoa in the quinoa cultivation site, and determining whether cultivation abnormal events occur based on image analysis. Another example: setting soil moisture sensors in the quinoa cultivation site and determining whether cultivation abnormal events occur based on the detection data of the soil moisture sensors, etc.; the analysis experts are expert personnel responsible for quinoa cultivation management; when matching analysis experts for the cultivation abnormal events, the cultivation abnormal events of the quinoa cultivation abnormal types that the analysis experts are good at handling are assigned to the corresponding analysis experts; after the analysis experts take over the cultivation abnormal events, they conduct event analysis and decide on the abnormal handling scheme of the cultivation abnormal events, and the system obtains and executes the scheme.

[0094] This application automatically monitors the cultivation abnormal events in the quinoa cultivation site, eliminating the need for expert personnel to enter the quinoa cultivation site for manual inspection, reducing the consumption of labor costs. In addition, matching analysis experts for the cultivation abnormal events improves the handling efficiency and accuracy of the cultivation abnormal events.

[0095] In one embodiment, the quinoa cultivation management system further includes:

[0096] An auxiliary module, which is used to identify the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormal event; and is also used to assist the analysis expert in solving the analysis difficulties.

[0097] Generally, the types of the occurred cultivation abnormal events are the same, and the reasons for the occurrence of the cultivation abnormal events may be different and diverse. Therefore, when the analysis expert takes over the cultivation abnormal events of the quinoa cultivation abnormal types that he is good at handling, analysis difficulties may still occur; in addition, after the analysis expert takes over the cultivation abnormal event and conducts analysis on the event, if he stops analyzing and inputs the analysis difficulties he encounters, it may cause the analysis thinking to be temporarily interrupted, and the thinking activity level will decrease, which may affect the processing efficiency; therefore, the embodiment of the present invention automatically identifies the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormality, and tries not to cause the analysis thinking of the analysis expert to be temporarily interrupted, which is more user-friendly; finally, the complex analysis expert solves the analysis difficulties, which improves the efficiency of his decision-making on the abnormal handling plan of the cultivation abnormal event.

[0098] In one embodiment, the auxiliary module identifies the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormal event, including:

[0099] When the analysis expert enters the quinoa cultivation site, obtain the movement trajectory generated by the analysis expert within the most recent preset time;

[0100] Based on the cultivation abnormal event and the preset site map corresponding to the quinoa cultivation site, generate an abnormal distribution map;

[0101] Represent the movement trajectory in the abnormal distribution map to obtain a map trajectory;

[0102] Determine a local trajectory that meets the local trajectory conditions from the map trajectory;

[0103] Obtain the start and end times of the local trajectory;

[0104] Obtain the first-person view video generated by the analysis expert during the time period between the start and end times;

[0105] Based on the first-person view video, determine the analysis difficulty;

[0106] Wherein, the local trajectory conditions include:

[0107] Condition A1: The local trajectory completely represents that the analysis expert stays beside an abnormal area on the abnormal distribution map for more than a preset duration;

[0108] Or,

[0109] Condition A2: The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the abnormal distribution map more than a preset number of times;

[0110] Or,

[0111] Condition A3: The local trajectory completely represents that the analysis expert turns back to an abnormal area on the abnormal distribution map.

[0112] The recent preset time can be 5 minutes; the movement trajectory and the first-person view video can both be obtained through the operation recorder worn by the analysis expert; the area position information of the quinoa cultivation site where quinoa anomalies occur in the quinoa cultivation anomaly event. When generating the abnormal distribution map, the corresponding map area is found in the site map based on this area position information and used as the abnormal area, and each abnormal area on the site map forms an abnormal distribution, and the current site map is used as the abnormal distribution map; the movement trajectory contains trajectory position information, and the corresponding map trajectory is represented in the abnormal distribution map based on this trajectory position information; the start and end times of the local trajectory are the generation times of the start and end positions of the local trajectory; the preset duration can be 1 minute; the preset number of times can be 1 time.

[0113] After taking over the quinoa cultivation anomaly event, the analysis expert needs to go to the quinoa cultivation site to further understand the situation. He wears an operation recorder and enters the quinoa cultivation site; the operation recorder is generally worn on the head of the analysis expert, and it can record the first-person view video and moving position of the analyst. The first-person view video not only includes the viewing pictures after the analyst enters the quinoa cultivation site, but also includes the human voice audio generated after the analyst enters the quinoa cultivation site; the first-person view video reflects the analysis behavior of the analyst analyzing the quinoa cultivation anomaly event in the quinoa cultivation site, and the analysis difficulties encountered by the analysis expert can be determined based on this (for example: if the audio of the analysis expert asking the question "Why is the fertilization uneven?" is recorded in the first-person view video, then the analysis difficulty can be determined as why the fertilization in the quinoa planting field is uneven); however, the analysis expert often stays in the quinoa cultivation site for a long time, and the first-person view video recorded by the operation recorder is often long. If all the recorded first-person view videos are analyzed to determine the analysis difficulties, the workload is huge, which reduces the efficiency of determining the analysis difficulties and delays the progress of assisting the analysis expert to solve the analysis difficulties in the later stage. This problem urgently needs to be solved.

[0114] To solve this problem, the embodiment of the present invention introduces local trajectory conditions, screens out local trajectories from the map trajectories based on the local trajectory conditions. Under the action of the local trajectory conditions, when generating the screened local trajectories, the first-person view video recorded by the operation recorder of the analysis expert is valuable for determining the analysis difficulties encountered by the analysis expert. Therefore, only the first-person view video generated by the analysis expert within the time period between the start and end times of the local trajectory is obtained, and based on this first-person view video, the analysis difficulties are determined, without analyzing all the first-person view videos recorded by the operation recorder after the analysis expert enters the quinoa cultivation site, greatly reducing the analysis workload, improving the efficiency of determining the analysis difficulties, and avoiding delaying the progress of assisting the analysis expert to solve the analysis difficulties later, which is particularly applicable.

[0115] Specifically, if the local trajectory can meet condition A1 in the local trajectory conditions, the straight-line distance from each trajectory point on it to the center point of the abnormal area is less than, for example, 5 meters, and the total duration of the generation of the local trajectory is greater than the preset duration. At this time, it indicates that the analysis expert continuously stays beside an abnormal area and carefully investigates and analyzes the on-site situation in the abnormal area. The operation recorder may record the first-person view video reflecting the analysis difficulties encountered by the analysis expert. Therefore, when generating the local trajectory screened by condition A1, the first-person view video recorded by the operation recorder of the analysis expert is valuable for determining the analysis difficulties encountered by the analysis expert; if the local trajectory can meet condition A2 in the local trajectory conditions, each trajectory point on it represents gradually leaving an abnormal area and approaching a non-abnormal area first, and then leaving the non-abnormal area just approached and approaching an abnormal area. At this time, it indicates that the analysis expert may be comparing the cultivated quinoa in the abnormal area with the cultivated quinoa in the non-abnormal area. The operation recorder may record the first-person view video reflecting the analysis difficulties encountered by the analysis expert. Therefore, when generating the local trajectory screened by condition A2, the first-person view video recorded by the operation recorder of the analysis expert is valuable for determining the analysis difficulties encountered by the analysis expert; if the local trajectory can meet condition A3 in the local trajectory conditions, each trajectory point on it represents gradually leaving an abnormal area first and then turning back to the abnormal area just left. At this time, it indicates that during the process of leaving the abnormal area, the analysis expert wants to verify a new problem and turns back. The operation recorder may record the first-person view video reflecting the analysis difficulties encountered by the analysis expert. Therefore, when generating the local trajectory screened by condition A3, the first-person view video recorded by the operation recorder of the analysis expert is valuable for determining the analysis difficulties encountered by the analysis expert.

[0116] In one embodiment, the auxiliary module assists the analysis expert in solving the analysis difficulties, including:

[0117] Obtaining the analysis operation sequence when the analysis expert solves the analysis difficulties;

[0118] Based on the analysis operation sequence, determine a solution idea;

[0119] Obtain the trigger value of the solution idea;

[0120] When the trigger value is greater than or equal to a preset trigger threshold, obtain the recommended solution ideas for the analysis difficulties;

[0121] Based on the recommended solution ideas, determine the connectable ideas of the solution idea;

[0122] Determine a first idea node and the remaining second idea nodes that meet the idea node conditions from the connectable ideas;

[0123] Display the connectable ideas to the analysis expert; when displaying, the first idea node is continuously displayed, and the second idea nodes are first hidden and then continuously displayed when they meet the display conditions;

[0124] Among them, the idea node conditions include:

[0125] Condition B1: The first idea node is the head and tail idea nodes of the connectable ideas;

[0126] And / or,

[0127] Condition B2: The importance value of the first idea node is greater than or equal to a preset importance threshold corresponding to the trigger value;

[0128] Among them, the display conditions include:

[0129] The updated solution idea determined based on the updated analysis operation sequence completely represents that the analysis expert is about to analyze to the second idea node.

[0130] When solving analysis difficulties, the analysis expert will perform a series of analysis operations, sort the analysis operations according to the order of operation, and obtain an analysis operation sequence. The analysis operations can be operations performed by the analysis expert using an intelligent terminal (such as a mobile phone, tablet, computer, etc.) after encountering analysis difficulties. For example: logging in to the quinoa planting experience sharing forum, querying thesis articles similar to the analysis difficulties, etc.; based on the analysis operation sequence, the thinking of the analysis expert to solve the analysis difficulties can be determined, such as: seeking help online, etc.; the trigger value represents the degree of maturity of the timing that can assist the analysis expert in solving the analysis difficulties; the trigger threshold can be 8; when the trigger value is greater than or equal to the trigger threshold, the timing to assist the analysis expert in solving the analysis difficulties is mature, and preparations are made to start the assistance; the recommended solution idea is that the system can recommend to the analysis expert the idea of solving the analysis difficulties, and the recommended solution idea can be set in advance by technical personnel according to the type of analysis difficulties; the solution idea and the recommended solution idea are both composed of individual idea nodes, and each idea node is connected according to the logical order of the idea. Each idea node represents something done to solve the analysis difficulties; when determining the connectable idea, if the solution idea exists in the previous section of the recommended solution idea, then the remaining idea in the recommended solution idea except the solution idea is used as the connectable idea; introduce the idea node condition, under the screening of the idea node condition, the first idea node selected is necessary to be continuously displayed to the analysis expert; introduce the display condition, the second idea node is hidden first and the analysis expert cannot see it, and it will be continuously displayed only when the second idea node meets the display condition.

[0131] In condition B1 of the idea node condition, when the first idea node is the first and last idea nodes of the connectable idea, the first idea node needs to ensure that the analysis expert can quickly get assistance (the thinking is connected to the first idea node of the connectable idea) and quickly have a general understanding of the thinking (clearly understand the first and last idea nodes of the connectable idea). Therefore, it is necessary to continuously display the first idea node that meets condition B1 to the analysis expert; in condition B2 of the idea node condition, different trigger values are preset with corresponding importance thresholds, which can be set in advance by technical personnel. The larger the trigger value, the greater the degree of maturity of the timing that can assist the analysis expert in solving the analysis difficulties, and it is necessary to assist the analysis expert as much as possible. Therefore, the lower the importance threshold, the lower the display constraint of the idea node. The importance value represents the importance of what the first idea node represents for solving the analysis difficulties to solving the analysis difficulties. When the importance value is greater than or equal to the importance threshold, it means that the first idea node is necessary to be continuously displayed to the analysis expert. Therefore, it is necessary to continuously display the first idea node that meets condition B2 to the analysis expert.

[0132] The updated solution idea determined based on the updated analysis operation sequence completely represents that the analysis expert is about to analyze to the second idea node.

[0133] When the analysis operation sequence is updated, a new analysis operation will be added to the analysis operation sequence, and the solution idea will be re-determined to obtain an updated solution idea; when the updated solution idea fully represents that the analysis expert is about to analyze to the second idea node, it means that the updated solution idea is the same as the idea composed of all the idea nodes before the second idea node on the connectable idea. At this time, the second idea node needs to be continuously displayed to the analysis expert.

[0134] In general, when assisting in solving difficulties, the traditional method is to directly output auxiliary content so that users can get assistance quickly. However, for the special scenario of quinoa planting management, the analysis expert is more familiar with the history and current situation of the quinoa planting site. If the auxiliary content is directly output, the analysis expert may not consider the history and current situation of the quinoa planting site to comprehensively make decisions on the treatment plan for abnormal events in quinoa cultivation. Even when the number of uses increases, the analysis expert will become dependent. To solve this problem, the embodiment of the present invention introduces a trigger value to determine the degree of maturity of the time when the analysis expert can be assisted to solve the analysis difficulties. When the time is ripe, the analysis expert is assisted again, leaving the analysis expert with time for independent analysis and solution. When assisting the analysis expert, the second idea node that is not necessary to be continuously displayed is first hidden, and the analysis expert continues to be left with time for independent analysis and solution. Doing so can not only ensure that the analysis expert will not be dependent on the system, but also ensure that the abnormal handling plan finally decided by the analysis expert is highly suitable and comprehensive, and particularly applicable.

[0135] In one embodiment, the auxiliary module obtains the trigger value of the solution idea, including:

[0136] Performing characterization processing on the solution idea to obtain a plurality of characteristic data;

[0137] Matching each of the characteristic data with each of the standard characteristic data in a preset standard characteristic database;

[0138] Whenever the feature data matches the standard feature data, a preset index value corresponding to the matching standard feature data is obtained;

[0139] The indicator values ​​are cumulatively calculated to obtain the trigger value.

[0140] The characteristic data includes: the number of times of overthrowing ideas (for example: after browsing and then exiting the quinoa planting experience sharing forum, it indicates that no valuable content has been extracted, and the number of times of overthrowing ideas is recorded as 1 time), etc.; the standard characteristic data has a preset index value, and the standard characteristic data is the characteristic data that reflects the degree of maturity of the timing to assist the analysis expert in solving the analysis difficulty. For example: when the number of times of overthrowing ideas is 5 times, the corresponding index value is the degree of maturity of the timing for the standard characteristic data to assist the analysis expert in solving the analysis difficulty, for example: 3; Therefore, the index values are accumulated to obtain the trigger value.

[0141] An embodiment of the present invention provides a quinoa cultivation management method, as Figure 2 shown, including:

[0142] S1. Monitor the cultivation abnormal events in the quinoa cultivation site;

[0143] S2. Match an analysis expert for the cultivation abnormal event;

[0144] S3. Obtain the abnormal handling plan for the cultivation abnormal event decided by the analysis expert and execute the plan.

[0145] The quinoa cultivation management method further includes:

[0146] Identify the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormal event; assist the analysis expert in solving the analysis difficulties.

[0147] The identifying the analysis difficulties encountered by the analysis expert when analyzing the cultivation abnormal event includes:

[0148] When the analysis expert enters the quinoa cultivation site, obtain the movement trajectory generated by the analysis expert in the most recent preset time;

[0149] Based on the cultivation abnormal event and the preset site map corresponding to the quinoa cultivation site, generate an abnormal distribution map;

[0150] Represent the movement trajectory in the abnormal distribution map to obtain a map trajectory;

[0151] Determine the local trajectory that meets the local trajectory conditions from the map trajectory;

[0152] Obtain the start and end times of the local trajectory;

[0153] Obtain the first-person view video generated by the analysis expert during the time period between the start and end times;

[0154] Based on the first-person view video, determine the analysis difficulty;

[0155] Among them, the local trajectory conditions include:

[0156] The local trajectory completely represents that the analysis expert stays beside an abnormal area on the abnormal distribution map for more than a preset duration;

[0157] Or,

[0158] The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the abnormal distribution map for more than a preset number of times;

[0159] Or,

[0160] The local trajectory completely represents that the analysis expert turns back to an abnormal area on the abnormal distribution map.

[0161] Assisting the analysis expert in solving the analysis difficulty includes:

[0162] Obtaining the sequence of analysis operations performed by the analysis expert when solving the analysis difficulty;

[0163] Determining a solution idea based on the sequence of analysis operations;

[0164] Obtaining the trigger value of the solution idea;

[0165] When the trigger value is greater than or equal to a preset trigger threshold, obtaining a recommended solution idea for the analysis difficulty;

[0166] Determining an adaptable idea for the solution idea based on the recommended solution idea;

[0167] Determining a first idea node that meets the idea node conditions and the remaining second idea nodes from the adaptable ideas;

[0168] Displaying the adaptable ideas to the analysis expert; when displaying, the first idea node is continuously displayed, and the second idea nodes are first hidden and then continuously displayed when they meet the display conditions;

[0169] Among them, the idea node conditions include:

[0170] The first idea node is the head and tail idea nodes of the adaptable idea;

[0171] And / or,

[0172] The importance value of the first idea node is greater than or equal to a preset importance threshold corresponding to the trigger value;

[0173] Among them, the display conditions include:

[0174] The updated solution idea determined based on the updated analysis operation sequence completely represents that the analysis expert is about to analyze to the second idea node.

[0175] Obtaining the trigger value of the solution idea includes:

[0176] Performing a characterization process on the solution idea to obtain a plurality of feature data;

[0177] Matching each of the feature data with each standard feature data in a preset standard feature database respectively;

[0178] Whenever the feature data matches the standard feature data, obtaining the preset index value corresponding to the matched standard feature data;

[0179] Accumulatively calculating the index value to obtain the trigger value.

[0180] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A quinoa cultivation management system, characterized in that: include: A monitoring module, used to monitor abnormal cultivation events within the quinoa cultivation site; A matching module, used for matching analysis experts for the abnormal cultivation events; An execution module, used for obtaining the abnormality handling plan of the abnormal cultivation event decided by the analysis expert, and executing the plan; Also includes: An auxiliary module, used for identifying the analysis difficulties encountered by the analysis expert when analyzing the abnormal cultivation event; and also used for assisting the analysis expert in solving the analysis difficulties; The auxiliary module assists the analysis expert in solving the analysis difficulties, including: Obtaining a sequence of analysis operations performed by the analysis expert when solving the analysis difficulty; Determine a solution based on the analysis operation sequence; Obtaining a trigger value of the solution; When the trigger value is greater than or equal to a preset trigger threshold, obtaining a recommended solution to the analysis difficulty; Based on the recommended solution ideas, determine the connectable ideas of the solution ideas; Determine a first idea node and other second idea nodes that meet idea node conditions from the connectable ideas; Displaying the connectable ideas to the analysis expert; when displaying, the first idea node is continuously displayed, and the second idea node is first hidden and then continuously displayed when it meets the display conditions; The idea node conditions include: The first idea node is the first and last idea nodes of the connectable idea; and / or, The importance value of the first idea node is greater than or equal to a preset importance threshold corresponding to the trigger value; The display conditions include: The updated solution idea determined based on the updated analysis operation sequence fully represents that the analysis expert is about to analyze to the second idea node.

2. The quinoa cultivation management system according to claim 1, characterized in that: The auxiliary module identifies the analysis difficulties encountered by the analysis expert when analyzing the abnormal cultivation event, including: When the analysis expert enters the quinoa cultivation site, obtaining the movement trajectory of the analysis expert within a recent preset time; Generate an abnormal distribution map based on the abnormal cultivation event and a preset site map corresponding to the quinoa cultivation site; Representing the movement trajectory in the anomaly distribution map to obtain a map trajectory; Determining a local track that meets the local track condition from the map tracks; Obtaining the start and end times of the local trajectory; Acquire a first-person perspective video generated by the analysis expert during a time period between the start and end times; Determining the analysis difficulty based on the first perspective video; Wherein, the local trajectory conditions include: The local trajectory completely represents that the analysis expert stays beside an abnormal area on the abnormal distribution map for more than a preset time; or, The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the abnormal distribution map for more than a preset number of times; or, The local trajectory completely characterizes the analysis expert's return to an abnormal area on the abnormal distribution map.

3. The quinoa cultivation management system according to claim 1, characterized in that: The auxiliary module obtains the trigger value of the solution idea, including: Performing characterization processing on the solution idea to obtain a plurality of characteristic data; Matching each of the characteristic data with each of the standard characteristic data in a preset standard characteristic database; Whenever the feature data matches the standard feature data, a preset index value corresponding to the matching standard feature data is obtained; The indicator values ​​are cumulatively calculated to obtain the trigger value.

4. A quinoa cultivation and management method, characterized in that: include: Monitor abnormal events of cultivation within the quinoa cultivation site; Matching analysis experts for said abnormal cultivation events; Obtaining the abnormality handling plan for the abnormal cultivation event decided by the analysis expert, and executing the plan; Also includes: Identifying the analysis difficulties encountered by the analysis expert when analyzing the abnormal cultivation event; assisting the analysis expert in resolving the analysis difficulties; The assisting the analysis expert to solve the analysis difficulties includes: Obtaining a sequence of analysis operations performed by the analysis expert when solving the analysis difficulty; Determine a solution based on the analysis operation sequence; Obtaining a trigger value of the solution; When the trigger value is greater than or equal to a preset trigger threshold, obtaining a recommended solution to the analysis difficulty; Based on the recommended solution ideas, determine the connectable ideas of the solution ideas; Determine a first idea node and other second idea nodes that meet idea node conditions from the connectable ideas; Displaying the connectable ideas to the analysis expert; when displaying, the first idea node is continuously displayed, and the second idea node is first hidden and then continuously displayed when it meets the display conditions; The idea node conditions include: The first idea node is the first and last idea nodes of the connectable idea; and / or, The importance value of the first idea node is greater than or equal to a preset importance threshold corresponding to the trigger value; The display conditions include: The updated solution idea determined based on the updated analysis operation sequence fully represents that the analysis expert is about to analyze to the second idea node.

5. The quinoa cultivation and management method according to claim 4, characterized in that: The identification of the analytical difficulties encountered by the analytical expert when analyzing the abnormal cultivation event includes: When the analysis expert enters the quinoa cultivation site, obtaining the movement trajectory of the analysis expert within a recent preset time; Generate an abnormal distribution map based on the abnormal cultivation event and a preset site map corresponding to the quinoa cultivation site; Representing the movement trajectory in the anomaly distribution map to obtain a map trajectory; Determining a local track that meets the local track condition from the map tracks; Obtaining the start and end times of the local trajectory; Acquire a first-person perspective video generated by the analysis expert during a time period between the start and end times; Determining the analysis difficulty based on the first perspective video; Wherein, the local trajectory conditions include: The local trajectory completely represents that the analysis expert stays beside an abnormal area on the abnormal distribution map for more than a preset time; or, The local trajectory completely represents that the analysis expert travels back and forth between an abnormal area and a non-abnormal area on the abnormal distribution map for more than a preset number of times; or, The local trajectory completely characterizes the analysis expert's return to an abnormal area on the abnormal distribution map.

6. The quinoa cultivation and management method according to claim 4, characterized in that: The obtaining of the trigger value of the solution idea includes: Performing characterization processing on the solution idea to obtain a plurality of characteristic data; Matching each of the characteristic data with each of the standard characteristic data in a preset standard characteristic database; Whenever the feature data matches the standard feature data, a preset index value corresponding to the matching standard feature data is obtained; The indicator values ​​are cumulatively calculated to obtain the trigger value.

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