Crop seed collection method and system based on big data image analysis

Abstract

The invention provides a crop seed collection method and system based on big data image analysis, and the method comprises the steps: receiving a growth image, collected by a collection main body, of a crop arriving at a general survey point when the collection main body arrives at the general survey point; based on big data image analysis, variety classification is determined according to the growth image; based on a seed collection knowledge base, determining a seed collection rule according to variety classification; based on a seed collection rule, the auxiliary collection main body carries out corresponding seed collection on the crops arriving at the general survey point; and when the collection is completed, prompting the collection main body to go to the next general survey point. The method does not need to manually judge the variety classification of crops at a general survey point and determine the collection rule of the seeds of the crops according to the variety classification, reduces the labor cost, improves the seed collection efficiency, can also rapidly, accurately and comprehensively determine the collection rule when a collection worker encounters unfamiliar varieties of crops, improves the seed collection efficiency, and improves the collection efficiency of the crops. The problem of wrong collection of the seeds is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of big data image analysis, and in particular to a crop seed collection method and system based on big data image analysis. Background Art

[0002] At present, seed collection is required when conducting a census of crop germplasm resources.

[0003] However, when collecting seeds traditionally, collectors are often required to manually determine the variety classification of crops at the census point, and then determine the collection rules for the crop seeds based on the variety classification. This not only has a high labor cost, but also reduces the efficiency of seed collection. In addition, collectors may encounter unfamiliar varieties of crops, and they cannot quickly, accurately and comprehensively determine their collection rules, which delays their seed collection efficiency and may even lead to the problem of incorrect seed collection.

[0004] Therefore, a solution is urgently needed. Summary of the invention

[0005] One of the purposes of the present invention is to provide a crop seed collection method based on big data image analysis. Based on big data image analysis, the variety classification is determined according to the growth images of the crops collected by the collection subject at the census point. Based on the seed collection knowledge base, the seed collection rules are determined according to the variety classification. Based on the seed collection rules, the collection subject is assisted to collect the corresponding seeds of the crops arrived at the census point. When the collection is completed, the collection subject is prompted to go to the next census point without manual judgment of the variety classification of the crops at the census point. The collection rules of the crop seeds are determined according to the variety classification, which reduces labor costs and improves seed collection efficiency. When the collectors encounter unfamiliar varieties of crops, they can also quickly, accurately and comprehensively determine their collection rules, improve their seed collection efficiency, and avoid the problem of incorrect seed collection.

[0006] The crop seed collection method based on big data image analysis provided by an embodiment of the present invention includes:

[0007] When the collecting subject arrives at a census point, the growth images of the crops at the census point collected by the collecting subject are received; wherein the collecting subject at least includes: a seed collecting person and a robot;

[0008] Based on big data image analysis, the variety classification is determined according to the growth image;

[0009] Based on the seed collection knowledge base and according to the variety classification, the seed collection rules are determined;

[0010] Based on the seed collection rules, the auxiliary collection entities collect seeds of the crops that arrive at the census points accordingly;

[0011] When the collection is completed, the collection subject is prompted to go to the next census point.

[0012] Optionally, the crop seed collection method based on big data image analysis also includes:

[0013] Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules;

[0014] Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary time;

[0015] When it is identified as entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule;

[0016] The method of determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject includes:

[0017] Try to determine the target encirclement from the distribution of survey work locations; the target encirclement has a minimum encirclement of more than or equal to 2 collection subjects, and the diameter of the target encirclement does not exceed the preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collection subjects in the target encirclement are assisted; the rule association relationship indicates that the two collection subjects can assist each other when conducting the census;

[0018] When trying to determine it, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set;

[0019] Traverse each auxiliary object in the secondary auxiliary object set in turn;

[0020] During each traversal, the work assistance needs are determined based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rules based on which the assistance is based;

[0021] Determine the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects that are assisted based on the seed collection rule representation and can meet the work assistance requirements and are closest to the traversed assistance object;

[0022] The opportunities for generating secondary assistance include: when the traversed auxiliary objects actually generate work assistance needs;

[0023] Generating secondary assistance rules includes: controlling the flight of the UAV to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

[0024] Optionally, the controlling the drone to fly to prompt the best assistance execution object to meet the work assistance needs of the traversed auxiliary object includes:

[0025] Plan the first flight path for the drone to enter the target encirclement and head to the best assistance execution object;

[0026] Planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement;

[0027] The drone is controlled to fly along a first flight path, and when it arrives near the best assistance execution object, the drone is controlled to broadcast a first preset voice corresponding to the work assistance requirement;

[0028] After the broadcast is completed, the drone is controlled to fly along the second flight path. When it arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast the second preset voice corresponding to the work assistance needs. After the broadcast is completed, the flight is continued to be controlled.

[0029] Optionally, the constraints of the first flight path include:

[0030] The straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from each other and the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object;

[0031] The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points;

[0032] Among them, the constraints of the second flight path include:

[0033] The straight-line distances between a plurality of second path points on the second flight path that are spaced apart by a second interval and the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary object;

[0034] The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of each second path point; wherein the second target distance sequence includes: the distance sequence obtained by sorting the straight-line distance between each second path point and the current position of the traversed auxiliary object in the order of the corresponding second path points.

[0035] Optionally, the first interval and the second interval are each negatively correlated with the density of personnel in the target encirclement.

[0036] Optionally, the decreasing trend of the distance in the sequence of the first standard distance sequence is greater than the decreasing trend of the distance in the sequence of the second standard distance sequence.

[0037] The crop seed collection system based on big data image analysis provided by the embodiment of the present invention includes:

[0038] A receiving module is used to receive the growth images of the crops collected by the collecting subject at a census point when the collecting subject arrives at the census point; wherein the collecting subject at least includes: a seed collecting person and a robot;

[0039] The first determination module is used to determine the variety classification based on the growth image based on big data image analysis;

[0040] The second determination module is used to determine the seed collection rules according to the variety classification based on the seed collection knowledge base;

[0041] An auxiliary module is used to assist the collection subject to collect seeds of crops that arrive at the census point according to the seed collection rules;

[0042] The prompt module is used to prompt the collection subject to go to the next census point when the collection is completed.

[0043] Optional, crop seed collection system based on big data image analysis, also includes:

[0044] Secondary auxiliary modules for:

[0045] Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules;

[0046] Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary time;

[0047] When it is identified as entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule;

[0048] The method of determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject includes:

[0049] Try to determine the target encirclement from the distribution of survey work locations; the target encirclement has a minimum encirclement of more than or equal to 2 collection subjects, and the diameter of the target encirclement does not exceed the preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collection subjects in the target encirclement are assisted; the rule association relationship indicates that the two collection subjects can assist each other when conducting the census;

[0050] When trying to determine it, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set;

[0051] Traverse each auxiliary object in the secondary auxiliary object set in turn;

[0052] During each traversal, the work assistance needs are determined based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rules based on which the assistance is based;

[0053] Determine the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects that are assisted based on the seed collection rule representation and can meet the work assistance requirements and are closest to the traversed assistance object;

[0054] The opportunities for generating secondary assistance include: when the traversed auxiliary objects actually generate work assistance needs;

[0055] Generating secondary assistance rules includes: controlling the flight of the UAV to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

[0056] Optionally, the secondary assistance module controls the flight of the drone to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance object, including:

[0057] Plan the first flight path for the drone to enter the target encirclement and head to the best assistance execution object;

[0058] Planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement;

[0059] The drone is controlled to fly along a first flight path, and when it arrives near the best assistance execution object, the drone is controlled to broadcast a first preset voice corresponding to the work assistance requirement;

[0060] After the broadcast is completed, the drone is controlled to fly along the second flight path. When it arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast the second preset voice corresponding to the work assistance needs. After the broadcast is completed, the flight is continued to be controlled.

[0061] Optionally, the constraints of the first flight path include:

[0062] The straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from each other and the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object;

[0063] The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points;

[0064] Among them, the constraints of the second flight path include:

[0065] The straight-line distances between a plurality of second path points on the second flight path that are spaced apart by a second interval and the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary object;

[0066] The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of each second path point; wherein the second target distance sequence includes: the distance sequence obtained by sorting the straight-line distance between each second path point and the current position of the traversed auxiliary object in the order of the corresponding second path points.

[0067] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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 of the present invention. In the accompanying drawings:

[0070] Figure 1 Schematic diagram of a crop seed collection method based on big data image analysis in an embodiment of the present invention;

[0071] Figure 2 It is a schematic diagram of a crop seed collection system based on big data image analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The preferred embodiments of the present invention are described below in conjunction with 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.

[0073] The present invention provides a method for collecting crop seeds based on big data image analysis. Figure 1 As shown, including:

[0074] S1. When a collection subject arrives at a census point, receiving the growth images of the crops at the census point collected by the collection subject; wherein the collection subject at least includes: a seed collection person and a robot;

[0075] In S1, the survey point can be an area where a survey of crop germplasm resources is required, etc. When the collection subject arrives at the survey point, the growth images of the crops at the survey point will be collected; when the collection subject is a seed collector, the collection can be carried out through a mobile phone or other equipment carried with them; when the collection subject is a robot, the collection can be carried out through the camera equipped with it;

[0076] S2. Based on big data image analysis, determine the variety classification according to the growth image;

[0077] In S2, performing big data image analysis refers to collecting a large number of growth images of different crops in advance based on big data technology, comparing the growth images collected by the collection subject with the growth images collected in advance, so as to determine the variety classification represented by the growth images collected by the collection subject;

[0078] S3, based on the seed collection knowledge base and according to the variety classification, determine the seed collection rules;

[0079] In S3, there are seed collection rules corresponding to different variety classifications in the seed collection knowledge base, and the seed collection rules indicate how to collect seeds of crops of corresponding variety classifications;

[0080] S4. Based on the seed collection rules, assist the collection subject to collect seeds of crops that arrive at the census point;

[0081] In S4, based on the seed collection rules, the collection subject can be assisted to collect seeds of the crops that arrive at the census point accordingly;

[0082] S5. When the collection is completed, the collection subject is prompted to go to the next census point.

[0083] In S5, when the collection subject has completed the collection, it needs to go to the next census point, and the collection subject is prompted to go to the next census point.

[0084] The present application is based on big data image analysis. It determines the variety classification according to the growth images of the crops at the census points collected by the collection subject. It determines the seed collection rules based on the seed collection knowledge base and the variety classification. Based on the seed collection rules, it assists the collection subject in collecting the corresponding seeds of the crops at the census points. When the collection is completed, the collection subject is prompted to go to the next census point without manual judgment of the variety classification of the crops at the census point. The collection rules of the seeds of the crops are determined according to the variety classification, which reduces the labor cost and improves the seed collection efficiency. When the collectors encounter unfamiliar varieties of crops, they can also quickly, accurately and comprehensively determine their collection rules, improve their seed collection efficiency and avoid the problem of incorrect seed collection.

[0085] In one embodiment, the crop seed collection method based on big data image analysis further includes:

[0086] S6. Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules;

[0087] In S6, the survey work location distribution of the collection subject is the location distribution of different collection subjects when conducting the crop germplasm resource survey; the secondary auxiliary object set includes the collection subjects for which secondary assistance is provided for seed collection; the secondary assistance timing is the timing when secondary assistance is required for each collection subject in the secondary auxiliary object set; the secondary assistance rule indicates how to provide secondary assistance to each collection subject in the secondary auxiliary object set;

[0088] S7, tracking and identifying whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary opportunity;

[0089] S8. When it is identified as an entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule;

[0090] In S8, when it is identified that each auxiliary object in the secondary auxiliary object set is in a secondary auxiliary opportunity, secondary assistance is performed on each auxiliary object accordingly based on the secondary auxiliary rule;

[0091] Wherein, the step S6, determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject, includes:

[0092] S61, trying to determine a target encirclement from the distribution of survey work locations; wherein the target encirclement has a minimum encirclement of more than or equal to two collecting subjects, and the diameter of the target encirclement does not exceed a preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collecting subjects in the target encirclement are assisted; the rule association relationship indicates that the two collecting subjects can assist each other when performing the survey;

[0093] In S61, setting the minimum encirclement of the target encirclement to be greater than or equal to 2 collecting subjects can enable the collecting subjects in the target encirclement to assist each other; the diameter threshold can be, for example, 25 meters; setting the diameter of the target encirclement not exceeding the preset diameter threshold can ensure that the area of ​​the target encirclement is not too large, which is convenient for using a drone to perform secondary assistance on the diameter of the target encirclement not exceeding the preset diameter threshold, and can also ensure that the distance between the collecting subjects in the target encirclement is not far, which is convenient for mutual assistance between them; the rule association relationship can be that the assisted instructions use the same seed collection tool, the assisted instructions perform the same seed collection operation, etc.; setting a rule association relationship between the seed collection rules based on which the two collecting subjects in the target encirclement are assisted can enable the collecting subjects in the target encirclement to have the need to assist each other;

[0094] S62, when the attempt is to determine, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set;

[0095] In S62, when trying to determine the target encirclement, the collection subjects in the target encirclement can be secondary assisted, and all of them are completed as auxiliary objects and combined into a secondary auxiliary object set;

[0096] S63, traversing each auxiliary object in the secondary auxiliary object set in turn;

[0097] S64, during each traversal, based on the first work preparation portrait of the traversed auxiliary object and the seed collection rule based on which the auxiliary object is assisted, determining the work assistance requirement;

[0098] In S64, the first work preparation portrait at least includes: the seed collection tools carried when conducting the census work, the seed collection experience possessed, etc.; based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rule based on which the auxiliary object is assisted, the work assistance requirement can be determined, for example: the seed collection rule based on which the auxiliary object is assisted indicates that a certain sub-collection tool is needed, and the first work preparation portrait reflects that the auxiliary object does not carry the seed collection tool, then the work assistance requirement is that the seed collection tool needs to be provided;

[0099] S65, determining the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects based on the seed collection rule, other assistance objects that can meet the work assistance requirements and are closest to the traversed assistance object among the current second work preparation portrait representation;

[0100] In S65, the best assistance execution object is the other auxiliary object closest to the traversed auxiliary object among other auxiliary objects that can meet the work assistance needs in the target encirclement; the seed collection rule based on which the assistance is based and the current second work preparation portrait representation can meet the work assistance needs, which means, for example: the work assistance demand is that a certain sub-collection tool needs to be provided, if the seed collection rule based on which the assistance is based represents that other auxiliary objects do not need to use the seed collection tool, and the second work preparation portrait represents that other auxiliary objects carry the seed collection tool, then the work assistance demand is met;

[0101] S66, generating secondary assistance opportunities includes: when the traversed assistance object actually generates work assistance needs;

[0102] S67, generating secondary assistance rules includes: controlling the flight of the drone to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

[0103] In S67, when performing secondary assistance, the drone is controlled to fly to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects, so as to assist the assistance objects to provide timely mutual assistance.

[0104] The embodiments of the present invention achieve the following beneficial effects:

[0105] Secondary assistance to auxiliary objects makes the crop seed collection process more efficient, intelligent and automated;

[0106] Based on the location distribution of the census work of the collection subject, the secondary auxiliary object set, secondary auxiliary timing and secondary auxiliary rules are accurately determined, ensuring the real-time and accurate identification of auxiliary objects and avoiding invalid or unnecessary assistance that may occur in traditional methods;

[0107] In the process of determining the target encirclement, the minimum encirclement is set to achieve effective area range control, so that the execution of auxiliary actions will not be too dispersed or delayed, further optimizing resource allocation and time utilization efficiency;

[0108] When determining the collection subjects within the target encirclement, it is ensured that there are regular associations between these subjects, ensuring that assistance is only provided when there is a need for mutual assistance, which greatly improves the efficiency of assistance and avoids the waste of resources;

[0109] By analyzing the first work preparation portrait of each collection subject, its work needs can be dynamically detected, such as lack of necessary tools or experience, and appropriate assistance objects can be matched in time. This refined demand matching ensures that each assistance can be accurate and in place, which not only improves work efficiency but also reduces manual intervention.

[0110] The generation of secondary assistance rules controls the flight path of the drone and promptly prompts the best assistance execution object, so that the assistance operation can accurately reach the target object; this method avoids the errors and timeliness problems of manual intervention and improves the automation and collaborative efficiency of the entire assistance process;

[0111] It adopts a flexible drone guidance and execution mechanism, which can intelligently adjust the execution strategy according to the on-site situation, ensuring that the mutual assistance between various collection entities can be carried out efficiently and in real time.

[0112] In one embodiment, in S67, controlling the drone to fly so as to prompt the best assistance execution object to meet the work assistance requirements of the traversed auxiliary object includes:

[0113] S671, planning the UAV to fly into the target encirclement and go to the first flight path next to the best assistance execution object;

[0114] In S671, going to the best assistance execution object means that the drone approaches the best assistance execution object, and specifically, the approach distance to the best assistance execution object can be set in advance by the technician;

[0115] S672, planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement;

[0116] In S671, similarly, going to the traversed auxiliary object means that the drone approaches the traversed auxiliary object, and specifically, the approach distance to the traversed auxiliary object can be set in advance by the technician;

[0117] S673, controlling the drone to fly along the first flight path, and when arriving next to the best assistance execution object, controlling the drone to broadcast a first preset voice corresponding to the work assistance requirement;

[0118] In S673, the first preset voice is a prompt voice for prompting the best help execution object to meet the work help requirement, for example: "Make a certain sub-collection tool available for others to use";

[0119] S674, after the broadcast is completed, the drone is controlled to fly along the second flight path. When the drone arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast a second preset voice corresponding to the work assistance requirement. After the broadcast is completed, the drone continues to control the flight;

[0120] In S674, after the first preset voice is broadcasted, the best help execution object will know that it needs to meet the work help demand, and it will pay attention to the future flight of the drone, and control the drone to fly along the second flight path. At this time, the best help execution object will know that the work help demand is generated by the traversed auxiliary object, control the drone to pause flying, and broadcast the second preset voice corresponding to the work help demand. The second preset voice is a voice prompting the traversed auxiliary object to accept that the work help demand has been met, for example: "Prepare to receive a certain sub-collection tool"; after the second preset voice is broadcasted, the drone is controlled to continue flying along the second flight path, driving out of the target encirclement, and completing a complete secondary assistance;

[0121] Among them, the constraints of the first flight path include:

[0122] Constraint A1: the straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object;

[0123] In constraint A1, the first path point includes the starting point of the first flight path; the first distance threshold is the distance at which the UAV flight will not interfere with the seed collection work of the collection subject, which can be obtained by technical personnel through relevant tests in advance; it is ensured that the straight-line distance between each first path point and the current position of the first interference-free object is greater than or equal to the first distance threshold, so that the UAV flight enters the target encirclement and goes to the best assistance execution object. The process will not interfere with the first interference-free object;

[0124] Constraint A2: The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points;

[0125] In constraint A2, there is a straight-line distance between each first path point and the current position of the best assisting execution object. The straight-line distances are sorted in the order of the corresponding first path points to obtain a distance sequence, which is used as the first target distance sequence. There are multiple decreasing distances in the first standard distance sequence. When the first target distance sequence matches the first standard distance sequence, it can be ensured that when the drone arrives at the first path point in turn, it will evenly and gradually approach the best assisting execution object, so that the best assisting execution object can hear that the drone is coming for it, and has the purpose of giving it relevant prompts, so that it slowly notices the drone.

[0126] Among them, the constraints of the second flight path include:

[0127] Constraint B1: the straight-line distances between a plurality of second path points on the second flight path that are spaced by a second interval from the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary objects;

[0128] In constraint B1, the second path point includes the starting point of the second flight path; the second distance threshold is the distance at which the drone flight will not interfere with the seed collection work of the collection subject, and the second distance threshold can be set to be greater than the first distance threshold, because the probability of the drone interfering with the second interference-free object when it moves from the best assisting execution object to the traversed auxiliary object and drives out of the target encirclement is relatively high, and a larger distance should be maintained from it; it is ensured that the straight-line distance between each second path point and the current position of the second interference-free object is greater than or equal to the second distance threshold, so that the drone will not interfere with the second interference-free object to a greater extent when it moves from the best assisting execution object to the traversed auxiliary object and drives out of the target encirclement;

[0129] Constraint B2: The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of the second path points; wherein the second target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the second path points and the current position of the traversed auxiliary object in the order of the corresponding second path points;

[0130] In constraint B2, there is also a straight-line distance between each second path point and the current position of the traversed auxiliary object. The straight-line distances are sorted in the order of the corresponding second path points to obtain a distance sequence, which is used as the second target distance sequence. There are multiple decreasing distances in the second standard distance sequence. When the second target distance sequence matches the second standard distance sequence, it can be ensured that when the drone arrives at the second path point in turn, it will evenly and gradually approach the traversed auxiliary object, so that the traversed auxiliary object can hear that the drone is coming for it, and has the purpose of giving it relevant prompts, so that it will gradually notice the drone.

[0131] Among them, the first interval and the second interval are respectively negatively correlated with the population density of the target encirclement; the greater the population density of the target encirclement, the more it is necessary to ensure that the drone cannot interfere with the corresponding first interference-free objects and second interference-free objects when flying along the first flight path and the second flight path, and the more frequent distance guarantee settings are required, and the smaller the first interval and the second interval are.

[0132] The decreasing trend of the distance in the sequence of the first standard distance sequence is greater than that of the second standard distance sequence; the decreasing trend of the distance in the sequence refers to the average value of the values ​​of the distances in the sequence that decrease successively; when flying along the first flight path, it is necessary to prompt the best helping execution object as soon as possible, and when flying along the second flight path, it is necessary to guide the best helping execution object to whom it needs to help, so the decreasing trend of the distance in the sequence of the first standard distance sequence is set to be greater than the decreasing trend of the distance in the sequence of the second standard distance sequence.

[0133] Generally, in actual crop seed collection sites, due to the complex environment of the site and the seed collectors' focus on seed collection work, when they need help with their work, it is rather cumbersome, and therefore, secondary assistance is required.

[0134] Therefore, the embodiments of the present invention achieve the following beneficial effects:

[0135] Through precise flight path planning and voice prompts, drones can efficiently deliver mission requirements to the correct objects, reduce personnel communication costs, and avoid low work efficiency due to misunderstandings or information delays. Tasks such as crop seed collection usually require collaboration among multiple people. Using drones to assist in precise task matching can effectively improve work efficiency.

[0136] The flight path of the drone is constrained, which not only ensures the assistance to key target objects, but also avoids interference with other operators. This design takes into account the complexity and personnel distribution of the crop seed collection site, and can minimize the interference of drones to other collectors during flight, thus maintaining order at the work site.

[0137] Traditional manual collaboration and tool delivery methods may be affected by factors such as human fatigue and environmental changes. However, drones can achieve autonomous collaboration in more complex environments through automated flight path control and intelligent voice broadcasting, reducing manual intervention and the probability of human error. The flight path of drones can be adjusted in real time according to the on-site situation, improving the system's adaptive ability.

[0138] By linking the interval of the flight path with the density of people in the target encirclement, the system can dynamically adjust the flight strategy, which makes the technology more flexible in practical applications. For example, in areas with dense population, the interval of the flight path will be shortened to reduce interference with other operators; in open areas, the interval of the path can be relatively increased to ensure that the drone can quickly approach the target object. This flexible adjustment mechanism enables the drone to work effectively in different scenarios.

[0139] The auxiliary work of drones not only improves work efficiency, but also enhances user experience through precise voice prompts; collectors can clearly know when to pay attention to the drone's prompts, reducing the trouble caused by unclear information transmission or misunderstanding; especially in complex farmland environments, seed collectors usually need to focus on the task at hand, and the drone's voice prompts can effectively guide them to complete the task and reduce their workload.

[0140] The collection of crop germplasm resources and specimen preparation are still a very critical step, which has many functions:

[0141] Specimens provide morphological characteristics of crops, helping experts to accurately classify and identify them;

[0142] Specimens record the morphological characteristics of crops (such as leaves, flowers, fruits, roots, etc.), providing important basis for subsequent research and species confirmation;

[0143] Through specimens and sampled materials (such as seeds, tissues, etc.), experts can conduct genetic identification, evaluate genetic diversity and germplasm characteristics, and provide support for breeding and genetic research;

[0144] Specimen collection is often accompanied by growth environment data, such as soil type and climatic conditions, which helps understand the ecological adaptability of crops.

[0145] In the process of germplasm resource collection, it is very necessary to ensure the scientific preparation and standardized preservation of specimens, which can effectively support subsequent research and utilization. Therefore, specimen preparation is not only an effective record of crop germplasm, but also an important guarantee for its protection, utilization and verification.

[0146] The embodiment of the present invention provides a crop seed collection system based on big data image analysis, such as Figure 2 As shown, including:

[0147] The receiving module 1 is used for receiving the growth images of the crops collected by the collecting subject at the enumeration point when the collecting subject arrives at the enumeration point; wherein the collecting subject at least includes: a seed collecting person and a robot;

[0148] The first determination module 2 is used to determine the variety classification based on the growth image based on big data image analysis;

[0149] The second determination module 3 is used to determine the seed collection rules based on the seed collection knowledge base and according to the variety classification;

[0150] Auxiliary module 4, used to assist the collection subject to collect seeds of crops arrived at the census point accordingly based on the seed collection rules;

[0151] The prompt module 5 is used to prompt the collection subject to go to the next census point when the collection is completed.

[0152] The crop seed collection system based on big data image analysis also includes:

[0153] Secondary auxiliary modules for:

[0154] Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules;

[0155] Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary time;

[0156] When it is identified as entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule;

[0157] The method of determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject includes:

[0158] Try to determine the target encirclement from the distribution of survey work locations; the target encirclement has a minimum encirclement of more than or equal to 2 collection subjects, and the diameter of the target encirclement does not exceed the preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collection subjects in the target encirclement are assisted; the rule association relationship indicates that the two collection subjects can assist each other when conducting the census;

[0159] When trying to determine it, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set;

[0160] Traverse each auxiliary object in the secondary auxiliary object set in turn;

[0161] During each traversal, the work assistance needs are determined based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rules based on which the assistance is based;

[0162] Determine the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects that are assisted based on the seed collection rule representation and can meet the work assistance requirements and are closest to the traversed assistance object;

[0163] The opportunities for generating secondary assistance include: when the traversed auxiliary objects actually generate work assistance needs;

[0164] Generating secondary assistance rules includes: controlling the flight of the UAV to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

[0165] The secondary assistance module controls the flight of the drone to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance object, including:

[0166] Plan the first flight path for the drone to enter the target encirclement and head to the best assistance execution object;

[0167] Planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement;

[0168] The drone is controlled to fly along a first flight path, and when it arrives near the best assistance execution object, the drone is controlled to broadcast a first preset voice corresponding to the work assistance requirement;

[0169] After the broadcast is completed, the drone is controlled to fly along the second flight path. When it arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast the second preset voice corresponding to the work assistance needs. After the broadcast is completed, the flight is continued to be controlled.

[0170] The constraints for the first flight path include:

[0171] The straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from each other and the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object;

[0172] The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points;

[0173] Among them, the constraints of the second flight path include:

[0174] The straight-line distances between a plurality of second path points on the second flight path that are spaced apart by a second interval and the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary object;

[0175] The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of each second path point; wherein the second target distance sequence includes: the distance sequence obtained by sorting the straight-line distance between each second path point and the current position of the traversed auxiliary object in the order of the corresponding second path points.

[0176] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for collecting crop seeds based on big data image analysis, characterized in that: include: When the collecting subject arrives at a census point, receiving the growth images of the crops at the census point collected by the collecting subject; wherein the collecting subject at least includes: a seed collecting person and a robot; Based on big data image analysis, the variety classification is determined according to the growth image; Based on the seed collection knowledge base and according to the variety classification, the seed collection rules are determined; Based on the seed collection rules, the auxiliary collection entities collect seeds of the crops that arrive at the census points accordingly; When the collection is completed, the collection subject is prompted to go to the next census point.

2. The crop seed collection method based on big data image analysis according to claim 1, characterized in that: Also includes: Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules; Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary time; When it is identified as entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule; The method of determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject includes: Try to determine the target encirclement from the distribution of survey work locations; the target encirclement has a minimum encirclement of more than or equal to 2 collection subjects, and the diameter of the target encirclement does not exceed the preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collection subjects in the target encirclement are assisted; the rule association relationship indicates that the two collection subjects can assist each other when conducting the survey; When trying to determine it, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set; Traverse each auxiliary object in the secondary auxiliary object set in turn; During each traversal, the work assistance needs are determined based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rules based on which the assistance is based; Determine the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects that are assisted based on the seed collection rule representation and can meet the work assistance requirements and are closest to the traversed assistance object; The opportunities for generating secondary assistance include: when the traversed auxiliary objects actually generate work assistance needs; Generating secondary assistance rules includes: controlling the flight of the UAV to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

3. The crop seed collection method based on big data image analysis according to claim 2, characterized in that: The controlling the drone to fly to prompt the best assistance execution object to meet the work assistance needs of the traversed auxiliary object includes: Plan the first flight path for the drone to enter the target encirclement and head to the best assistance execution object; Planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement; The drone is controlled to fly along a first flight path, and when it arrives near the best assistance execution object, the drone is controlled to broadcast a first preset voice corresponding to the work assistance requirement; After the broadcast is completed, the drone is controlled to fly along the second flight path. When it arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast the second preset voice corresponding to the work assistance needs. After the broadcast is completed, the flight is continued to be controlled.

4. The crop seed collection method based on big data image analysis according to claim 3, characterized in that: The constraints for the first flight path include: The straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from each other and the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object; The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points; Among them, the constraints of the second flight path include: The straight-line distances between a plurality of second path points on the second flight path that are spaced apart by a second interval and the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary object; The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of each second path point; wherein the second target distance sequence includes: the distance sequence obtained by sorting the straight-line distance between each second path point and the current position of the traversed auxiliary object in the order of the corresponding second path points.

5. The crop seed collection method based on big data image analysis according to claim 4, characterized in that: The first interval and the second interval are negatively correlated with the population density of the target encirclement respectively.

6. The crop seed collection method based on big data image analysis according to claim 4, characterized in that: The decreasing trend of the distance in the sequence of the first standard distance sequence is greater than the decreasing trend of the distance in the sequence of the second standard distance sequence.

7. A crop seed collection system based on big data image analysis, characterized in that: include: A receiving module is used to receive the growth images of the crops collected by the collecting subject at a census point when the collecting subject arrives at the census point; wherein the collecting subject at least includes: a seed collecting person and a robot; The first determination module is used to determine the variety classification based on the growth image based on big data image analysis; The second determination module is used to determine the seed collection rules according to the variety classification based on the seed collection knowledge base; An auxiliary module is used to assist the collection subject to collect seeds of crops that arrive at the census point according to the seed collection rules; The prompt module is used to prompt the collection subject to go to the next census point when the collection is completed.

8. The crop seed collection system based on big data image analysis according to claim 7, characterized in that: Also includes: Secondary auxiliary modules for: Based on the census work location distribution of the collection subject, determine the secondary assistance object set, secondary assistance timing and secondary assistance rules; Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary time; When it is identified as entry, secondary assistance is performed on each auxiliary object based on the secondary assistance rule; The method of determining the secondary assistance object set, secondary assistance timing and secondary assistance rules based on the census work location distribution of the collection subject includes: Try to determine the target encirclement from the distribution of survey work locations; the target encirclement has a minimum encirclement of more than or equal to 2 collection subjects, and the diameter of the target encirclement does not exceed the preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which the two collection subjects in the target encirclement are assisted; the rule association relationship indicates that the two collection subjects can assist each other when conducting the survey; When trying to determine it, all the collected subjects within the target encirclement are taken as auxiliary objects and combined into a secondary auxiliary object set; Traverse each auxiliary object in the secondary auxiliary object set in turn; During each traversal, the work assistance needs are determined based on the current first work preparation portrait of the traversed auxiliary object and the seed collection rules based on which the assistance is based; Determine the best assistance execution object from the target encirclement; wherein the best assistance execution object includes: other assistance objects in the target encirclement other than the traversed assistance object, other assistance objects that are assisted based on the seed collection rule representation and can meet the work assistance requirements and are closest to the traversed assistance object; The opportunities for generating secondary assistance include: when the traversed auxiliary objects actually generate work assistance needs; Generating secondary assistance rules includes: controlling the flight of the UAV to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance objects.

9. The crop seed collection system based on big data image analysis according to claim 8, characterized in that: The secondary assistance module controls the flight of the drone to prompt the best assistance execution object to meet the work assistance needs of the traversed assistance object, including: Plan the first flight path for the drone to enter the target encirclement and head to the best assistance execution object; Planning a second flight path for the UAV to fly from the best assisting execution object to the traversed auxiliary object and out of the target encirclement; The drone is controlled to fly along a first flight path, and when it arrives near the best assistance execution object, the drone is controlled to broadcast a first preset voice corresponding to the work assistance requirement; After the broadcast is completed, the drone is controlled to fly along the second flight path. When it arrives at the traversed auxiliary object, the drone is controlled to pause flying and broadcast the second preset voice corresponding to the work assistance needs. After the broadcast is completed, the flight is continued to be controlled.

10. The crop seed collection system based on big data image analysis according to claim 9, characterized in that: The constraints for the first flight path include: The straight-line distances between a plurality of first path points on the first flight path that are spaced by a first interval from each other and the current position of the first interference-free object are all greater than or equal to a first distance threshold; wherein the first interference-free object includes: all auxiliary objects in the target encirclement except the best assisting execution object; The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of the first path points; wherein the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between the first path points and the current position of the best assisting execution object in the order of the corresponding first path points; Among them, the constraints of the second flight path include: The straight-line distances between a plurality of second path points on the second flight path that are spaced apart by a second interval and the current position of the second interference-free object are all greater than or equal to a second distance threshold; wherein the second interference-free object includes: all auxiliary objects in the target encirclement except the traversed auxiliary object; The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of each second path point; wherein the second target distance sequence includes: the distance sequence obtained by sorting the straight-line distance between each second path point and the current position of the traversed auxiliary object in the order of the corresponding second path points.

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