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

Abstract

The application provides a crop seed collection method and system based on big data image analysis, wherein the method comprises: when a collection subject arrives at a survey point, receiving a growth image of crops at the arrived survey point collected by the collection subject; based on big data image analysis, determining a variety classification according to the growth image; based on a seed collection knowledge base, determining a seed collection rule according to the variety classification; based on the seed collection rule, assisting the collection subject to collect seeds of the crops at the arrived survey point accordingly; and when the collection is completed, prompting the collection subject to go to the next survey point. The variety classification of the crops at the survey point does not need to be determined by manual judgment, and the seed collection rule of the crops is determined according to the variety classification, thereby reducing the labor cost and improving the seed collection efficiency. When the collection personnel encounter crops of an unfamiliar variety, the collection rule can also be quickly, accurately and comprehensively determined, the seed collection efficiency is improved, and the problem of incorrect seed collection is avoided.

Description

TECHNICAL FIELD

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

[0002] At present, when conducting crop germplasm resource survey, seed collection needs to be performed.

[0003] However, when performing seed collection in a traditional manner, a collection personnel needs to manually judge the variety classification of the crops at the survey point, and then determine the seed collection rule of the crops according to the variety classification. This not only has a large labor cost, but also reduces the seed collection efficiency. In addition, the collection personnel may encounter crops of an unfamiliar variety, and cannot quickly, accurately and comprehensively determine the collection rule thereof, which delays the seed collection efficiency, and even may cause the problem of incorrect seed collection.

[0004] Therefore, a solution is urgently needed. SUMMARY

[0005] One of the purposes of the present application is to provide a crop seed collection method based on big data image analysis. Based on big data image analysis, the growth image of the crops at the survey point collected by the collection subject is determined to determine the variety classification. Based on the seed collection knowledge base, the seed collection rule is determined according to the variety classification. Based on the seed collection rule, the collection subject is assisted to collect the seeds of the crops at the survey point. When the collection is completed, the collection subject is prompted to go to the next survey point. The variety classification of the crops at the survey point does not need to be manually judged, and the seed collection rule of the crops is determined according to the variety classification. The labor cost is reduced, and the seed collection efficiency is improved. When the collection personnel encounter crops of an unfamiliar variety, the collection rule thereof can also be quickly, accurately and comprehensively determined, the seed collection efficiency is improved, and the problem of incorrect seed collection is avoided.

[0006] The crop seed collection method based on big data image analysis provided by the embodiments of the present application comprises:

[0007] When the collection subject arrives at a survey point, the growth image of the crops at the survey point collected by the collection subject is received. The collection subject at least includes a seed collection personnel 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, the seed collection rule is determined according to the variety classification.

[0010] Based on the seed collection rule, the collection subject is assisted to collect the seeds of the crops at the survey point.

[0011] When the data collection is complete, the data collection entity will be prompted to proceed to the next census site.

[0012] Optional methods for collecting crop seeds based on big data image analysis also include:

[0013] Based on the location distribution of the census work of the data collection subjects, the set of objects for secondary assistance, the timing of secondary assistance, and the rules for secondary assistance are determined;

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

[0015] When an entry is detected, secondary assistance is applied to each auxiliary object based on the secondary assistance rules.

[0016] The determination of 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] An attempt is made to determine the target encirclement from the distribution of census work locations; wherein the target encirclement has a minimum of two collection subjects, 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 on which each pair of collection subjects within the target encirclement is assisted; the rule association relationship indicates that the two collection subjects can cooperate when conducting census work;

[0018] When the target is identified, all the collected entities within the target encirclement are treated as auxiliary objects and combined into a secondary auxiliary object set.

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

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

[0021] The optimal help execution object is determined from the target encirclement; wherein, the optimal help execution object includes: the other auxiliary object that is closest to the traversed auxiliary object among the other auxiliary objects in the target encirclement whose seed collection rule representation can satisfy the work help requirements, excluding the traversed auxiliary objects;

[0022] The timing for generating secondary assistance includes: when the traversed assistance object actually generates a need for work assistance;

[0023] The generation of secondary auxiliary rules includes: controlling the drone's flight to prompt the best assist execution object to meet the work assistance requirements of the traversed auxiliary objects.

[0024] Optionally, the controlling the UAV to fly to prompt the optimal help execution object to meet the work help demand of the auxiliary object traversed to, comprises:

[0025] planning a first flight path for the UAV to fly into the target encirclement and to the optimal help execution object;

[0026] planning a second flight path for the UAV to fly from the optimal help execution object to the auxiliary object traversed to and out of the target encirclement;

[0027] controlling the UAV to fly along the first flight path, when reaching the optimal help execution object, controlling the UAV to broadcast a first preset voice corresponding to the work help demand;

[0028] after the broadcasting is completed, controlling the UAV to fly along the second flight path, when reaching the auxiliary object traversed to, controlling the UAV to pause the flight, and broadcast a second preset voice corresponding to the work help demand, and after the broadcasting is completed, continue to control the flight.

[0029] Optionally, the constraint of the first flight path comprises:

[0030] the straight line distance between each of the first path points on the first flight path and the current position of the first interference-free object is greater than or equal to the first distance threshold; wherein the first interference-free object comprises all the auxiliary objects in the target encirclement except the optimal help 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 comprises a distance sequence obtained by sorting the straight line distance between each of the first path points and the current position of the optimal help execution object in the corresponding first path point order;

[0032] wherein the constraint of the second flight path comprises:

[0033] the straight line distance between each of the second path points on the second flight path and the current position of the second interference-free object is greater than or equal to the second distance threshold; wherein the second interference-free object comprises all the auxiliary objects in the target encirclement except the auxiliary object traversed to;

[0034] 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 comprises a distance sequence obtained by sorting the straight line distance between each of the second path points and the current position of the auxiliary object traversed to in the corresponding second path point order.

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

[0036] Optionally, a distance decreasing trend in the sequence of the first standard distance sequence is greater than a distance decreasing trend 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 application comprises:

[0038] The receiving module is configured to receive a growth image of a crop at a survey point collected by a collection subject when the collection subject arrives at the survey point, wherein the collection subject at least comprises a seed collection personnel and a robot.

[0039] The first determining module is configured to determine a variety classification based on big data image analysis according to the growth image.

[0040] The second determining module is configured to determine a seed collection rule based on the variety classification based on a seed collection knowledge base.

[0041] The auxiliary module is configured to assist the collection subject in performing corresponding seed collection on the crop at the survey point based on the seed collection rule.

[0042] The prompting module is configured to prompt the collection subject to go to a next survey point when the collection is completed.

[0043] Optionally, the crop seed collection system based on big data image analysis further comprises:

[0044] The secondary auxiliary module is configured to:

[0045] determine a secondary auxiliary object set, a secondary auxiliary time, and a secondary auxiliary rule based on a survey work position distribution of the collection subject;

[0046] track and identify whether each auxiliary object in the secondary auxiliary object set enters the secondary auxiliary time;

[0047] when it is identified that each auxiliary object enters the secondary auxiliary time, perform corresponding secondary assistance on each auxiliary object based on the secondary auxiliary rule;

[0048] The secondary auxiliary module is configured to:

[0049] determine a target enclosure from the census work position distribution; wherein the target enclosure minimally encloses more than or equal to two collection subjects, and a diameter of the target enclosure does not exceed a preset diameter threshold, and a rule association relationship exists between respective seed collection rules based on which two collection subjects in the target enclosure are assisted;

[0050] When the target enclosure is determined, all collection subjects in the target enclosure are taken as auxiliary objects and combined into a secondary auxiliary object set;

[0051] Each auxiliary object in the secondary auxiliary object set is sequentially traversed;

[0052] Each time the auxiliary object is traversed, a work help demand is determined based on a current first work preparation portrait of the traversed auxiliary object and a seed collection rule based on which the traversed auxiliary object is assisted;

[0053] A best help execution object is determined from the target enclosure; wherein the best help execution object includes: other auxiliary objects in the target enclosure, other than the traversed auxiliary object, based on a seed collection rule based on which the other auxiliary objects can meet the work help demand, and the other auxiliary objects are closest to the traversed auxiliary object;

[0054] The secondary assistance opportunity includes: the traversed auxiliary object actually generates the work help demand;

[0055] The secondary assistance rule includes: controlling the unmanned aerial vehicle to fly to prompt the best help execution object to meet the work help demand of the traversed auxiliary object.

[0056] Optionally, the secondary assistance module controls the unmanned aerial vehicle to fly to prompt the best help execution object to meet the work help demand of the traversed auxiliary object, including:

[0057] A first flight path is planned for the unmanned aerial vehicle to fly into the target enclosure and to the best help execution object;

[0058] A second flight path is planned for the unmanned aerial vehicle to fly from the best help execution object to the traversed auxiliary object and out of the target enclosure;

[0059] The unmanned aerial vehicle is controlled to fly along the first flight path, and when it arrives at the best help execution object, the unmanned aerial vehicle is controlled to broadcast a first preset voice corresponding to the work help demand;

[0060] After the broadcast is completed, the unmanned aerial vehicle is controlled to fly along the second flight path, and when it arrives at the traversed auxiliary object, the unmanned aerial vehicle is controlled to pause flight and broadcast a second preset voice corresponding to the work help demand, and after the broadcast is completed, the flight is continued.

[0061] Optionally, the constraint of the first flight path comprises:

[0062] The straight-line distances between the plurality of first path points on the first flight path and the current position of the first interference-free object are all greater than or equal to the first distance threshold, the first interference-free object comprising all the auxiliary objects in the target enclosure except the best-helping 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, the first target distance sequence comprising a sequence of distances between the first path points and the current position of the best-helping execution object sorted in the order of the first path points;

[0064] Optionally, the constraint of the second flight path comprises:

[0065] The straight-line distances between the plurality of second path points on the second flight path and the current position of the second interference-free object are all greater than or equal to the second distance threshold, the second interference-free object comprising all the auxiliary objects in the target enclosure except the traversed auxiliary object;

[0066] The second target distance sequence matches the preset second standard distance sequence corresponding to the total number of the second path points, the second target distance sequence comprising a sequence of distances between the second path points and the current position of the traversed auxiliary object sorted in the order of the second path points.

[0067] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0068] The technical solutions of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0069] The accompanying drawings incorporated in and forming a part of the specification, illustrate several aspects of the present application, and together with the description serve to explain the principles of the application. In the drawings:

[0070] Figure 1 A schematic diagram of the crop seed collection method based on big data image analysis in the embodiment of the present application;

[0071] Figure 2 A schematic diagram of the crop seed collection system based on big data image analysis in the embodiment of the present application. DETAILED DESCRIPTION

[0072] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, in which it is understood that the preferred embodiments described below are merely intended to illustrate and explain the present application, and are not intended to limit the present application.

[0073] The embodiments of the present application provide a crop seed collection method based on big data image analysis, as shown in the figure, comprising: Figure 1

[0074] S1, when the collection subject arrives at a survey point, receiving the growth image of the crops at the arrived survey point collected by the collection subject; wherein the collection subject at least includes: seed collection personnel, robots;

[0075] In S1, the survey point can be an area that needs to be surveyed for crop germplasm resources, etc. When the collection subject arrives at the survey point, the growth image of the crops at the arrived survey point will be collected. When the collection subject is seed collection personnel, the collection can be carried out through the mobile phone and other devices carried by the personnel. When the collection subject is a robot, the collection can be carried out through the camera equipped on the robot;

[0076] S2, based on big data image analysis, determining the variety classification according to the growth image;

[0077] In S2, the big data image analysis means that a large number of growth images of different crops are collected in advance based on big data technology, and the growth image collected by the collection subject is compared with the growth images collected in advance, so as to determine the variety classification represented by the growth image collected by the collection subject;

[0078] S3, based on the seed collection knowledge base, determining the seed collection rule according to the variety classification;

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

[0080] S4, based on the seed collection rule, assisting the collection subject to collect the seeds of the crops at the arrived survey point accordingly;

[0081] In S4, based on the seed collection rule, the collection subject can be assisted to collect the seeds of the crops at the arrived survey point accordingly;

[0082] S5, when the collection is completed, prompting the collection subject to go to the next survey point.

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

[0084] ​The application is based on big data image analysis, determines the variety classification according to the growth image of the crops arriving at the census point collected by the collection subject, determines the seed collection rule according to the variety classification based on the seed collection knowledge base, assists the collection subject in collecting the seeds of the crops arriving at the census point according to the seed collection rule, and prompts the collection subject to go to the next census point when the collection is completed, without manually judging the variety classification of the crops at the census point and determining the seed collection rule of the crops according to the variety classification, thereby reducing the labor cost and improving the seed collection efficiency. When the collection personnel encounter crops of an unknown variety, the collection rule can also be quickly, accurately and comprehensively determined, the seed collection efficiency is improved, and the problem of incorrect seed collection is avoided.

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

[0086] S6, determining a secondary assistance object set, a secondary assistance time and a secondary assistance rule based on the census work position distribution of the collection subject;

[0087] In S6, the census work position distribution of the collection subject is the position distribution when different collection subjects perform crop germplasm resource census work; the secondary assistance object set has collection subjects that perform secondary assistance on the work of seed collection; the secondary assistance time is the time when secondary assistance needs to be performed on the collection subjects in the secondary assistance object set; and the secondary assistance rule indicates how to perform secondary assistance on the collection subjects in the secondary assistance object set.

[0088] S7, tracking and identifying whether each assistance object in the secondary assistance object set enters the secondary assistance time;

[0089] S8, when it is identified that it enters, performing corresponding secondary assistance on each assistance object based on the secondary assistance rule;

[0090] In S8, when it is identified that each assistance object in the secondary assistance object set enters the secondary assistance time, performing corresponding secondary assistance on each assistance object based on the secondary assistance rule.

[0091] The S6, determining a secondary assistance object set, a secondary assistance time and a secondary assistance rule based on the census work position distribution of the collection subject, comprises:

[0092] S61, attempting to determine a target enclosure from the census work position distribution; wherein the target enclosure encloses more than or equal to two collection subjects, and the diameter of the target enclosure does not exceed a preset diameter threshold, and there is a rule association relationship between the seed collection rules based on which each two collection subjects in the target enclosure are assisted; the rule association relationship represents that the two collection subjects can assist each other when performing census work.

[0093] In S61, setting the target circle to enclose no less than 2 collection subjects can enable mutual assistance among the collection subjects within the target circle; the diameter threshold value can be, for example, 25 meters; setting the diameter of the target circle to not exceed the preset diameter threshold value can prevent the target circle from being too large, thereby facilitating the use of a UAV to provide secondary assistance to the target circle whose diameter does not exceed the preset diameter threshold value, and can also prevent the distance between any two collection subjects within the target circle from being too far apart, thereby facilitating mutual assistance between them; the rule association relationship can be, for example, that the assisted collection subjects use the same seed collection tool and perform the same seed collection operation; setting the rule association relationship between the seed collection rules based on which each of the collection subjects within the target circle is assisted can enable the collection subjects within the target circle to have a need for mutual assistance;

[0094] S62, when the target circle is determined, all the collection subjects within the target circle are taken as assistance objects and combined into a secondary assistance object set;

[0095] In S62, when the target circle is determined, the collection subjects within the target circle can be provided with secondary assistance, and all of them are taken as assistance objects and combined into a secondary assistance object set;

[0096] S63, each assistance object in the secondary assistance object set is sequentially traversed;

[0097] S64, each time the assistance object is traversed, the work assistance demand is determined based on the first work preparation profile of the traversed assistance object and the seed collection rule based on which the assistance is provided;

[0098] In S64, the first work preparation profile includes, for example, the seed collection tool carried when performing the census work and the seed collection experience possessed; based on the first work preparation profile of the traversed assistance object and the seed collection rule based on which the assistance is provided, the work assistance demand can be determined, for example, the seed collection rule based on which the assistance is provided indicates that the assistance needs to use a certain seed collection tool, and the first work preparation profile indicates that the assistance does not carry the seed collection tool, so the work assistance demand is to provide the assistance with the seed collection tool;

[0099] S65, the best assistance execution object is determined from the target circle; the best assistance execution object includes, for example, the other assistance object closest to the traversed assistance object among the other assistance objects within the target circle, other than the traversed assistance object, whose second work preparation profile indicates that the assistance execution object can meet the work assistance demand and whose seed collection rule based on which the assistance is provided meets the rule association relationship;

[0100] In S65, the optimal help execution object is the other auxiliary object closest to the auxiliary object traversed among other auxiliary objects that can meet the work help demand in the target enclosure; the seed collection rule based on the assisted and the current second work preparation portrait characteristic can meet the work help demand, for example: the work help demand is to be provided with a certain seed collection tool, if the seed collection rule based on the assisted indicates that the other auxiliary object does not need to use the seed collection tool, and the second work preparation portrait characteristic indicates that the other auxiliary object carries the seed collection tool, the work help demand is met;

[0101] In S66, the secondary assistance opportunity is generated when the auxiliary object traversed actually generates a work help demand;

[0102] In S67, the secondary assistance rule includes controlling the unmanned aerial vehicle to fly to prompt the optimal help execution object to meet the work help demand of the auxiliary object traversed.

[0103] In S67, when the secondary assistance is performed, the unmanned aerial vehicle is controlled to fly to prompt the optimal help execution object to meet the work help demand of the auxiliary object traversed, so as to assist the auxiliary objects to perform timely mutual assistance.

[0104] The embodiments of the present application have the following beneficial effects:

[0105] The secondary assistance to the auxiliary object makes the crop seed collection process more efficient, intelligent and automated;

[0106] By surveying the work position distribution based on the collection subjects, the secondary assistance object set, the secondary assistance opportunity and the secondary assistance rule are accurately determined, so as to ensure real-time and accurate identification of the auxiliary object and avoid invalid or unnecessary assistance in the traditional method;

[0107] In the determination process of the target enclosure, the effective range control is realized by setting the minimum enclosure, so that the execution of the assistance behavior will not be too dispersed or delayed, and the resource allocation and time utilization efficiency are further optimized;

[0108] When the collection subjects in the target enclosure are determined, it is ensured that there is a rule association relationship among these subjects, so that assistance is only performed when there is a mutual assistance demand, the assistance efficiency is greatly improved, and the waste of resources is avoided;

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

[0110] The generation of the secondary assistance rule controls the flight path of the unmanned aerial vehicle, and timely prompts the optimal help execution object, so that the assistance operation can accurately reach the target object; in this way, the error and timeliness problem of manual intervention is avoided, and the automation degree and collaborative efficiency of the entire assistance process are improved;

[0111] A flexible unmanned aerial vehicle guidance and execution mechanism is adopted, which can intelligently adjust the execution strategy according to the on-site situation, and ensure that the mutual assistance behavior among the collection subjects can be efficiently and timely performed.

[0112] In one embodiment, in the S67, the unmanned aerial vehicle is controlled to fly to prompt the optimal help execution object to meet the work help demand of the traversed assistance object, comprising:

[0113] S671, planning the first flight path of the unmanned aerial vehicle to enter the target surrounding circle and go to the side of the optimal help execution object;

[0114] In S671, going to the side of the optimal help execution object means that the unmanned aerial vehicle approaches the optimal help execution object, and specifically, the approaching distance on the side of the optimal help execution object can be set by the technical personnel in advance;

[0115] S672, planning the second flight path of the unmanned aerial vehicle from the side of the optimal help execution object to the side of the traversed assistance object and out of the target surrounding circle;

[0116] In S671, similarly, going to the side of the traversed assistance object means that the unmanned aerial vehicle approaches the traversed assistance object, and specifically, the approaching distance on the side of the traversed assistance object can be set by the technical personnel in advance;

[0117] S673, controlling the unmanned aerial vehicle to fly along the first flight path, and when reaching the side of the optimal help execution object, controlling the unmanned aerial vehicle to broadcast the first preset voice corresponding to the work help demand;

[0118] In S673, the first preset voice is a prompt voice for prompting the optimal help execution object to meet the work help demand, for example: "provide a certain type of collection tool for others to use";

[0119] S674, after the broadcast is completed, controlling the unmanned aerial vehicle to fly along the second flight path, and when reaching the side of the traversed assistance object, controlling the unmanned aerial vehicle to pause flight, and broadcast the second preset voice corresponding to the work help demand, and after the broadcast is completed, continue to control the flight;

[0120] In S674, after the first preset voice broadcast is completed, the best help execution object knows that it needs to meet the work help demand, and it will pay attention to the future flight of the UAV, control the UAV to fly along the second flight path, at this time, the best help execution object knows that the work help demand is generated by the traversed auxiliary object, controls the UAV to pause flight, and broadcasts the second preset voice corresponding to the work help demand, the second preset voice is a voice prompting the traversed auxiliary object to accept the work help demand to be met, for example: "Prepare to receive a certain seed collection tool"; after the second preset voice broadcast is completed, the UAV continues to fly along the second flight path and drives out of the target enclosure, completing a complete secondary assistance;

[0121] The constraint of the first flight path includes:

[0122] Constraint A1: The straight-line distances between the plurality of first path points on the first flight path and the current positions of the first interference-free objects are all greater than or equal to the first distance threshold; wherein the first interference-free objects include all auxiliary objects in the target enclosure except the best help execution object;

[0123] In constraint A1, the first path point includes the starting point of the first flight path; the first distance threshold is a distance at which the UAV flight does not interfere with the seed collection work of the collection subject, which can be obtained by technicians in advance through relevant tests; ensuring that the straight-line distances between each first path point and the current positions of the first interference-free objects are all greater than or equal to the first distance threshold, so that the process of the UAV flying into the target enclosure and going to the side of the best help execution object does not interfere with the first interference-free objects;

[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 each first path point and the current position of the best help execution object in the corresponding first path point order;

[0125] In constraint A2, each first path point has a straight-line distance from the current position of the best help execution object, and the straight-line distances are sorted in the corresponding first path point order to obtain a distance sequence, which is the first target distance sequence; the first standard distance sequence has a plurality of distances that decrease in turn, and when the first target distance sequence matches the first standard distance sequence, it can be ensured that when the UAV reaches the first path point in turn, it will approach the best help execution object uniformly and gradually, so that the best help execution object will perceive that the UAV is for it and has the purpose of prompting it, thereby gradually paying attention to the UAV;

[0126] The constraint of the second flight path includes:

[0127] Constraint B1: the straight-line distances between the multiple second path points on the second flight path and the current positions of the second interference-free objects, which are mutually spaced by the second interval, are all greater than or equal to a second distance threshold; wherein the second interference-free objects include all the auxiliary objects within the target enclosure except the traversed auxiliary object;

[0128] In constraint B1, the second path point contains the starting point of the second flight path; the second distance threshold is the distance at which the unmanned aerial vehicle does 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 that the unmanned aerial vehicle interferes with the second interference-free object when it goes from the side of the best help execution object to the side of the traversed auxiliary object and exits the target enclosure is larger, and a larger distance should be maintained; ensuring that the straight-line distances between the multiple second path points on the second flight path and the current positions of the second interference-free objects, which are mutually spaced by the second interval, are all greater than or equal to a second distance threshold, so that the unmanned aerial vehicle does not interfere with the second interference-free object to a greater extent when it goes from the side of the best help execution object to the side of the traversed auxiliary object and exits the target enclosure;

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

[0130] In constraint B2, the straight-line distances between the multiple second path points on the second flight path and the current positions of the traversed auxiliary object are also obtained, and the distance sequence is sorted in the corresponding order of the second path points to obtain the second target distance sequence; the second standard distance sequence has multiple distances that decrease in turn, and when the second target distance sequence matches the second standard distance sequence, it can be ensured that the unmanned aerial vehicle will approach the traversed auxiliary object uniformly and gradually when it reaches the second path points in turn, so that the traversed auxiliary object will perceive the unmanned aerial vehicle as coming for it and will pay attention to it for the purpose of related prompting, thereby gradually paying attention to the unmanned aerial vehicle;

[0131] wherein the first interval and the second interval are each negatively correlated with the personnel density of the target enclosure; the greater the personnel density of the target enclosure, the more it is necessary to ensure that the unmanned aerial vehicle does not interfere with the corresponding first interference-free object and second interference-free object when it flies 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 distance decreasing trend in the sequence of the first standard distance sequence is greater than the distance decreasing trend in the sequence of the second standard distance sequence; the distance decreasing trend in the sequence refers to the average value of the values of the distances in the sequence that decrease in turn; when flying along the first flight path, the best help execution object needs to be prompted as soon as possible, and when flying along the second flight path, the best help execution object needs to be guided to know who needs to be helped, and therefore the distance decreasing trend in the sequence of the first standard distance sequence is set to be greater than the distance decreasing trend in the sequence of the second standard distance sequence.

[0133] Generally, in an actual crop seed collection site, due to the complex environment of the site and the fact that the seed collection personnel focus on seed collection work, it is relatively cumbersome when they need help, and therefore secondary assistance is needed.

[0134] Therefore, the embodiments of the present application have the following beneficial effects:

[0135] Through accurate flight path planning and voice prompts, the unmanned aerial vehicle can efficiently deliver task requirement information to the correct object, reducing the communication cost of personnel and avoiding low work efficiency due to misunderstanding or information delay; crop seed collection and other tasks usually require multiple people to collaborate, and through unmanned aerial vehicle assistance, the precise docking of tasks can be achieved, effectively improving work efficiency;

[0136] The flight path of the unmanned aerial vehicle is designed to be constrained, not only ensuring help to key target objects, but also avoiding interference with other workers; this design takes into account the complexity and personnel distribution of the crop seed collection site, and can maximize the avoidance of interference with other collection personnel when the unmanned aerial vehicle is flying, maintaining the order of the work site;

[0137] Traditional manual collaboration and delivery tools may be affected by factors such as personnel fatigue and environmental changes, while the unmanned aerial vehicle, through automated flight path control and intelligent voice broadcasting, can achieve autonomous collaboration in a relatively complex environment, reducing manual intervention and reducing the probability of human error; the flight path of the unmanned aerial vehicle is adjusted in real time according to the site conditions, improving the adaptive ability of the system;

[0138] By linking the flight path interval to the personnel density within the target enclosure, the system can dynamically adjust the flight strategy, making the technology more flexible in actual application; for example, in areas with high personnel density, the flight path interval will be reduced, thereby reducing interference with other workers; in open areas, the path interval can be relatively increased to ensure that the unmanned aerial vehicle can quickly approach the target object; this flexible adjustment mechanism enables the unmanned aerial vehicle to work effectively in different scenarios;

[0139] The assistance of the unmanned aerial vehicle not only improves the work efficiency, but also enhances the user experience through accurate voice prompts; the collection personnel can clearly know when to pay attention to the prompt of the unmanned aerial vehicle, thereby reducing the confusion caused by unclear information transmission or understanding deviation; especially in complex farmland environment, the seed collection personnel usually need to focus on the task at hand, and the voice prompt of the unmanned aerial vehicle can effectively guide them to complete the task and reduce their work burden.

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

[0141] The specimen provides the morphological characteristics of crops, helping experts to accurately classify and identify;

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

[0143] Through the specimen and sampling 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] The specimen collection often accompanies the growth environment data such as soil type and climate conditions, which helps to understand the ecological adaptability of crops.

[0145] In the process of germplasm resource collection, it is 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] Embodiments of the present application provide a crop seed collection system based on big data image analysis, as shown in Figure 2 , comprising:

[0147] The receiving module 1 is used for receiving the growth image of the crop at the survey point collected by the collection subject when the collection subject arrives at the survey point; wherein the collection subject at least includes: seed collection personnel, robots;

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

[0149] The second determination module 3 is used for determining the seed collection rule based on the variety classification based on the seed collection knowledge base;

[0150] The auxiliary module 4 is used for assisting the collection subject to collect the seeds of the crops at the survey point according to the seed collection rule;

[0151] The prompting module 5 is configured 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 further comprises:

[0153] The secondary assistance module is configured to:

[0154] Determine the secondary assistance object set, the secondary assistance timing, and the secondary assistance rule based on the census work position distribution of the collection subject;

[0155] Track and identify whether each assistance object in the secondary assistance object set enters the secondary assistance timing;

[0156] When it is identified that the assistance object enters the secondary assistance timing, the secondary assistance module is configured to perform secondary assistance on the assistance object based on the secondary assistance rule;

[0157] The secondary assistance module is configured to:

[0158] Attempt to determine a target enclosure from the census work position distribution; wherein the target enclosure encloses more than or equal to two collection subjects, and the diameter of the target enclosure does not exceed a preset diameter threshold, and each of the two collection subjects in the target enclosure is associated with a rule relationship between the seed collection rules based on which the collection subjects are assisted; the rule relationship indicates that the two collection subjects can assist each other when performing census work;

[0159] When the target enclosure is determined, all the collection subjects in the target enclosure are combined into the secondary assistance object set as the assistance objects;

[0160] Iterate through each assistance object in the secondary assistance object set;

[0161] Each time the iteration is performed, the work assistance demand is determined based on the first work preparation portrait of the assistance object and the seed collection rule based on which the assistance object is assisted;

[0162] Determine the best assistance execution object from the target enclosure; wherein the best assistance execution object includes: the other assistance object in the target enclosure that is closest to the assistance object and that can meet the work assistance demand based on the seed collection rule based on which the assistance object is assisted;

[0163] The secondary assistance timing is generated when the assistance object actually generates the work assistance demand;

[0164] The secondary assistance rule is generated by controlling the unmanned aerial vehicle to fly to prompt the best assistance execution object to meet the work assistance demand of the assistance object.

[0165] The secondary assistance module controls the unmanned aerial vehicle to fly to prompt the optimal help execution object to meet the work help demand of the assistance object traversed, comprising:

[0166] Planning a first flight path for the unmanned aerial vehicle to fly into the target enclosure and go to the side of the optimal help execution object;

[0167] Planning a second flight path for the unmanned aerial vehicle to fly from the side of the optimal help execution object to the side of the assistance object traversed and out of the target enclosure;

[0168] Controlling the unmanned aerial vehicle to fly along the first flight path, when reaching the side of the optimal help execution object, controlling the unmanned aerial vehicle to broadcast the first preset voice corresponding to the work help demand;

[0169] After the broadcast is completed, controlling the unmanned aerial vehicle to fly along the second flight path, when reaching the side of the assistance object traversed, controlling the unmanned aerial vehicle to pause flight, and broadcast the second preset voice corresponding to the work help demand, and after the broadcast is completed, continue to control the flight.

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

[0171] The straight line distances between the plurality of first path points on the first flight path and mutually spaced by a first interval and the current positions of the first interference-free objects are all greater than or equal to a first distance threshold; wherein the first interference-free objects include all the assistance objects in the target enclosure except the optimal help 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 the distance sequence obtained by sorting the straight line distances between the first path points and the current position of the optimal help execution object in the corresponding first path point order;

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

[0174] The straight line distances between the plurality of second path points on the second flight path and mutually spaced by a second interval and the current positions of the second interference-free objects are all greater than or equal to a second distance threshold; wherein the second interference-free objects include all the assistance objects in the target enclosure except the assistance object traversed;

[0175] 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 the distance sequence obtained by sorting the straight line distances between the second path points and the current position of the assistance object traversed in the corresponding second path point order.

[0176] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for collecting crop seeds based on big data image analysis, characterized in that, include: When the collecting entity arrives at a census point, it receives images of crop growth collected by the collecting entity at the census point; wherein, the collecting entity includes at least: seed collecting personnel and robots; Based on big data image analysis, the variety classification is determined according to the growth images; Based on the seed collection knowledge base, seed collection rules are determined according to variety classification; Based on seed collection rules, the collection entities are assisted in collecting the corresponding seeds of crops at the census points they arrive at; When the data collection is complete, the data collection entity will be prompted to proceed to the next census site. Also includes: Based on the location distribution of the census work of the data collection subjects, the set of objects for secondary assistance, the timing of secondary assistance, and the rules for secondary assistance are determined; Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary timing; When an entry is detected, secondary assistance is applied to each auxiliary object based on the secondary assistance rules. The determination of the secondary assistance object set, secondary assistance timing, and secondary assistance rules based on the census work location distribution of the collection subject includes: An attempt is made to determine the target encirclement from the distribution of census work locations; wherein the target encirclement has a minimum of two collection subjects, 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 on which each pair of collection subjects within the target encirclement is assisted; the rule association relationship indicates that the two collection subjects can cooperate when conducting census work; When the target is identified, all the collected entities within the target encirclement are treated as auxiliary objects and combined into a secondary auxiliary object set. Iterate through each auxiliary object in the secondary auxiliary object set in turn; During each traversal, the work assistance requirements are determined based on the current first work preparation profile of the traversed auxiliary object and the seed collection rules on which the auxiliary object is based. The optimal help execution object is determined from the target encirclement; wherein, the optimal help execution object includes: the other auxiliary object that is closest to the traversed auxiliary object among the other auxiliary objects in the target encirclement whose seed collection rule representation can satisfy the work help requirements, excluding the traversed auxiliary objects; The timing for generating secondary assistance includes: when the traversed assistance object actually generates a need for work assistance; The generation of secondary auxiliary rules includes: controlling the drone's flight to prompt the best assist execution object to meet the work assistance requirements of the traversed auxiliary objects.

2. The method for collecting crop seeds based on big data image analysis as described in claim 1, characterized in that, The control of the drone flight to indicate the best assisting object to meet the work assistance requirements of the traversed assisting objects includes: Plan the drone's flight path to enter the target encirclement and proceed to the first flight path next to the best target for assistance; Plan the second flight path for the drone to move from the best assisting object to the traversed auxiliary object and out of the target encirclement; Control the drone to fly along the first flight path, and when it arrives next to the best object to be assisted, control the drone to broadcast the first preset voice corresponding to the work assistance request; After the broadcast is completed, control the drone to fly along the second flight path. When it arrives at the traversed auxiliary object, control the drone to pause flight and broadcast the second preset voice corresponding to the work assistance request. After the broadcast is completed, control the drone to continue flying.

3. The method for collecting crop seeds based on big data image analysis as described in claim 2, characterized in that, The constraints of the first flight path include: The straight-line distances between multiple first path points spaced apart by a first interval on the first flight path 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 within the target encirclement except for the best assist execution object; The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of each first path point; wherein, the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between each first path point and the current position of the best help execution object according to the order of the corresponding first path points; The constraints of the second flight path include: The straight-line distances between multiple second path points spaced apart by a second interval on the second flight path 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 within the target encirclement except for the traversed auxiliary objects; 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 auxiliary object traversed according to the order of the corresponding second path points.

4. The method for collecting crop seeds based on big data image analysis as described in claim 3, characterized in that, The first and second intervals are each negatively correlated with the personnel density within the target encirclement.

5. The method for collecting crop seeds based on big data image analysis as described in claim 3, characterized in that, The decreasing trend of distance in the first standard distance sequence is greater than that in the second standard distance sequence.

6. A crop seed collection system based on big data image analysis, characterized in that, include: A receiving module is used to receive crop growth images collected by the collecting entity at a census point when the collecting entity arrives at the census point; wherein the collecting entity includes at least: seed collecting personnel and a robot; The first determination module is used to determine the variety classification based on big data image analysis and growth images; The second determination module is used to determine seed collection rules based on the seed collection knowledge base and variety classification. The auxiliary module is used to assist the collection entity in collecting the corresponding seeds of crops at the census points based on the seed collection rules. The notification module is used to prompt the data collection subject to proceed to the next census point when the data collection is completed; Also includes: Secondary auxiliary module, used for: Based on the location distribution of the census work of the data collection subjects, the set of objects for secondary assistance, the timing of secondary assistance, and the rules for secondary assistance are determined; Track and identify whether each auxiliary object in the secondary auxiliary object set has entered the secondary auxiliary timing; When an entry is detected, secondary assistance is applied to each auxiliary object based on the secondary assistance rules. The determination of the secondary assistance object set, secondary assistance timing, and secondary assistance rules based on the census work location distribution of the collection subject includes: An attempt is made to determine the target encirclement from the distribution of census work locations; wherein the target encirclement has a minimum of two collection subjects, 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 on which each pair of collection subjects within the target encirclement is assisted; the rule association relationship indicates that the two collection subjects can cooperate when conducting census work; When the target is identified, all the collected entities within the target encirclement are treated as auxiliary objects and combined into a secondary auxiliary object set. Iterate through each auxiliary object in the secondary auxiliary object set in turn; During each traversal, the work assistance requirements are determined based on the current first work preparation profile of the traversed auxiliary object and the seed collection rules on which the auxiliary object is based. The optimal help execution object is determined from the target encirclement; wherein, the optimal help execution object includes: the other auxiliary object that is closest to the traversed auxiliary object among the other auxiliary objects in the target encirclement whose seed collection rule representation can satisfy the work help requirements, excluding the traversed auxiliary objects; The timing for generating secondary assistance includes: when the traversed assistance object actually generates a need for work assistance; The generation of secondary auxiliary rules includes: controlling the drone's flight to prompt the best assist execution object to meet the work assistance requirements of the traversed auxiliary objects.

7. The crop seed collection system based on big data image analysis as described in claim 6, characterized in that, The secondary assistance module controls the drone's flight to indicate the optimal assistance execution object to meet the work assistance requirements of the traversed assistance objects, including: Plan the drone's flight path to enter the target encirclement and proceed to the first flight path next to the best target for assistance; Plan the second flight path for the drone to move from the best assisting object to the traversed auxiliary object and out of the target encirclement; Control the drone to fly along the first flight path, and when it arrives next to the best object to be assisted, control the drone to broadcast the first preset voice corresponding to the work assistance request; After the broadcast is completed, control the drone to fly along the second flight path. When it arrives at the traversed auxiliary object, control the drone to pause flight and broadcast the second preset voice corresponding to the work assistance request. After the broadcast is completed, control the drone to continue flying.

8. The crop seed collection system based on big data image analysis as described in claim 7, characterized in that, The constraints of the first flight path include: The straight-line distances between multiple first path points spaced apart by a first interval on the first flight path 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 within the target encirclement except for the best assist execution object; The first target distance sequence matches the preset first standard distance sequence corresponding to the total number of each first path point; wherein, the first target distance sequence includes: a distance sequence obtained by sorting the straight-line distances between each first path point and the current position of the best help execution object according to the order of the corresponding first path points; The constraints of the second flight path include: The straight-line distances between multiple second path points spaced apart by a second interval on the second flight path 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 within the target encirclement except for the traversed auxiliary objects; 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 auxiliary object traversed according to the order of the corresponding second path points.

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