CN112913393B - Crop sowing depth intelligent decision-making system, method, storage medium and equipment - Google Patents

Abstract

The present invention provides an intelligent decision-making system, method, medium and equipment for the sowing depth of crops. The system includes: a soil information collection module for collecting actual soil information in the sowing area of the crops; a seed information acquisition module for obtaining the The seed information of the crops and obtain the soil information required for the germination of the crop seeds according to the seed information; the information processing module is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, And with the soil information required for the germination of the crop seeds as a contrast, calculate the layered value of each depth layer on the soil layer of the crop sowing area according to the actual soil information of the crop sowing area; the depth decision module is used for The optimal sowing depth layer of the crop seeds is determined according to the layered values of the depth layers on the soil layer.

Description

Crop sowing depth intelligent decision-making system, method, storage medium and equipment

technical field

The invention relates to the technical field of agricultural information, in particular to a crop sowing depth intelligent decision-making system, method, storage medium and equipment.

Background technique

my country is a large agricultural country, and the annual output of corn and other crops ranks among the top in the world. However, the per-mu yield of corn and other crops in my country is still far behind that of other developed countries. Therefore, at this stage, the use of information technology to increase the yield per mu of corn in my country is of great significance to the development of agriculture in China and the income of farmers. The sowing depth of corn plays an important role in the growth and germination of crops such as corn and the yield per mu of crops such as corn. And the mastery of regional soil data is of great significance to the agricultural sector and farmers.

At present, there are still many problems in soil data monitoring and decision-making of crop sowing depth in most areas of my country. In most areas, the decision-making of soil data and sowing depth is based on manual prediction based on experience, resulting in different yields per mu of crops in the same area. . In some areas, in order to prevent crops from not germinating, multiple seeds will be sown in the same area, which is a great waste of land resources and corn. How to solve these problems has become one of the problems that need to be solved urgently at this stage.

Contents of the invention

Aiming at the problems existing in the prior art, the present invention provides a simple, intuitive and convenient intelligent decision-making system for crop sowing depth, so that both farmers and agricultural departments can accurately and targetedly grasp the soil data of a certain farmland in a certain area anytime and anywhere And intelligent broadcast depth decision.

In order to achieve the purpose of the above invention, the present invention provides an intelligent decision-making system for crop sowing depth, including:

Soil information collection module, used to collect the actual soil information of the crop sowing area;

A seed information acquisition module, configured to acquire the seed information of the crops and obtain the soil information required for the germination of the crop seeds according to the seed information;

The information processing module is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, and compare the soil information required for the germination of the crop seeds, according to the soil information of the crop sowing area The actual soil information calculates the stratification value of each depth layer on the soil layer of the crop sowing area;

The depth decision module is used to determine the optimal sowing depth layer of the crop seeds according to the layered values of each depth layer on the soil layer.

In an exemplary embodiment of the present disclosure, the soil information collection module is communicatively connected to a soil data monitoring station, so as to acquire actual soil information of the crop sowing area from the soil data monitoring station.

In an exemplary embodiment of the present disclosure, the soil information collection module has a soil data sensor for collecting actual soil information of the crop sowing area from the soil layer of the crop sowing area.

In an exemplary embodiment of the present disclosure, the actual soil information of the crop sowing area includes temperature T, humidity H, pH value, and compactness D at each depth of the soil.

In an exemplary embodiment of the present disclosure, the soil layer has a depth, and the soil layer is divided into a plurality of depth layers within the depth range.

In an exemplary embodiment of the present disclosure, the intelligent decision-making system further includes an input module and a storage module, and the user inputs crop seed information through the input module, and the seed information acquisition module obtains the crop seed information from the The storage module acquires the corresponding soil information required for the germination of the crop seeds.

In an exemplary embodiment of the present disclosure, the calculation formula of the stratification value θ of the soil layer is:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above.

In an exemplary embodiment of the present disclosure, the depth decision module determines the layering attributes of each depth layer on the soil layer according to the layering value θ of each depth layer on the soil layer, and then according to the The hierarchical properties of each depth layer on the layer yields the optimal sowing depth layer for the crop seeds.

In an exemplary embodiment of the present disclosure, the hierarchical properties of each depth layer on the soil layer reflect the suitability of sowing the crop seeds on the depth layer;

The smaller the stratification value of the depth layer, the better the suitability for sowing the crop seeds on the depth layer.

In an exemplary embodiment of the present disclosure, it further includes a spatial distribution processing module and a display module, the spatial distribution processing module is used to analyze the actual soil information of the crop sowing area, the soil layer of the crop sowing area The hierarchical attributes of each depth layer and the optimal sowing depth layer of the crop seeds are processed in spatial distribution, and the processing results are displayed through the display module.

In an exemplary embodiment of the present disclosure, the spatial distribution processing module includes a GIS unit and an engine unit;

Wherein, the GIS unit is used to calculate the geographic location of the crop sowing area, and combine the soil information of the crop sowing area, the layered attributes of each depth layer on the soil layer of the crop sowing area, and the crop seed The optimal sowing depth layer is marked on the digital map of the geographic location to obtain a decision-making space distribution map of crop sowing depth, and the engine unit is used to send the decision-making space distribution map of crop sowing depth to the display module.

To achieve another object of the present invention, the present invention also provides a method for intelligent decision-making of crop sowing depth, which is characterized in that it comprises the following steps:

The soil information collection step S1 is used to collect the actual soil information of the crop sowing area;

The seed information obtaining step S2 is used to obtain the seed information of the crop and obtain the soil information required for the germination of the crop seed according to the seed information;

The information processing step S3 is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, and compare the soil information required for the germination of the crop seeds, according to the crop sowing area The actual soil information calculates the layered value of each depth layer on the soil layer of the crop sowing area;

The depth decision step S4 is used to determine the optimal sowing depth layer of the crop seeds according to the layered values of the depth layers on the soil layer.

In an exemplary embodiment of the present disclosure, the soil information collection step S1 further includes:

S11, judging whether the crop sowing area belongs to a certain soil data monitoring station;

S12. If yes, directly call the actual soil information of the crop sowing area from the soil data monitoring station; otherwise, collect the actual soil information of the crop sowing area through a soil data sensor.

In an exemplary embodiment of the present disclosure, the actual soil information of the crop sowing area includes soil temperature T, soil humidity H, soil pH value, and soil compaction degree D at each depth layer of the soil layer.

In an exemplary embodiment of the present disclosure, the soil layer has a depth, and the soil layer is divided into a plurality of depth layers within the depth range.

In an exemplary embodiment of the present disclosure, the step S2 of obtaining seed information further includes:

S21, obtaining the crop seed information input by the user;

S22. Identify the seed type according to the seed information, and search for the soil information required for the germination of the crop seeds according to the seed type.

In an exemplary embodiment of the present disclosure, the information processing step S3 is characterized in that the calculation formula of the layered value θ of the soil layer is:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above. In an exemplary embodiment of the present disclosure, the depth decision step S4 includes:

S41. Determine the stratification attribute of each depth layer on the soil layer according to the stratification value θ of each depth layer on the soil layer;

S42. Obtain an optimal sowing depth layer of the crop seeds according to the layering attributes of each depth layer on the soil layer.

In an exemplary embodiment of the present disclosure, the hierarchical properties of each depth layer on the soil layer reflect the suitability of sowing the crop seeds on the depth layer;

The smaller the stratification value of the depth layer, the better the suitability for sowing the crop seeds on the depth layer.

In an exemplary embodiment of the present disclosure, it further includes a spatial distribution processing step for analyzing the actual soil information of the crop sowing area, the hierarchical attributes of each depth layer on the soil layer of the crop sowing area, and the The optimal sowing depth layer of the above-mentioned crop seeds is processed on the spatial distribution, and the processing results are displayed through a display module.

In order to achieve another object of the present invention, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the intelligent decision-making of crop sowing depth described in any one of the above-mentioned items is realized. method.

To achieve another purpose of the present invention, the present invention also provides an electronic device, comprising:

processor; and

a memory for storing executable instructions of the processor;

Wherein, the processor is configured to execute the intelligent decision-making method for crop sowing depth described in any one of the above-mentioned methods by executing the executable instructions.

The crop sowing depth intelligent decision-making system provided by the present invention can automatically obtain farmland soil data and crop sowing depth decision-making, and no matter the farmer or the agricultural department can accurately and pertinently grasp the soil of a certain farmland in a certain area through the interface display module anytime and anywhere Data information and intelligent crop sowing depth decision-making, without having to visit the farmlands in various places, enables farmers to directly query the soil data information and intelligent crop sowing depth decisions of their own farmlands in different regions. The soil data information of each farmland and intelligent crop sowing depth decision-making, in order to carry out large-scale intelligent analysis of crop production, so the present invention solves the problems caused by the inability to view different farmland soil data information and crop sowing depth decisions in different regions anytime and anywhere in the prior art Problems, combined with modeling technology, real-time display of soil data information and crop planting depth decisions, can be used by the agricultural sector, and can also be extended to grass-roots farmers, and the operation is simple, intuitive and convenient, avoiding the existing technology relying on manual past experience Predict the problem that the yield per mu of corn cannot be increased. At the same time, due to the direct access to the crop sowing depth strategy, farmers can get professional guidance on precise sowing, avoiding random sowing and agricultural land waste and affecting crop harvest, and providing scientific farming. The key factor.

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

Description of drawings

Fig. 1 is a schematic structural diagram of an intelligent decision-making system for crop sowing depth according to an embodiment of the present invention.

Fig. 2 is a flowchart of an intelligent decision-making method for crop sowing depth according to an embodiment of the present invention.

detailed description

Below in conjunction with accompanying drawing, structural principle and working principle of the present invention are specifically described:

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details being omitted, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and/or processor means and/or microcontroller means.

In this example embodiment, a kind of crop sowing depth intelligent decision-making system 100 is firstly provided. The crop sowing depth intelligent decision-making system of the present invention can be used for the calculation of the sowing depth of various crop seeds. Based on the soil characteristics of the region, make reasonable judgment decisions. As shown in FIG. 1 , the crop sowing depth intelligent decision system 100 of this embodiment may firstly include a soil information collection module 110 , a seed information acquisition module 120 , an information processing module 130 and a depth decision module 140 . Wherein, the information processing module 130 performs soil stratification calculation modeling according to the range of soil environment data required for germination of different types of seeds of crops, so as to establish a soil stratification calculation model. In order to be able to specifically and clearly introduce the implementation of the present invention, the crop in this embodiment is corn as an example.

The soil information acquisition module 110 collects the actual soil information of the sowing area and inputs it into the soil stratification calculation model of the information processing module 130, and the seed information acquisition module 120 manually inputs the crop seed type and inputs it into the soil stratification calculation model. model, the soil stratification calculation model performs soil stratification calculation according to the actual soil information of the sowing area collected by the soil information acquisition module 110 and the soil information required for the germination of this kind of crop seeds determined by the seed information acquisition module 120, and The results of the soil stratification calculation are input to the depth decision module 140; the depth decision module 140 obtains the optimal sowing depth decision through the combination of artificial intelligence and decision support according to the results of the soil stratification calculation. Among them, for example, the soil information required for the germination of crop seeds may include the optimum soil temperature Topt, the _optimum soil moisture Hopt, the _optimum soil pH value and the _optimum soil pH required for the germination of crop seeds. Firmness D is the _best , etc.

Specifically, in this embodiment, when farmers or agricultural departments need to know the optimal sowing depth of crops in a certain farmland, if the farmland belongs to a soil data monitoring station, the soil data can be directly called from the monitoring station; This farmland does not belong to a certain soil monitoring station, or when the soil monitoring station collects soil information, the soil data collection module 110 can perform the following operations: the soil information collection module 110 collects soil information on the soil layer at a certain depth through the soil data sensor , and in actual use, the depth to which the information of the soil layer is collected is mainly considered according to the type of seeds to be sown. For example, when the crop to be sown is corn, its information on the soil layer The collection was at a depth of 0cm-6cm.

That is to say, the soil information collection module 110 of this embodiment has at least three information collection methods, one is to directly call the soil information stored in the soil data monitoring station, and the other is to use the soil data sensor to analyze the soil layer. Real-time soil information collection at a certain depth, and the third is a combination of the two.

Generally speaking, the soil information collected by the soil information collection module 110 of this embodiment mainly includes: soil temperature T, soil humidity H, soil pH value, and soil compactness D.

Further, the crop sowing depth intelligent decision-making system 100 of this embodiment also includes an input module and a storage module (not shown in the figure), the user inputs crop seed information through the input module, and the seed information acquisition module 110 according to the The crop seed information obtains the corresponding soil information required for the germination of the crop seeds from the storage module. That is to say, when farmers or agricultural departments use this system, they need to input the information of crop varieties through the input module. The soil information required for the germination of the crop seeds is searched in the preset storage module, and input to the information processing module 130 for soil layer modeling. Of course, the present invention is not limited thereto.

The information processing module 130 compares the optimal soil information required for crop germination provided by the seed information acquisition module 120, wherein, for example, the soil information required for crop seed germination may include the optimal soil information required for crop seed germination The temperature T is the _best , the best soil moisture H is the _best , the best soil pH is the _best PH, and the best soil compactness D is the _best . According to the actual soil information provided by the soil information collection module 110, the stratification value is calculated for each depth layer of the soil layer, and then each depth layer is graded according to the stratification value. As mentioned above, the soil layer has a depth. For example, the information collection of the above-mentioned soil layer of corn seeds is a depth of 0cm-6cm, and the soil layer is divided into multiple depth layers within the depth range, such as 0cm-6cm. The soil layer at a depth of 6cm is divided into three depth layers: 0cm-2cm, 2cm-4cm, and 4cm-6cm. In this embodiment, the information processing module 130 calculates the corresponding hierarchical value θ of the three depth layers respectively, as a basis for classifying the three depth layers. The calculation method of the layered value θ of the soil layer is, for example:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above.

Accordingly, it needs to be further clarified that when the aforementioned soil information collection module 110 collects the soil information of the sowing area, it should have been collected separately according to the depth layers on these soil layers, so as to further calculate the soil information on each depth layer. layered value. Because different crop seeds have different requirements on the depth of the soil layer, and the depth layer division of the soil layer corresponding to different seeds is also different, so the soil information collection module needs to determine the collected soil according to the seed information obtained by the seed information acquisition module 120 information, wherein, for example, the soil information required for the germination of crop seeds may include the optimum soil temperature Topt, the _optimum soil moisture Hopt, the _optimum soil pH value PHopt and the _optimum Best soil compactness D _best etc.

Further, the depth decision module 140 compares the θ value of each depth layer of the soil, and determines the layered attributes of each depth layer on the soil layer according to the θ value of each depth layer, and then according to the θ value of each depth layer on the soil layer The stratification attribute yields the optimum sowing depth stratum for the crop seeds. The stratification attribute of each depth layer on the soil layer reflects the suitability of sowing the crop seeds on the depth layer; the smaller the stratification value of the depth layer, the suitability of sowing the crop seeds on the depth layer Sex is better.

Continuing to take the above-mentioned corn seeds as an example, the stratification attributes of each depth layer on the soil layer include four types: very suitable, suitable, relatively suitable and relatively suitable, among which, if 0≤θ<100, the level of this layer is very suitable; If 100≤θ<200, the grade of this layer is suitable; if 200≤θ<300, then the grade of this layer is more suitable; if θ≥300, then the grade of this layer is more suitable. For example, calculate the layered value θ=225 of the depth layer of 0cm-2cm, the layered value θ=169 of the depth layer of 2cm-4cm, and the layered value θ=81 of the depth layer of 4cm-6cm, then 0cm-2cm The depth layer is more suitable, the 2cm-4cm depth layer is suitable, and the 4cm-6cm depth layer is very suitable, so the best sowing depth layer is 4cm-6cm.

Furthermore, the crop sowing depth intelligent decision-making system 100 of the present invention also includes a spatial distribution processing module 150 and a display module 160, and the spatial distribution processing module 150 is used for the actual soil information of the crop sowing area, the crop sowing The hierarchical attributes of each depth layer on the soil layer of the region and the optimal sowing depth layer of the crop seeds are processed in spatial distribution, and the processing results are displayed by the display module 160 .

The spatial distribution processing module 150 includes an engine unit 152 and a GIS unit 151 connected to each other, the GIS unit 151 is respectively connected to the soil information collection module 110, the information processing module 130 and the depth decision module 140, and the engine unit 152 is connected to the display module 160; the GIS unit 151 provides Map service information, preferably digital map service information with a scale of 1:10000 can be provided. Specifically, GIS technology is used to process different spatial distributions of geographical locations of different farmlands in different bases and different farmlands, and the actual soil information and soil information obtained from the same farmland. The results of layered calculation and the structure of the sowing depth decision are subjected to the same spatial distribution processing, that is, the soil information of the crop sowing area, the layered attributes of each depth layer on the soil layer of the crop sowing area, and the crops The optimal sowing depth layer of the seeds is marked on the digital map of the geographical location, and the spatial distribution map of crop sowing depth decision is obtained, and then sent to the display module 160 by the engine unit 152 for display.

The display module 160 provides a browser interface to realize system module interaction and human-computer interaction. The interface display module can be touch-type or key-type, and can build a multi-level module directory structure, so that the user operation interface has rich forms of expression and flexible configuration. , The characteristics of uniform style, thus ensuring the diversity and consistency of the user interface of the whole system, friendly customized interface, clear organization, clear items listed in the tree state logic, providing users with a good operating experience. The interface display module shows the relative grade of each layer of soil, the optimum sowing depth and the geographic location of the measured soil.

Finally, farmers or agricultural departments can view the relative grades of the soil layers of this farmland and the optimum sowing depth of this kind of corn in this farmland through the interface display module. Thereby, the next crop sowing operation can be carried out more scientifically.

Correspondingly, based on the same inventive concept, the present invention also provides an intelligent decision-making method for crop sowing depth, as shown in Figure 2, comprising the following steps:

The soil information collection step S1 is used to collect the actual soil information of the crop sowing area;

The seed information obtaining step S2 is used to obtain the seed information of the crop and obtain the soil information required for the germination of the crop seed according to the seed information;

The information processing step S3 is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, and compare the soil information required for the germination of the crop seeds, according to the crop sowing area The actual soil information calculates the layered value of each depth layer on the soil layer of the crop sowing area;

The depth decision step S4 is used to determine the optimal sowing depth layer of the crop seeds according to the layered values of the depth layers on the soil layer.

In this method, soil relative grade modeling is carried out according to the soil environment required for crop seed germination, so as to establish a soil relative grade calculation model. At the same time, real-time soil data and crop seed types are collected and input into the soil relative grade calculation model for soil relative grade calculation, and then through The corn sowing depth decision is obtained by combining artificial intelligence and decision support; the sowing depth decision, the collected soil data, and the soil relative grade calculation results of the soil relative grade calculation model are all spatially distributed and displayed on the interface. The agricultural department can accurately and targetedly grasp the soil data information of a certain farmland in a certain area and the intelligent crop sowing depth decision-making through the interface display module anytime, anywhere, which solves the shortcomings of the existing technology and realizes the remote monitoring of soil data information And scientifically guide the decision-making of intelligent crop sowing depth.

Next, each step in the above-mentioned method for intelligent decision-making of crop sowing depth in this exemplary embodiment will be explained and described in detail.

In the soil information collection step S1, the soil data can be collected by a soil data monitoring station or a soil data sensor to monitor the actual soil information of the crop sowing area, which specifically includes the following steps:

S11, judging whether the crop sowing area belongs to a certain soil data monitoring station;

S12. If yes, directly call the actual soil information of the crop sowing area from the soil data monitoring station; otherwise, collect the actual soil information of the crop sowing area through a soil data sensor.

Wherein, the actual soil information of the crop sowing area includes soil temperature T, soil humidity H, soil pH value, and soil compaction degree D on each depth layer of the soil layer. The soil layer is divided into multiple depth layers within a certain depth range.

In the seed information obtaining step S2, the specific type of the seed can be determined according to the input seed information, so as to obtain the corresponding soil information required for seed germination. Specifically, the following steps can be included:

S21, obtaining the crop seed information input by the user;

S22. Identify the seed type according to the seed information, and search for the soil information required for the germination of the crop seeds according to the seed type, wherein, for example, the soil information required for the germination of the crop seeds may include the soil information required for the germination of the crop seeds The best soil temperature, the best soil moisture, the best soil PH value and the best soil compaction.

In the information processing step S3, the calculation method of the layered value θ of the soil layer is, for example:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above.

In the depth decision step S4, specific steps may be included:

S41. Determine the stratification attribute of each depth layer on the soil layer according to the stratification value θ of each depth layer on the soil layer;

S42. Obtain an optimal sowing depth layer of the crop seeds according to the layering attributes of each depth layer on the soil layer.

Wherein, the stratification attribute of each depth layer on the described soil layer reflects the suitability of sowing the crop seeds on the depth layer; the smaller the stratification value of the depth layer is, the more described The better the suitability of the crop seeds.

Further, the intelligent decision-making method for crop sowing depth of the present invention may also include a spatial distribution processing step for analyzing the actual soil information of the crop sowing area, the layering of each depth layer on the soil layer of the crop sowing area The attribute and the optimal sowing depth layer of the crop seeds are processed on the spatial distribution, and the processing results are displayed through a display module.

It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. Actually, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided to be embodied by a plurality of modules or units.

In addition, although steps of the methods of the present disclosure are depicted in the drawings in a particular order, there is no requirement or implication that the steps must be performed in that particular order, or that all illustrated steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution, etc.

Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

Correspondingly, based on the same inventive concept, the present invention also provides a computer-readable storage medium on which is stored a program product capable of implementing the above-mentioned method in this specification. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes program code, and when the program product is run on a terminal device, the program code is used to make the The terminal device executes the steps according to various exemplary embodiments of the present invention described in the "Exemplary Method" section above in this specification.

According to the program product for implementing the above method according to the embodiment of the present invention, it may adopt a portable compact disc read-only memory (CD-ROM) and include program codes, and may run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus or device.

The program product may reside on any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer readable signal medium may include a data signal carrying readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming languages. Programming language - such as "C" or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider). business to connect via the Internet).

Correspondingly, based on the same inventive concept, the present invention also provides an electronic device.

Those skilled in the art can understand that various aspects of the present invention can be implemented as systems, methods or program products. Therefore, various aspects of the present invention can be embodied in the following forms, that is: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as "circuit", "module" or "system".

The electronic device of this embodiment is expressed in the form of a general-purpose computing device. Components of the electronic device may include, but are not limited to: the above at least one processing unit, the above at least one storage unit, and a bus connecting different system components (including the storage unit and the processing unit).

Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit, so that the processing unit executes the various exemplary implementations according to the present invention described in the "Exemplary Methods" section of this specification. A step of.

The storage unit may include readable media in the form of volatile storage units, such as random access memory units (RAM) and/or cache storage units, and may further include read only memory units (ROM).

The memory unit may also include programs/utilities having a set (at least one) of program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, in which case Each or some combination may include implementations of network environments.

A bus may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures .

The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), and with one or more devices that enable the user to interact with the electronic device, and/or communicate with the electronic A device communicates with any device (eg, router, modem, etc.) that is capable of communicating with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through a network adapter. As shown, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and Data backup storage system, etc.

Through the description of the above implementations, those skilled in the art can easily understand that the example implementations described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of software products, and the software products can be stored in a non-volatile storage medium (which can be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to make a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

Certainly, the present invention also can have other multiple embodiments, without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention, but these corresponding Changes and deformations should belong to the scope of protection of the appended claims of the present invention.

Claims (16)

1. An intelligent decision-making system for planting depth of crops, characterized in that it comprises: Soil information collection module, used to collect the actual soil information of the crop sowing area, the soil information collection module has a soil data sensor, used to collect the actual soil information of the crop sowing area from the soil layer of the crop sowing area , the actual soil information of the crop sowing area includes soil temperature T, soil humidity H, soil pH value, soil compactness D on each depth layer of the soil layer; A seed information acquisition module, configured to acquire the seed information of the crops and obtain the soil information required for the germination of the crop seeds according to the seed information; The information processing module is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, and compare the soil information required for the germination of the crop seeds, according to the soil information of the crop sowing area The actual soil information calculates the stratification value of each depth layer on the soil layer of the crop sowing area; Depth decision-making module, used to determine the optimal sowing depth layer of the crop seeds according to the stratification value of each depth layer on the soil layer; Wherein, the calculation formula of the layered value θ of the soil layer is:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above. 2. The crop sowing depth intelligent decision-making system according to claim 1, wherein the soil information collection module is communicatively connected to a soil data monitoring station, so as to acquire the actual data of the crop sowing area from the soil data monitoring station. soil information. 3 . The intelligent decision-making system for sowing depth of crops according to claim 1 , wherein the soil layer has a depth, and the soil layer is divided into a plurality of depth layers within the depth range. 4 . 4. The crop planting depth intelligent decision-making system according to claim 1, characterized in that, the intelligent decision-making system also includes an input module and a storage module, and the user inputs crop seed information through the input module, and the seed information acquisition module Acquire corresponding soil information required for germination of the crop seeds from the storage module according to the crop seed information. 5. The crop sowing depth intelligent decision-making system according to claim 1, wherein the depth decision-making module determines the division of each depth layer on the soil layer according to the layered value θ of each depth layer on the soil layer. layer attributes, and then obtain the optimal sowing depth layer of the crop seeds according to the layered attributes of each depth layer on the soil layer. 6. The crop sowing depth intelligent decision-making system according to claim 5, wherein the layered attributes of each depth layer on the soil layer reflect the suitability of sowing the crop seeds on the depth layer; The smaller the stratification value of the depth layer, the better the suitability for sowing the crop seeds on the depth layer. 7. The crop sowing depth intelligent decision-making system according to claim 5, further comprising a spatial distribution processing module and a display module, the spatial distribution processing module being used for the actual soil information of the crop sowing area, the The layered attributes of each depth layer on the soil layer of the crop sowing area and the optimal sowing depth layer of the crop seeds are processed on the spatial distribution, and the processing results are displayed through the display module. 8. The crop sowing depth intelligent decision-making system according to claim 7, wherein the spatial distribution processing module comprises a GIS unit and an engine unit; Wherein, the GIS unit is used to calculate the geographic location of the crop sowing area, and combine the soil information of the crop sowing area, the layered attributes of each depth layer on the soil layer of the crop sowing area, and the crop seed The optimal sowing depth layer is marked on the digital map of the geographic location to obtain a decision-making space distribution map of crop sowing depth, and the engine unit is used to send the decision-making space distribution map of crop sowing depth to the display module. 9. An intelligent decision-making method for crop sowing depth, comprising the following steps: The soil information collection step S1 is used to collect the actual soil information of the crop sowing area; The seed information obtaining step S2 is used to obtain the seed information of the crop and obtain the soil information required for the germination of the crop seed according to the seed information; The information processing step S3 is used to obtain the actual soil information of the crop sowing area and the soil information required for the germination of the crop seeds, and compare the soil information required for the germination of the crop seeds, according to the crop sowing area The actual soil information calculates the layered value of each depth layer on the soil layer of the crop sowing area; Depth decision-making step S4, for determining the optimal sowing depth layer of the crop seeds according to the stratification value of each depth layer on the soil layer; Wherein, the soil information collection step S1 also includes: S11, judging whether the crop sowing area belongs to a certain soil data monitoring station; S12, if so, directly call the actual soil information of the crop sowing area from the soil data monitoring station; otherwise, collect the actual soil information of the crop sowing area through the soil data sensor, the actual soil information of the crop sowing area Including soil temperature T, soil moisture H, soil pH value, soil compactness D on each depth layer of the soil layer; In the information processing step S3, the calculation formula of the layered value θ of the soil layer is:

Wherein, the _best T is the optimum soil temperature for the germination of the crops, the best H is the _optimum soil humidity for the germination of the crops, the best pH is the _optimum soil pH value for the germination of the crops, and the best D is the _optimum soil pH for the germination of the crops. optimal soil compaction for germination of the crops described above. 10 . The method for intelligent decision-making of crop sowing depth according to claim 9 , wherein the soil layer has a depth, and the soil layer is divided into a plurality of depth layers within the depth range. 11 . 11. The intelligent decision-making method for crop sowing depth according to claim 9, characterized in that, said seed information acquisition step S2 also includes: S21, obtaining the crop seed information input by the user; S22. Identify the seed type according to the seed information, and search for the soil information required for the germination of the crop seeds according to the seed type. 12. The intelligent decision-making method for crop sowing depth according to claim 9, wherein the depth decision-making step S4 comprises: S41. Determine the stratification attribute of each depth layer on the soil layer according to the stratification value θ of each depth layer on the soil layer; S42. Obtain an optimum sowing depth layer of the crop seeds according to the layering attributes of each depth layer on the soil layer. 13. The intelligent decision-making method for sowing depth of crops according to claim 12, characterized in that, the hierarchical attributes of each depth layer on the soil layer reflect the suitability of sowing the crop seeds on the depth layer; The smaller the stratification value of the depth layer, the better the suitability of sowing the crop seeds on the depth layer. 14. The intelligent decision-making method for crop sowing depth according to claim 12, further comprising a spatial distribution processing step for analyzing the actual soil information of the crop sowing area, the soil layers of the crop sowing area The layered attributes of the depth layer and the optimal sowing depth layer of the crop seeds are processed on the spatial distribution, and the processing results are displayed through a display module. 15. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the intelligent decision-making method for crop sowing depth according to any one of claims 9-14 is realized. 16. An electronic device, characterized in that it comprises: processor; and a memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the method for intelligent decision-making of crop sowing depth according to any one of claims 9-14 by executing the executable instructions.

Images