Underforest breeding intelligent control system based on biodiversity monitoring

Abstract

The invention discloses an under-forest breeding intelligent control system based on biodiversity monitoring. The under-forest breeding intelligent control system comprises a data acquisition module, a preprocessing module, a database module, a data processing module and a data output module, the preprocessing module is used for acquiring real-time biological density data; the data processing module is used for determining the breeding area and controlling the balance between the food putting amount and the biological density in the breeding area; according to the method, the local ecological density is estimated according to the collected biomass quantity and variety data, and then the cultivation area required by the expected cultivation quantity is scientifically planned according to the historical data, so that ecological resources can be reasonably utilized; and finally, the under-forest biological density and the livestock breeding data are monitored in real time, so that the livestock quantity and the ecological density can be dynamically balanced, and the effects of protecting the environment and improving the ecological system are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control management, and in particular to an intelligent control system for forest farming based on biodiversity monitoring. Background Art

[0002] Understory farming refers to an agricultural production method that involves planting crops and raising livestock under forests or woodlands. Understory resources can provide food sources for livestock, and livestock manure can promote plant growth and improve the ecological environment. It achieves a win-win situation for ecology and agricultural production by rationally utilizing understory space and resources, which not only improves land use efficiency, but also helps protect the environment and promotes sustainable agricultural development.

[0003] During under-forest farming, it is necessary to supervise and control the ecological environment under the forest and the growth conditions of poultry and livestock to avoid breaking the ecological balance and achieve the goal of sustainable development. Currently, under-forest farming mostly relies on manual collection and testing, which is not only inefficient but also has high farming costs. It is also difficult to detect problems in a timely manner. When problems are found, they are often accompanied by certain economic losses and ecological damage, which increases the difficulty of governance. To solve this problem, an intelligent control system for under-forest farming based on biodiversity monitoring is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing safety protection system has a single protection type, resulting in poor protection effect and bringing certain impacts on the use of the safety protection system, and provides an intelligent control system for forest farming based on biodiversity monitoring.

[0005] The present invention solves the above technical problems through the following technical solutions. The present invention includes a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module;

[0006] The data collection module is used to collect real-time data information and expected breeding quantity information within the breeding range;

[0007] The preprocessing module is used to create a minimum monitoring area within the breeding range using the first rule, and to count the number of the first type of organisms within the minimum monitoring area to obtain real-time organism density data;

[0008] The database module is used to store historical data information and standard parameter information;

[0009] The data processing module is used to receive real-time data information, historical data information, standard parameter information, expected breeding quantity information and real-time biological density data, first determine the breeding area according to the historical data information, expected breeding quantity information and real-time biological density data, and generate a breeding area planning report; then calculate the food demand of poultry and livestock in combination with the real-time data information and standard parameter information, control the balance between the food supply and the real-time biological density in the breeding area according to the calculation result, and generate a control report;

[0010] The data output module is used to receive and output aquaculture area planning reports and control reports.

[0011] Preferably, the real-time data information includes real-time quantity information and real-time image data, and the specific processing process of the pre-processing module is:

[0012] Collect real-time image data within the breeding range and obtain first image data within the breeding range;

[0013] Use the first rule to create a minimum monitoring area;

[0014] The first rule is:

[0015] Randomly acquiring a first reference point in the first image;

[0016] Taking the first reference point as the starting point, continuously making a plurality of reference areas that are connected and gradually increase in area in any direction;

[0017] Collect the number of first-type biological species in multiple reference areas respectively;

[0018] A plane rectangular coordinate system is established with the area of ​​the reference area as the horizontal coordinate and the number of species of the first type of organisms in the reference area as the vertical coordinate to obtain a curve graph;

[0019] Acquire two second reference points with a preset size as the interval in the curve image, and calculate the type increase K of the first type of organisms. The specific calculation process is:

[0020] K = ΔM / ΔS;

[0021] Among them, ΔM is the change in the number of the first type of biological species at the two second reference points, and ΔS is the change in the area of ​​the reference area;

[0022] Calculate the increase in the different positions of the curve graph. When the increase in the value K is less than the preset threshold value Q1, obtain two second-type reference points under the current increase in the value, and obtain their area values, which are S1 and S2 respectively.

[0023] Using the formula Calculate the average area of ​​two second type reference points;

[0024] The average area As the minimum monitoring area;

[0025] Count the number of the first type of organisms A in the minimum monitoring area;

[0026] Using the formula Calculate the biomass density in the minimum monitoring area;

[0027] Use the biomass density within the minimum monitoring area as a standard to produce real-time biomass density data within the culture range.

[0028] Preferably, the process of making the minimum monitoring area further includes:

[0029] Reading first image data within the breeding range;

[0030] A standard rectangle is created with the area of ​​the minimum monitoring area as a unit, and the first image data is divided into a plurality of standard rectangles;

[0031] Randomly select n sample rectangles;

[0032] Collect the number of species of the first type of organisms in n sample rectangles, namely Ae1, Ae2, Ae3, ..., Aen;

[0033] Calculate the average data of the first type of organisms in n sample rectangles

[0034] Calculate the separation degree E1 of the number of species of the first type of organisms. The specific calculation process is:

[0035]

[0036] Among them, Aen is the number of biological species in the nth sample rectangle;

[0037] When the separation degree E1>Q preset threshold value Q2, the sample rectangle is marked;

[0038] Count the number of marked sample rectangles B;

[0039] The first mark ratio is calculated using the formula E2=B / n;

[0040] When the first mark proportion E2 is greater than the preset threshold value Q3, the first reference point is reselected to calculate the type increase K of the first type of organisms.

[0041] Preferably, the historical data information includes historical weight data and historical biological density information of livestock, and the generation process of the breeding area planning report is:

[0042] Read historical biomass density information;

[0043] Retrieve historical weight data of livestock and poultry, and obtain historical weight data of the same livestock and poultry under different historical biological density environments;

[0044] Select multiple reference individuals under different historical biological densities, and use the formula E3 = ΔMe / ΔT to calculate the rate of change of livestock weight per unit time under the preset time period;

[0045] Calculate the weight change rate of multiple reference individuals and obtain the average weight change rate

[0046] When the average weight change rate >When the preset threshold Q4 is reached, the breeding data under the biological density is marked;

[0047] Obtain the marked breeding data, which includes the breeding quantity Ne and the breeding area Se, and use the formula E4=Ne / Se to calculate the historical breeding density;

[0048] Establish a mapping relationship between historical breeding density and historical biological density;

[0049] After reading the real-time biological density data, the target breeding density Pr is obtained according to the mapping relationship;

[0050] Read the expected breeding quantity information and obtain the expected breeding quantity Nr;

[0051] Use the formula Sr=Nr / Pr to calculate the breeding area and generate a breeding area planning report.

[0052] Preferably, the real-time data information includes real-time biological density data, and the generation process of the control report is:

[0053] Collect real-time biomass density data within multiple minimum monitoring areas;

[0054] Randomly select a minimum monitoring area and continuously and evenly collect real-time biological density data in the breeding area;

[0055] The density change rate for each time period is calculated using the formula E5 = Δρ / Δt1;

[0056] Among them, Δρ is the change of biological density in the minimum monitoring area within the same time period Δt1;

[0057] Get the average density change rate in multiple equal time periods

[0058] When the average density change rate >When the preset threshold Q5 is reached, the minimum monitoring area is marked;

[0059] Get the average density change rate in different minimum monitoring areas, and get the marked minimum monitoring area;

[0060] Count the number of minimum monitoring areas C1 and the number of marked minimum monitoring areas C2;

[0061] The second mark ratio is calculated using the formula E6=C2 / C1;

[0062] When the second mark proportion E6>preset threshold Q6, the unmarked minimum monitoring area is obtained and recorded as the first type monitoring area;

[0063] Collecting real-time image data within each first type monitoring area;

[0064] Randomly select a first type of monitoring area, collect the number of poultry and livestock in the first type of monitoring area at different time nodes within a preset period of time, and obtain the average number of active poultry and livestock Nx in the first type of monitoring area;

[0065] Get the average number of livestock Nx in each first type monitoring area and calculate the average of multiple average numbers of livestock

[0066] Import the breeding area Sr and the minimum monitoring area Calculate the standard breeding quantity Ni. The specific calculation process is:

[0067]

[0068] Import the expected breeding quantity Nr and calculate the reduction of livestock ΔNi. The specific calculation process is as follows:

[0069] ΔNi=Nr-Ni, the number of livestock is reduced by ΔNi, and a first type of control report is generated.

[0070] Preferably, the real-time data information further includes real-time dimension data, and the generation process of the first type of control report further includes:

[0071] Read the reduction of livestock ΔNi and the expected breeding number Nr;

[0072] When ΔNi / Nr>preset threshold Q7, real-time image data of the breeding area is collected;

[0073] A standard circle Y1 of the breeding area is made, wherein the breeding area is within the standard circle Y1, and the standard circle Y1 has at least three intersections with the boundary of the breeding area;

[0074] Arbitrarily obtain the geometric center point D of the first type monitoring area Y2;

[0075] Collect the radius size Ry of the standard circle Y1, and then collect the size data Ld from point D to the center of the standard circle Y1;

[0076] Calculate the deviation E7 of the position of the first type of monitoring area. The specific calculation process is:

[0077] E7=Ld / Ry;

[0078] Then, the average number of active livestock Nx and the expected number of livestock Nr in the first type of monitoring area Y2 are imported to calculate the activity E8 of the first type of monitoring area. The specific calculation process is:

[0079] E8 = Nx / Nr;

[0080] When the deviation E7>preset threshold Q8 and the activity E8<preset threshold Q9, the data of the first type monitoring area Y2 is cleared and the average of the multiple average livestock numbers is recalculated.

[0081] Preferably, the process of making the standard circle Y1 further includes:

[0082] Collect real-time image data of the breeding area and obtain the geometric center point U of the breeding area;

[0083] Then obtain the center of the standard circle Y1, and collect the dimension data Lu from point U to the center of the standard circle Y1;

[0084] Import the radius size Ry of the standard circle Y1;

[0085] Calculate the deviation J1 between the center of the standard circle Y1 and the geometric center point U of the breeding area. The specific calculation process is:

[0086] J1=Lu / Ry;

[0087] When the difference J1>preset threshold W1, a standard circle Y3 is made with the geometric center point U of the breeding area as the center, the breeding area is located within the standard circle Y3, and the standard circle Y3 has at least two intersections with the boundary of the breeding area;

[0088] The standard circle Y3 is used to replace the standard circle Y2 and the deviation E7 of the position of the first type monitoring area is recalculated.

[0089] Preferably, when the second mark proportion E6≤preset threshold Q6, the control report generation process further includes:

[0090] Based on the mapping relationship between historical breeding density and historical biological density;

[0091] Get the historical biological density ρ1 of the breeding density Pr;

[0092] Collect real-time biological density data ρ2 of the first type of organisms in multiple minimum monitoring areas;

[0093] Then import the area of ​​the lowest detection area Using the formula Estimate the number of the first type of organisms in multiple minimum monitoring areas, and then obtain the average number of the first type of organisms

[0094] Using the formula Estimate the initial number Au of the first type of organisms in the minimum monitoring area;

[0095] Calculate the decrease in the number of the first type of organisms in the minimum monitoring area, J2. The specific calculation process is:

[0096]

[0097] When the decrease J2>the preset threshold W2, the feeding amount in the breeding area is increased and a second type of control report is generated.

[0098] Preferably, when the drop J2 ≤ the preset threshold W2, the real-time data information further includes real-time weight data, the historical data information further includes historical weight information, and the generation process of the control report further includes:

[0099] Import standard data information to obtain the standard food delivery amount Ge1 for a preset number of livestock;

[0100] Read the actual amount of food delivered to the preset number of livestock Ge2;

[0101] When Ge1-Ge2>preset threshold W3:

[0102] Collect the number of livestock Nz entering the cage;

[0103] The cage mass before the livestock enters the cage and the cage mass after the livestock leave the cage are collected to obtain the feces volume G1;

[0104] Use the formula Ge1 = G1 / Nz to calculate the average amount of manure per livestock;

[0105] The mass of livestock when collected in the cage G2;

[0106] The average weight of each livestock is obtained using the formula Ge2 = (G2-G1-G0) / Nz, where G0 is the weight of the cage before the livestock enters the cage;

[0107] Read historical weight information; obtain the historical feces volume Gi1 and historical weight Gi2 of each livestock when it last entered the cage;

[0108] When Ge1-Gi1>preset threshold value W4 or Ge2-Gi2<preset threshold value W5, it indicates that the growth of poultry and livestock is abnormal, and a third type of control report is generated.

[0109] Preferably, the system further comprises a service platform module, and the service platform module is used for receiving and displaying output aquaculture area planning reports and control reports.

[0110] Compared with the prior art, the present invention has the following advantages: the system first estimates the local ecological density based on the collected biological quantity and species data, and then scientifically plans the breeding area required for the expected breeding quantity based on historical data, so that ecological resources can be reasonably utilized; finally, the biological density under the forest and the breeding data of poultry and livestock are monitored in real time, so that the number of poultry and livestock and the ecological density can achieve a dynamic balance, achieving the effect of protecting the environment and improving the ecosystem; the system does not rely on manual labor, and can independently perform real-time monitoring to improve the supervision efficiency in the breeding area; at the same time, breeding problems can be discovered in time to avoid the aggravation of breeding problems, reduce the difficulty of governance, achieve the goal of efficient and low-cost breeding, and promote the sustainable development of agriculture, making the system more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 It is the overall module diagram of the present invention. DETAILED DESCRIPTION

[0112] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0113] like Figure 1 As shown, this embodiment provides a technical solution: an intelligent control system for forest farming based on biodiversity monitoring, including a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module;

[0114] The data collection module is used to collect real-time data information and expected breeding quantity information within the breeding range;

[0115] The preprocessing module is used to create a minimum monitoring area within the breeding range using the first rule, and to count the number of the first type of organisms within the minimum monitoring area to obtain real-time organism density data;

[0116] It should be noted that the first type of organisms are organisms on the diet of poultry and livestock, including animals, insects, and plants.

[0117] The database module is used to store historical data information and standard parameter information;

[0118] Standard parameter information is the aquaculture parameter comparison data pre-made based on historical aquaculture data;

[0119] The data processing module is used to receive real-time data information, historical data information, standard parameter information, expected breeding quantity information and real-time biological density data, first determine the breeding area according to the historical data information, expected breeding quantity information and real-time biological density data, and generate a breeding area planning report; then combine the real-time data information and standard parameter information to estimate the food demand of poultry and livestock, control the balance of food supply and real-time biological density in the breeding area according to the estimated results, and generate a control report;

[0120] The data output module is used to receive and output aquaculture area planning reports and control reports.

[0121] The system first estimates the local ecological density based on the collected biological quantity and species data, and then scientifically plans the breeding area required for the expected breeding quantity based on historical data, so that ecological resources can be used rationally; finally, the biological density under the forest and the breeding data of poultry and livestock are monitored in real time, so that the number of poultry and livestock and the ecological density can achieve a dynamic balance, achieving the effect of protecting the environment and improving the ecosystem; the system does not rely on manual labor, and conducts real-time monitoring independently to improve the supervision efficiency in the breeding area; at the same time, breeding problems can be discovered in time to avoid the aggravation of breeding problems, reduce the difficulty of governance, achieve efficient and low-cost breeding goals, and promote the sustainable development of agriculture.

[0122] Preferably, the real-time data information includes real-time quantity information and real-time image data, and the specific processing process of the preprocessing module is:

[0123] It should be noted that the real-time number is the result of long-term monitoring, and the number of insects can be collected using insect monitoring instruments.

[0124] The insect monitoring instrument uses modern optical, electrical, and numerical control technologies to achieve functions such as pest trapping, automatic far-infrared treatment of insect bodies, conveyor belt transportation, and automatic operation of the entire lamp to count and monitor insects.

[0125] And cooperate with high-definition cameras and infrared cameras to count and monitor the types and quantities of animals and plants. This is the existing technology and is not the core content of the invention, so it will not be repeated.

[0126] Collect real-time image data within the breeding range and obtain first image data within the breeding range;

[0127] Use the first rule to create a minimum monitoring area;

[0128] The first rule is:

[0129] Randomly acquiring a first reference point in the first image;

[0130] Taking the first reference point as the starting point, continuously making a plurality of reference areas that are connected and gradually increase in area in any direction;

[0131] Collect the number of first-type biological species in multiple reference areas respectively;

[0132] A plane rectangular coordinate system is established with the area of ​​the reference area as the horizontal coordinate and the number of species of the first type of organisms in the reference area as the vertical coordinate to obtain a curve graph;

[0133] This curve usually shows that as the sample area expands, the number of new plant species per unit area gradually decreases, the curve gradually flattens, and finally the number of organisms in the sample tends to be relatively stable;

[0134] Acquire two second reference points with a preset size as the interval in the curve image, and calculate the type increase K of the first type of organisms. The specific calculation process is:

[0135] K = ΔM / ΔS;

[0136] Among them, ΔM is the change in the number of the first type of biological species at the two second reference points, and ΔS is the change in the area of ​​the reference area;

[0137] Calculate the increase in the different positions of the curve graph. When the increase in the value K is less than the preset threshold value Q1, obtain two second-type reference points under the current increase in the value, and obtain their area values, which are S1 and S2 respectively.

[0138] When the value of the increase K is small, the number of organisms in the surface reference area is relatively stable. The biological density in the reference area can be calculated to replace the biological density in the entire breeding range. The error is relatively small and representative.

[0139] Using the formula Calculate the average area of ​​two second type reference points;

[0140] The average area As the minimum monitoring area;

[0141] Count the number of the first type of organisms A in the minimum monitoring area;

[0142] Using the formula Calculate the biomass density in the minimum monitoring area;

[0143] Use the biomass density within the minimum monitoring area as a standard to produce real-time biomass density data within the culture range.

[0144] This implementation method establishes reference areas of gradually increasing sizes within the breeding range, establishes a curve graph of area and number of biological species, and calculates the change in the number of biological species as the area increases; screens out reference areas with stable species. When the biological species are stable, the number of organisms in the area is also relatively stable; counts the number of organisms in the reference area to calculate the biological density, improves the representativeness of the biological density calculation, and thereby improves the scientificity and accuracy of the system's data collection and monitoring.

[0145] The understory environment is uncertain. Selecting a reference area in a specific direction to formulate a minimum monitoring area to estimate the biological density of the breeding range may result in large errors. In order to verify the accuracy of the biological density, the following further technical solutions are proposed:

[0146] Furthermore, the process of making the minimum monitoring area also includes:

[0147] Reading first image data within the breeding range;

[0148] A standard rectangle is created with the area of ​​the minimum monitoring area as a unit, and the first image data is divided into a plurality of standard rectangles;

[0149] Randomly select n sample rectangles;

[0150] Collect the number of species of the first type of organisms in n sample rectangles, namely Ae1, Ae2, Ae3, ..., Aen;

[0151] Calculate the average data of the first type of organisms in n sample rectangles

[0152] Calculate the separation degree E1 of the number of species of the first type of organisms. The specific calculation process is:

[0153]

[0154] Among them, Aen is the number of biological species in the nth sample rectangle;

[0155] When the separation degree E1>Q preset threshold value Q2, the sample rectangle is marked;

[0156] Count the number of marked sample rectangles B;

[0157] The first mark ratio is calculated using the formula E2=B / n;

[0158] When the first mark proportion E2 is greater than the preset threshold value Q3, the first reference point is reselected to calculate the type increase K of the first type of organisms.

[0159] Based on the area of ​​the minimum monitoring area, multiple sample rectangular areas are obtained within the breeding range, and the number of organisms in each sample rectangular area is counted respectively; when the number of organisms in each sample rectangular area is quite different, it indicates that there is a large error in the formulation of the minimum monitoring area and it needs to be re-determined; when the number of organisms in each sample rectangular area is relatively small, it indicates that the number of organisms in the minimum monitoring area is relatively stable, the formulation of the minimum monitoring area is representative, and the biological density within the breeding range is relatively accurate, thereby improving the reliability of system use.

[0160] Among them, the historical data information includes the historical weight data and historical biological density information of poultry and livestock. The generation process of the breeding area planning report is as follows:

[0161] Read historical biomass density information;

[0162] Retrieve historical weight data of livestock and poultry, and obtain historical weight data of the same livestock and poultry under different historical biological density environments;

[0163] Select multiple reference individuals under different historical biological densities, and use the formula E3 = ΔMe / ΔT to calculate the rate of change of livestock weight per unit time under the preset time period;

[0164] The rate of change of the livestock weight per unit time in the preset time period is the rate of change of the livestock weight per unit time in the livestock growth stage.

[0165] Calculate the weight change rate of multiple reference individuals and obtain the average weight change rate

[0166] When the average weight change rate >When the preset threshold Q4 is reached, the breeding data under the biological density is marked;

[0167] Obtain the marked breeding data, which includes the breeding quantity Ne and the breeding area Se, and use the formula E4=Ne / Se to calculate the historical breeding density;

[0168] Establish a mapping relationship between historical breeding density and historical biological density;

[0169] After reading the real-time biological density data, the target breeding density Pr is obtained according to the mapping relationship;

[0170] Read the expected breeding quantity information and obtain the expected breeding quantity Nr;

[0171] Use the formula Sr=Nr / Pr to calculate the breeding area and generate a breeding area planning report.

[0172] In this embodiment, the weight changes of poultry and livestock under different historical biological densities are first obtained, and breeding data that meets the requirements are screened out; the breeding density is calculated based on the breeding data, and a mapping relationship between breeding density and biological density is established, and finally the required breeding area is estimated based on the expected breeding quantity, current biological density and breeding density; the selection of this area can provide food for poultry and livestock through the ecosystem and reduce breeding costs; at the same time, the stability of the ecology is maintained and the sustainable development of agriculture is promoted; a scientific breeding area is established through the system to achieve the breeding goal of making full use of natural resources and reducing the management area.

[0173] Furthermore, the real-time data information includes real-time biomass density data, and the generation process of the control report is as follows:

[0174] Collect real-time biomass density data within multiple minimum monitoring areas;

[0175] Randomly select a minimum monitoring area and continuously and evenly collect real-time biological density data in the breeding area;

[0176] The density change rate for each time period is calculated using the formula E5 = Δρ / Δt1;

[0177] Among them, Δρ is the change of biological density in the minimum monitoring area within the same time period Δt1;

[0178] Get the average density change rate in multiple equal time periods

[0179] When the average density change rate >When the preset threshold Q5 is reached, the minimum monitoring area is marked;

[0180] Get the average density change rate in different minimum monitoring areas, and get the marked minimum monitoring area;

[0181] Count the number of minimum monitoring areas C1 and the number of marked minimum monitoring areas C2;

[0182] The second mark ratio is calculated using the formula E6=C2 / C1;

[0183] When the second mark proportion E6>preset threshold Q6, the unmarked minimum monitoring area is obtained and recorded as the first type monitoring area;

[0184] Collecting real-time image data within each first type monitoring area;

[0185] Randomly select a first type of monitoring area, collect the number of poultry and livestock in the first type of monitoring area at different time nodes within a preset period of time, and obtain the average number of active poultry and livestock Nx in the first type of monitoring area;

[0186] The number of poultry and livestock at different time nodes within the preset time period is the number of poultry and livestock at different time nodes during the livestock activity time.

[0187] Get the average number of livestock Nx in each first type monitoring area and calculate the average of multiple average numbers of livestock

[0188] Import the breeding area Sr and the minimum monitoring area Calculate the standard breeding quantity Ni. The specific calculation process is:

[0189]

[0190] Import the expected breeding quantity Nr and calculate the reduction of livestock ΔNi. The specific calculation process is as follows:

[0191] ΔNi=Nr-Ni, the number of livestock is reduced by ΔNi, and a first type of control report is generated.

[0192] The first type of control report is: the number of poultry and livestock in the breeding area is too large, causing damage to the ecosystem, and the number of poultry and livestock in the breeding area needs to be reduced.

[0193] In this embodiment, the biological density in the breeding area is monitored, and the balance between the breeding quantity and the biological density is controlled in real time; when it is found that the biological density in the breeding area drops suddenly, it indicates that the breeding quantity in the breeding area is large, which has caused damage to the ecological environment; a control report for reducing the number of poultry and livestock in the breeding area is generated; at the same time, the number of poultry and livestock activities in the smallest undamaged monitoring area in the breeding area is collected, and the effective carrying capacity of the ecosystem for poultry and livestock is calculated based on this, and finally the reduction in poultry and livestock is estimated; the breeding area is scientifically managed to avoid damage to the ecological environment and improve the breeding quality of poultry and livestock.

[0194] There are differences in the activities of livestock in different locations within the breeding area. Calculating the reduction in the number of livestock based on the average number of livestock activities in the smallest undamaged monitoring area may result in the estimated breeding number being smaller than the effective carrying capacity of the actual environment. This will lead to the inability to fully utilize natural resources during breeding and reduce the economic benefits of breeding. To improve this problem, the following further solutions are proposed:

[0195] Furthermore, the real-time data information also includes real-time dimension data, and the generation process of the first type of control report also includes:

[0196] Read the reduction of livestock ΔNi and the expected breeding number Nr;

[0197] When ΔNi / Nr>preset threshold Q7, real-time image data of the breeding area is collected;

[0198] A standard circle Y1 is made for the breeding area, the breeding area is within the standard circle Y1, and the standard circle Y1 has at least three intersections with the boundary of the breeding area;

[0199] There are at least three intersection points between the standard circle Y1 and the boundary of the breeding area to ensure that the standard circle Y1 is connected to the breeding area.

[0200] Arbitrarily obtain the geometric center point D of the first type monitoring area Y2;

[0201] Collect the radius size Ry of the standard circle Y1, and then collect the size data Ld from point D to the center of the standard circle Y1;

[0202] Calculate the deviation E7 of the position of the first type of monitoring area. The specific calculation process is:

[0203] E7=Ld / Ry;

[0204] Then, the average number of active livestock Nx and the expected number of livestock Nr in the first type of monitoring area Y2 are imported to calculate the activity E8 of the first type of monitoring area. The specific calculation process is:

[0205] E8 = Nx / Nr;

[0206] When the deviation E7>preset threshold Q8 and the activity E8<preset threshold Q9, the data of the first type monitoring area Y2 is cleared and the average of the multiple average livestock numbers is recalculated.

[0207] The smallest undamaged monitoring area is the first type of monitoring area. In this embodiment, the remoteness of the first type of monitoring area is estimated based on the degree of proximity of the first type of monitoring area to the edge of the breeding area, which is reflected by the deviation E7; and the amount of activity of poultry and livestock in the first type of monitoring area. When the first type of monitoring area is close to the edge of the breeding area and the amount of activity of poultry and livestock is low, it indicates that the first type of monitoring area is remote, the number of poultry and livestock going to this area is small, and it is not representative. The average value of the average number of poultry and livestock is estimated from this area. When this happens, the result will inevitably be smaller, causing a larger error; clearing this data to correct the system's calculated results can improve the reliability and accuracy of the system.

[0208] Furthermore, the production process of the standard circle Y1 also includes:

[0209] Collect real-time image data of the breeding area and obtain the geometric center point U of the breeding area;

[0210] Then obtain the center of the standard circle Y1, and collect the dimension data Lu from point U to the center of the standard circle Y1;

[0211] Import the radius size Ry of the standard circle Y1;

[0212] Calculate the deviation J1 between the center of the standard circle Y1 and the geometric center point U of the breeding area. The specific calculation process is:

[0213] J1=Lu / Ry;

[0214] When the difference J1> the preset threshold W1, a standard circle Y3 is made with the geometric center point U of the breeding area as the center, the breeding area is located within the standard circle Y3, and the standard circle Y3 has at least two intersections with the boundary of the breeding area;

[0215] The standard circle Y3 is used to replace the standard circle Y2 and the deviation E7 of the position of the first type monitoring area is recalculated.

[0216] When the shape of the breeding area is irregular, the distribution of the breeding area within the standard circle Y1 may be concentrated in the local area of ​​the standard circle Y1; when estimating the position of the first type of monitoring area, there may be a large deviation; when the geometric center of the breeding area deviates greatly from the circular shape of the standard circle Y1, it indicates that the breeding area is irregular. A standard circle is established with the breeding center as the center to ensure that the breeding area is distributed at the center of the standard circle, thereby improving the accuracy of data estimation.

[0217] Furthermore, when the second mark proportion E6≤preset threshold Q6, the process of generating the control report further includes:

[0218] Based on the mapping relationship between historical breeding density and historical biological density;

[0219] Get the historical biological density ρ1 of the breeding density Pr;

[0220] Collect real-time biological density data ρ2 of the first type of organisms in multiple minimum monitoring areas;

[0221] Then import the area of ​​the lowest detection area Using the formula Estimate the number of first-type organisms in multiple minimum monitoring areas, and then obtain the average number of first-type organisms

[0222] Using the formula Estimate the initial number Au of the first type of organisms in the minimum monitoring area;

[0223] Calculate the decrease in the number of the first type of organisms in the minimum monitoring area, J2. The specific calculation process is:

[0224]

[0225] When the decrease J2>the preset threshold W2, the feeding amount in the breeding area is increased and a second type of control report is generated.

[0226] The second type of control report is: increase the amount of food in the breeding area and improve the ecological environment.

[0227] When the decrease in biological density in the breeding area is within a reasonable range, but the number of organisms changes greatly, it indicates that the amount of food supplied to the breeding area is small, and long-term development is not conducive to the stability of the ecological environment and the growth of poultry and livestock. It is necessary to increase the amount of food supplied to maintain a balanced relationship between biological density and breeding quantity in the breeding area, so as to promote the sustainable development of the breeding industry and improve the economic benefits of breeding.

[0228] Furthermore, when the decrease J2 ≤ the preset threshold W2, the real-time data information also includes real-time weight data, the historical data information also includes historical weight information, and the generation process of the control report also includes:

[0229] Import standard data information to obtain the standard food delivery amount Ge1 for a preset number of livestock;

[0230] Read the actual amount of food delivered to the preset number of livestock Ge2;

[0231] When Ge1-Ge2>preset threshold W3:

[0232] Collect the number of livestock Nz entering the cage;

[0233] The cage mass before the livestock enters the cage and the cage mass after the livestock leave the cage are collected to obtain the feces volume G1;

[0234] Use the formula Ge1 = G1 / Nz to calculate the average amount of manure per livestock;

[0235] The mass of livestock when collected in the cage G2;

[0236] The average weight of each livestock is obtained using the formula Ge2 = (G2-G1-G0) / Nz, where G0 is the weight of the cage before the livestock enters the cage;

[0237] Read historical weight information; obtain the historical feces volume Gi1 and historical weight Gi2 of each livestock when it last entered the cage;

[0238] When Ge1-Gi1>preset threshold value W4 or Ge2-Gi2<preset threshold value W5, it indicates that the growth of poultry and livestock is abnormal, and a third type of control report is generated.

[0239] The third type of control report is the medication administration report.

[0240] A decrease J2 ≤ the preset threshold W2 indicates that the amount of food obtained by livestock from the ecosystem is small, and they rely on the input of food to provide energy for growth; when the amount of livestock feces is large, the weight gain is small or there is negative growth within a certain period of time, it indicates that the livestock may be sick, and the release of drugs should be controlled in time and a report should be issued to treat the livestock, so as to avoid delaying treatment time and causing large economic losses, thereby improving the practicality of the system.

[0241] The system also includes a service platform module, which is used to receive and display output aquaculture area planning reports and control reports.

[0242] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0243] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0244] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An intelligent control system for forest farming based on biodiversity monitoring, characterized in that: It includes data acquisition module, preprocessing module, database module, data processing module and data output module; The data collection module is used to collect real-time data information and expected breeding quantity information within the breeding range; The preprocessing module is used to create a minimum monitoring area within the breeding range using the first rule, and to count the number of the first type of organisms within the minimum monitoring area to obtain real-time organism density data; The database module is used to store historical data information and standard parameter information; The data processing module is used to receive real-time data information, historical data information, standard parameter information, expected breeding quantity information and real-time biological density data, first determine the breeding area according to the historical data information, expected breeding quantity information and real-time biological density data, and generate a breeding area planning report; then calculate the food demand of poultry and livestock in combination with the real-time data information and standard parameter information, control the balance between the food supply and the real-time biological density in the breeding area according to the calculation result, and generate a control report; The data output module is used to receive and output aquaculture area planning reports and control reports.

2. According to claim 1, an intelligent control system for forest farming based on biodiversity monitoring is characterized by: The real-time data information includes real-time quantity information and real-time image data. The specific processing process of the pre-processing module is as follows: Collect real-time image data within the breeding range and obtain first image data within the breeding range; Use the first rule to create a minimum monitoring area; The first rule is: Randomly acquiring a first reference point in the first image; Taking the first reference point as the starting point, continuously making a plurality of reference areas that are connected and gradually increase in area in any direction; Collect the number of first-type biological species in multiple reference areas respectively; A plane rectangular coordinate system is established with the area of ​​the reference area as the horizontal coordinate and the number of species of the first type of organisms in the reference area as the vertical coordinate to obtain a curve graph; Acquire two second reference points with a preset size as the interval in the curve image, and calculate the type increase K of the first type of organisms. The specific calculation process is: K = ΔM / ΔS; Among them, ΔM is the change in the number of the first type of biological species at the two second reference points, and ΔS is the change in the area of ​​the reference area; The increase in amplitude at different positions of the curve graph is calculated. When the increase in amplitude K is less than the preset threshold Q1, two second-type reference points under the current increase in amplitude are obtained, and their area values ​​are obtained, which are S1 and S2 respectively; Using the formula Calculate the average area of ​​two second type reference points; The average area As the minimum monitoring area; Count the number of the first type of organisms A in the minimum monitoring area; Using the formula Calculate the biomass density in the minimum monitoring area; Use the biomass density within the minimum monitoring area as a standard to produce real-time biomass density data within the culture range.

3. According to claim 2, an intelligent control system for forest farming based on biodiversity monitoring is characterized by: The process of making the minimum monitoring area also includes: Reading first image data within the breeding range; A standard rectangle is created with the area of ​​the minimum monitoring area as a unit, and the first image data is divided into a plurality of standard rectangles; Randomly select n sample rectangles; Collect the number of species of the first type of organisms in n sample rectangles, namely Ae1, Ae2, Ae3, ..., Aen; Calculate the average data of the first type of organisms in n sample rectangles Calculate the separation degree E1 of the number of species of the first type of organisms. The specific calculation process is: Among them, Aen is the number of biological species in the nth sample rectangle; When the separation degree E1>Q preset threshold value Q2, the sample rectangle is marked; Count the number of marked sample rectangles B; The first mark ratio is calculated using the formula E2=B / n; When the first mark proportion E2 is greater than the preset threshold value Q3, the first reference point is reselected to calculate the type increase K of the first type of organisms.

4. According to claim 1, an intelligent control system for forest farming based on biodiversity monitoring is characterized by: The historical data information includes historical weight data and historical biological density information of livestock. The generation process of the breeding area planning report is as follows: Read historical biomass density information; Retrieve historical weight data of livestock and poultry, and obtain historical weight data of the same livestock and poultry under different historical biological density environments; Select multiple reference individuals under different historical biological densities, and use the formula E3 = ΔMe / ΔT to calculate the rate of change of livestock weight per unit time under the preset time period; Calculate the weight change rate of multiple reference individuals and obtain the average weight change rate When the average weight change rate >When the preset threshold Q4 is reached, the breeding data under the biological density is marked; Obtain the marked breeding data, which includes the breeding quantity Ne and the breeding area Se, and use the formula E4=Ne / Se to calculate the historical breeding density; Establish a mapping relationship between historical breeding density and historical biological density; After reading the real-time biological density data, the target breeding density Pr is obtained according to the mapping relationship; Read the expected breeding quantity information and obtain the expected breeding quantity Nr; Use the formula Sr=Nr / Pr to calculate the breeding area and generate a breeding area planning report.

5. An intelligent control system for forest farming based on biodiversity monitoring according to claim 2 or 4, characterized in that: The real-time data information includes real-time biological density data, and the generation process of the control report is: Collect real-time biomass density data within multiple minimum monitoring areas; Randomly select a minimum monitoring area and continuously and evenly collect real-time biological density data in the breeding area; The density change rate for each time period is calculated using the formula E5 = Δρ / Δt1; Among them, Δρ is the change in biomass density in the minimum monitoring area within the same time period Δt1; Get the average density change rate in multiple equal time periods When the average density change rate >When the preset threshold Q5 is reached, the minimum monitoring area is marked; Get the average density change rate in different minimum monitoring areas, and get the marked minimum monitoring area; Count the number of minimum monitoring areas C1 and the number of marked minimum monitoring areas C2; The second mark ratio is calculated using the formula E6=C2 / C1; When the second mark proportion E6>preset threshold Q6, the unmarked minimum monitoring area is obtained and recorded as the first type monitoring area; Collecting real-time image data within each first type monitoring area; Randomly select a first type of monitoring area, collect the number of poultry and livestock in the first type of monitoring area at different time nodes within a preset period of time, and obtain the average number of active poultry and livestock Nx in the first type of monitoring area; Get the average number of livestock Nx in each first type monitoring area and calculate the average of multiple average numbers of livestock Import the breeding area Sr and the minimum monitoring area Calculate the standard breeding quantity Ni. The specific calculation process is: Import the expected breeding quantity Nr and calculate the reduction of livestock ΔNi. The specific calculation process is as follows: ΔNi=Nr-Ni, the number of livestock is reduced by ΔNi, and a first type of control report is generated.

6. The intelligent control system for forest farming based on biodiversity monitoring according to claim 5 is characterized by: The real-time data information also includes real-time size data, and the generation process of the first type of control report also includes: Read the reduction of livestock ΔNi and the expected breeding number Nr; When ΔNi / Nr>preset threshold Q7, real-time image data of the breeding area is collected; A standard circle Y1 of the breeding area is made, wherein the breeding area is within the standard circle Y1, and the standard circle Y1 has at least three intersections with the boundary of the breeding area; Arbitrarily obtain the geometric center point D of the first type monitoring area Y2; Collect the radius size Ry of the standard circle Y1, and then collect the size data Ld from point D to the center of the standard circle Y1; Calculate the deviation E7 of the position of the first type of monitoring area. The specific calculation process is: E7=Ld / Ry; Then, the average number of active livestock Nx and the expected number of livestock Nr in the first type of monitoring area Y2 are imported to calculate the activity level E8 of the first type of monitoring area. The specific calculation process is: E8 = Nx / Nr; When the deviation E7>preset threshold Q8 and the activity E8<preset threshold Q9, the data of the first type monitoring area Y2 is cleared and the average of the multiple average livestock numbers is recalculated.

7. The intelligent control system for forest farming based on biodiversity monitoring according to claim 6 is characterized by: The manufacturing process of the standard circle Y1 also includes: Collect real-time image data of the breeding area and obtain the geometric center point U of the breeding area; Then obtain the center of the standard circle Y1, and collect the dimension data Lu from point U to the center of the standard circle Y1; Import the radius size Ry of the standard circle Y1; Calculate the deviation J1 between the center of the standard circle Y1 and the geometric center point U of the breeding area. The specific calculation process is: J1=Lu / Ry; When the difference J1>preset threshold W1, a standard circle Y3 is made with the geometric center point U of the breeding area as the center, the breeding area is located within the standard circle Y3, and the standard circle Y3 has at least two intersections with the boundary of the breeding area; The standard circle Y3 is used to replace the standard circle Y2 and the deviation E7 of the position of the first type monitoring area is recalculated.

8. The intelligent control system for forest farming based on biodiversity monitoring according to claim 5 is characterized by: When the second mark proportion E6≤preset threshold Q6, the generation process of the control report further includes: Based on the mapping relationship between historical breeding density and historical biological density; Get the historical biological density ρ1 of the breeding density Pr; Collect real-time biological density data ρ2 of the first type of organisms in multiple minimum monitoring areas; Then import the area of ​​the lowest detection area Using the formula Estimate the number of the first type of organisms in multiple minimum monitoring areas, and then obtain the average number of the first type of organisms Using the formula Estimate the initial number Au of the first type of organisms in the minimum monitoring area; Calculate the decrease in the number of the first type of organisms in the minimum monitoring area, J2. The specific calculation process is: When the decrease J2>the preset threshold W2, the feeding amount in the breeding area is increased and a second type of control report is generated.

9. The intelligent control system for forest farming based on biodiversity monitoring according to claim 8 is characterized by: When the drop J2≤preset threshold W2, the real-time data information further includes real-time weight data, the historical data information further includes historical weight information, and the generation process of the control report further includes: Import standard data information to obtain the standard food delivery amount Ge1 for a preset number of livestock; Read the actual amount of food delivered to the preset number of livestock Ge2; When Ge1-Ge2>preset threshold W3: Collect the number of livestock Nz entering the cage; The cage mass before the livestock enters the cage and the cage mass after the livestock leave the cage are collected to obtain the feces volume G1; Use the formula Ge1 = G1 / Nz to calculate the average amount of manure per livestock; The mass of livestock when collected in the cage G2; The average weight of each livestock is obtained using the formula Ge2 = (G2-G1-G0) / Nz, where G0 is the weight of the cage before the livestock enters the cage; Read historical weight information; obtain the historical feces volume Gi1 and historical weight Gi2 of each livestock when it last entered the cage; When Ge1-Gi1>preset threshold value W4 or Ge2-Gi2<preset threshold value W5, it indicates that the growth of poultry and livestock is abnormal, and a third type of control report is generated.

10. The intelligent control system for forest farming based on biodiversity monitoring according to claim 1 is characterized by: It also includes a service platform module, which is used to receive and display output aquaculture area planning reports and control reports.

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