CN116431952A - Grassland ecological monitoring method and system based on artificial intelligence - Google Patents

Grassland ecological monitoring method and system based on artificial intelligence Download PDF

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CN116431952A
CN116431952A CN202310282883.9A CN202310282883A CN116431952A CN 116431952 A CN116431952 A CN 116431952A CN 202310282883 A CN202310282883 A CN 202310282883A CN 116431952 A CN116431952 A CN 116431952A
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CN116431952B (en
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白羽萍
胡业翠
周伟
高梦雯
王艺伟
翁楚尧
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China University of Geosciences Beijing
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Abstract

The invention discloses a grassland ecological monitoring method and system based on artificial intelligence, which are characterized in that plant production efficiency is obtained by calculating real-time air temperature data and real-time precipitation data of a grassland to be detected, theoretical grass yield is calculated based on the plant production efficiency, the pasture availability and grazing intensity of the grassland to be detected are determined by obtaining the livestock quantity and livestock feeding data of the grassland to be detected, production efficiency wallpaper and grazing intensity interpolation are obtained by calculating based on a plant production efficiency threshold and a grazing intensity threshold, and vegetation attenuation rate is obtained; the method and the device are beneficial to the grazing staff to judge whether the forage grass feeding amount of the livestock cultivated in the grassland exceeds the forage grass feeding amount of the grassland based on the monitoring result of the edible grassland forage grass amount, so that the harm of grassland degradation is avoided.

Description

Grassland ecological monitoring method and system based on artificial intelligence
Technical Field
The invention relates to the field of environmental protection service, in particular to a grassland ecological monitoring method and system based on artificial intelligence.
Background
The grassland ecology is one of natural resources with larger occupied area in China, the animal husbandry is developed by virtue of the grassland ecology environment, if the animal husbandry is developed too fast, the grassland grass yield is influenced, the grassland ecology is destroyed, and the degradation and unrecoverable result of the grassland is further caused, so that the quantitative monitoring of the grassland yield, especially the grassland resources, is beneficial to the user to determine the raising scale of the livestock based on the monitoring result of the grassland resources, thereby avoiding the excessive livestock stocking and reducing the risk of the grassland degradation. However, the prior art cannot effectively monitor the amount of pasture.
Thus, there is a need for a grass ecology monitoring strategy that addresses the problem of excessive grazing due to uncertainty in the amount of grass.
Disclosure of Invention
The embodiment of the invention provides a grassland ecological monitoring method and system based on artificial intelligence, which aim to solve the problem of excessive grazing caused by uncertain amount of pasture.
In order to solve the above problems, an embodiment of the present invention provides an artificial intelligence grassland ecological monitoring method, including:
acquiring real-time air temperature data and real-time precipitation data of a to-be-detected grassland, and calculating the primary production efficiency of plants based on the real-time air temperature data and the real-time precipitation data to obtain the plant production efficiency of the to-be-detected grassland;
Calculating the grass yield of the vegetation production efficiency to obtain the theoretical grass yield of the grassland to be detected;
acquiring the quantity of livestock in the grassland to be tested and feeding data of the livestock, determining the pasture availability of the grassland to be tested based on the quantity of the livestock, and determining the grazing intensity of the grassland to be tested based on the quantity of the livestock and the feeding data;
calculating to obtain a production efficiency ratio of the plant production efficiency to a plant production efficiency threshold value and a grazing intensity difference value of the grazing intensity to a grazing intensity threshold value, and determining a vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value;
and calculating the pasture amount based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass preservation rate, so as to obtain the edible pasture amount of the grassland to be detected.
As an improvement of the above, the weather data includes: temperature data and precipitation data; the calculating the plant production efficiency of the grassland to be measured based on the meteorological data comprises the following steps:
calculating plant production efficiency of the grassland to be detected based on the air temperature data and the precipitation data; the plant production efficiency is calculated according to the following calculation formula, and the calculation method specifically comprises the following steps:
NPP(t)＝r 1 ·P(t)+r 2 ·T(t)+j
Wherein r is 1 And r 2 The fitting coefficients obtained based on historical precipitation data and historical air temperature data are calculated, P (T) is precipitation data of a to-be-measured grassland in a T time period, T (T) is air temperature data of the to-be-measured grassland in the T time period, j is a constant term, and NPP (T) is plant production efficiency of the to-be-measured grassland in the T time period.
As an improvement of the above-mentioned scheme, the calculating the theoretical grass yield of the grassland to be measured according to the plant production efficiency includes:
calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency; the theoretical grass yield is calculated according to the following formula, and the theoretical grass yield is calculated specifically as follows:
Figure BDA0004138632770000031
wherein B (t) is the theoretical grass yield of the grassland to be detected in unit area within t time, and the unit is g/(m-2*a-1); NPP (t) is the plant production efficiency of the grassland to be detected within t time, and the unit gC/(m-2*a-1); s is S bn Converting grassland biomass of a grassland to be detected into a conversion coefficient of NPP; s is S ug Is the ratio coefficient of biomass of underground and overground parts of the grassland to be measured.
As an improvement to the above, the feeding data includes: daily feed and days of eating for single livestock; the determining of the grazing intensity of the grassland to be tested based on the theoretical grass yield, the livestock quantity and the feeding data comprises:
According to the theoretical grass yield, the livestock quantity and the feeding data, calculating and obtaining grazing intensity of the grassland to be detected; the grazing intensity is calculated according to the following formula, and the grazing intensity is calculated specifically as follows:
Figure BDA0004138632770000032
wherein G is w (t) is the feed intake of livestock in t time, GI (t) is grazing intensity of grassland to be tested in t time, N w (t) is the number of animals in t time, I is the daily ration of a single animal, D w Is days of eating, B w(t) The theoretical grass yield of the grassland to be measured in the t time is obtained.
As an improvement of the above-described aspect, the edible pasture amount includes a warm season edible pasture amount and a cold season edible pasture amount, the theoretical yield includes a warm season theoretical yield and a cold season theoretical yield, the pasture availability includes a warm season pasture availability and a cold season pasture availability, the vegetation decay rate includes a warm season vegetation decay rate and a cold season vegetation decay rate, the meadow area includes a warm season meadow area and a cold season meadow area, and the meadow preservation rate includes a warm season meadow preservation rate and a cold season meadow preservation rate; the calculating the edible pasture amount of the to-be-measured grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass conservation rate comprises the following steps:
When the grassland to be measured is in a warm season time period, calculating and obtaining warm season edible grassland quantity of the grassland to be measured according to the warm season theoretical grassland yield, the warm season grassland availability, the warm season vegetation attenuation rate, the warm season grassland area and the warm season grassland preservation rate; the warm season edible pasture amount is calculated according to the following formula, and the method is as follows:
F lw (t)＝B (t1) ·A w(t1) ·η 1 ·a (t1) ·γ 1
wherein F is lw B, the warm season edible pasture quantity of the grassland to be detected is (t1) For the theoretical grass yield per unit area in the t1 time period, A w(t1) Is the area of the grassland in warm season, eta 1 A is the availability of pasture in warm season (t1) Is the vegetation attenuation rate in warm seasons, gamma 1 The preservation rate of the warm season grass is 1.
As an improvement of the above-mentioned scheme, the calculating the edible pasture amount of the to-be-measured grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation, the grazing intensity, the grassland area and the grass conservation rate further includes:
when the grassland to be detected is in a cold season time period, calculating and obtaining the cold season edible grassland quantity of the grassland to be detected according to the cold season theoretical grassland yield, the cold season grassland availability, the cold season vegetation attenuation rate, the grazing intensity, the cold season grassland area and the cold season grassland preservation rate; the calculation of the cold season edible pasture amount is carried out according to the following formula, and the method is as follows:
Figure BDA0004138632770000041
Wherein G is w (t 1) is the grass forage feed intake of the grasslands in the warm season t1 time period, and GI (t 1) is the grass forage feed intake in the warm season t1 time periodGrazing intensity of grassland to be measured, F lc (t 2) is the cold season edible pasture quantity of the grassland to be detected, B (t2) For the theoretical grass yield per unit area in the t2 period, A c(t2) Is the area of the grassland in cold seasons, eta 2 A is the availability of forage grass in cold season (t2) Is the vegetation attenuation rate in cold seasons, gamma 2 Is the preservation rate of the cold season grass.
As an improvement of the above scheme, the obtaining the number of livestock in the grassland to be tested includes:
acquiring a thermal imaging image of the back of livestock of a grassland to be detected;
extracting the number of hot spots and a plurality of hot spot areas corresponding to the livestock back thermal imaging map;
determining the quantity of livestock of the grassland to be detected according to the quantity of the hot spots;
obtaining single livestock daily feed of the livestock, comprising: and determining the livestock body type corresponding to each hot spot area according to the hot spot areas, and obtaining the daily feed quantity of the single livestock corresponding to each hot spot area based on the livestock body type matching.
Correspondingly, an embodiment of the invention also provides a grassland ecological monitoring system based on artificial intelligence, which comprises: the system comprises a plant production efficiency calculation module, a theoretical grass yield calculation module, a grazing intensity calculation module, a grass availability calculation module, a vegetation attenuation rate calculation module and an edible grass amount calculation module;
The plant production efficiency calculation module is used for acquiring meteorological data of a grassland to be detected and calculating plant production efficiency of the grassland to be detected based on the meteorological data;
the theoretical grass yield calculation module is used for calculating and obtaining the theoretical grass yield of the grassland to be detected according to the plant production efficiency;
the grazing intensity calculation module is used for acquiring the quantity of livestock of the grassland to be detected and feeding data of the livestock, and determining the grazing intensity of the grassland to be detected based on the theoretical grass yield, the quantity of the livestock and the feeding data;
the pasture availability calculation module is used for determining the pasture availability of the to-be-detected grassland based on the type of the to-be-detected grassland; wherein different types of grasslands to be detected correspond to different forage grass availability rates respectively;
the vegetation attenuation rate calculation module is used for calculating a production efficiency ratio of the plant production efficiency to a preset plant production efficiency threshold value and a grazing intensity difference value of the grazing intensity and the preset grazing intensity threshold value, and determining the vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value;
The edible pasture amount calculating module is used for calculating the edible pasture amount of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass preservation rate.
As an improvement of the above, the weather data includes: temperature data and precipitation data; the calculating the plant production efficiency of the grassland to be measured based on the meteorological data comprises the following steps:
calculating plant production efficiency of the grassland to be detected based on the air temperature data and the precipitation data; the plant production efficiency is calculated according to the following calculation formula, and the calculation method specifically comprises the following steps:
NPP(t)＝r 1 ·P(t)+r 2 ·T(t)+j
wherein r is 1 And r 2 The fitting coefficients obtained based on historical precipitation data and historical air temperature data are calculated, P (T) is precipitation data of a to-be-measured grassland in a T time period, T (T) is air temperature data of the to-be-measured grassland in the T time period, j is a constant term, and NPP (T) is plant production efficiency of the to-be-measured grassland in the T time period.
As an improvement of the above scheme, the theoretical grass yield calculation module includes:
calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency; the theoretical grass yield is calculated according to the following formula, and the theoretical grass yield is calculated specifically as follows:
Figure BDA0004138632770000061
Wherein B (t) is the theoretical grass yield of the grassland to be detected in unit area within t time, and the unit is g/(m-2*a-1); NPP (t) is the plant production efficiency of the grassland to be detected within t time, and the unit gC/(m-2*a-1); s is S bn Converting grassland biomass of a grassland to be detected into a conversion coefficient of NPP; s is S ug Is the ratio coefficient of biomass of underground and overground parts of the grassland to be measured.
As an improvement to the above, the feeding data includes: daily feed and days of eating for single livestock; the determining of the grazing intensity of the grassland to be tested based on the theoretical grass yield, the livestock quantity and the feeding data comprises:
according to the theoretical grass yield, the livestock quantity and the feeding data, calculating and obtaining grazing intensity of the grassland to be detected; the grazing intensity is calculated according to the following formula, and the grazing intensity is calculated specifically as follows:
Figure BDA0004138632770000062
wherein G is w (t) is the feed intake of livestock in t time, GI (t) is grazing intensity of grassland to be tested in t time, N w (t) is the number of animals in t time, I is the daily ration of a single animal, D w Is days of eating, B w(t) The theoretical grass yield of the grassland to be measured in the t time is obtained.
As an improvement of the above-described aspect, the edible pasture amount includes a warm season edible pasture amount and a cold season edible pasture amount, the theoretical yield includes a warm season theoretical yield and a cold season theoretical yield, the pasture availability includes a warm season pasture availability and a cold season pasture availability, the vegetation decay rate includes a warm season vegetation decay rate and a cold season vegetation decay rate, the meadow area includes a warm season meadow area and a cold season meadow area, and the meadow preservation rate includes a warm season meadow preservation rate and a cold season meadow preservation rate; the edible pasture amount calculating module comprises: a warm season edible pasture amount calculating unit;
The warm season edible pasture amount calculating unit is used for calculating and obtaining the warm season edible pasture amount of the to-be-measured grassland according to the warm season theoretical grass yield, the warm season pasture availability, the warm season vegetation attenuation rate, the warm season grass field area and the warm season grass preservation rate when the to-be-measured grassland is in a warm season time period; the warm season edible pasture amount is calculated according to the following formula, and the method is as follows:
F lw (t)＝B (t1) ·A w(t1) ·η 1 ·a (t1) ·γ 1
wherein F is lw B, the warm season edible pasture quantity of the grassland to be detected is (t1) For the theoretical grass yield per unit area in the t1 time period, A w(t1) Is the area of the grassland in warm season, eta 1 A is the availability of pasture in warm season (t1) Is the vegetation attenuation rate in warm seasons, gamma 1 The preservation rate of the warm season grass is 1.
As an improvement of the above solution, the edible pasture amount calculating module further includes: a cold season edible pasture amount calculating unit;
the cold season edible pasture amount calculating unit is used for calculating and obtaining the cold season edible pasture amount of the to-be-detected grassland according to the cold season theoretical pasture amount, the cold season pasture availability, the cold season vegetation attenuation rate, the grazing intensity, the cold season grassland area and the cold season pasture preservation rate when the to-be-detected grassland is in the cold season time period; the calculation of the cold season edible pasture amount is carried out according to the following formula, and the method is as follows:
Figure BDA0004138632770000081
Wherein G is w (t 1) is the grass feed intake of the grasslands in the period of t1 of the warm season, GI (t 1) is the grazing intensity of the grasslands to be detected in the period of t1 of the warm season, F lc (t 2) is the cold season edible pasture quantity of the grassland to be detected, B (t2) For the theoretical grass yield per unit area in the t2 period, A c(t2) Is the area of the grassland in cold seasons, eta 2 A is the availability of forage grass in cold season (t2) Is the vegetation attenuation rate in cold seasons, gamma 2 Is coldThe preservation rate of the quaternary grass.
In this embodiment, obtaining the number of livestock in the grassland to be tested includes:
acquiring a thermal imaging image of the back of livestock of a grassland to be detected;
extracting the number of hot spots and a plurality of hot spot areas corresponding to the livestock back thermal imaging map;
determining the quantity of livestock of the grassland to be detected according to the quantity of the hot spots;
obtaining single livestock daily feed of the livestock, comprising: and determining the livestock body type corresponding to each hot spot area according to the hot spot areas, and obtaining the daily feed quantity of the single livestock corresponding to each hot spot area based on the livestock body type matching.
Correspondingly, an embodiment of the invention also provides a grassland ecological monitoring system based on artificial intelligence, which comprises: the artificial intelligence-based grassland ecological monitoring method is applied to a data processor, a meteorological detection device, a livestock quantity counting device, a livestock breeding decision module and a basic database of the grassland ecological monitoring method; the input end of the data processor is respectively connected with the weather detection device and the livestock quantity counting device; the output end of the data processor is connected with the livestock breeding decision module, and the basic database is respectively connected with the data processor, the weather detection device, the livestock quantity counting device and the livestock breeding decision module; the basic database comprises animal husbandry basic data and grassland meteorological basic data;
The weather detection device is used for detecting and obtaining weather data of a grassland to be detected;
the livestock quantity counting device is used for counting the livestock quantity of the grassland to be tested;
the data processor is used for calculating the edible pasture amount according to the meteorological data and the livestock data;
the livestock cultivation decision-making module is used for making cultivation decision according to the edible pasture quantity, the theoretical pasture yield and the cultivation cost of herders; wherein the cultivation decision comprises: increasing livestock cultivation strategies, reducing livestock cultivation strategies and replacing cultivation grassland strategies.
Correspondingly, an embodiment of the invention also provides a computer terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the artificial intelligence-based grassland ecological monitoring method when executing the computer program.
Correspondingly, an embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program controls equipment where the computer readable storage medium is located to execute the artificial intelligence-based grassland ecological monitoring method according to the invention when running.
From the above, the invention has the following beneficial effects:
the invention provides an artificial intelligence-based grassland ecological monitoring method, which is characterized in that the real-time air temperature data and the real-time rainfall data of a grassland to be detected are obtained, the plant production efficiency is calculated, the theoretical grassland yield is calculated based on the plant production efficiency, the grass availability and grazing intensity of the grassland to be detected are determined by obtaining the livestock quantity and the livestock feeding data of the grassland to be detected, the production efficiency wallpaper and the grazing intensity interpolation are calculated based on the plant production efficiency threshold value and the grazing intensity threshold value, so that the vegetation attenuation rate is obtained, and finally the grassland yield is calculated based on the theoretical grassland yield, the grassland availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grassland preservation rate, so that the edible grassland yield of the grassland to be detected is obtained, thereby completing the monitoring of the grassland edible grassland, being beneficial to grazing personnel judging whether the grassland feed of the grassland is exceeded or not based on the monitoring result of the grassland edible grassland yield, and further controlling the grazing intensity, thereby avoiding the ecological harm of grassland degradation caused by the grazing intensity overage, and being beneficial to sustainable development of the grassland.
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FIG. 1 is a schematic flow chart of a grassland ecological monitoring method based on artificial intelligence according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a grassland ecological monitoring system based on artificial intelligence according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of an livestock breeding monitoring system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a grassland ecological monitoring method based on artificial intelligence according to an embodiment of the invention, as shown in fig. 1, the embodiment includes steps 101 to 106, and the steps are specifically as follows:
step 101: and acquiring meteorological data of the grassland to be detected, and calculating the plant production efficiency of the grassland to be detected based on the meteorological data.
In this embodiment, the meteorological data includes: temperature data and precipitation data; the calculating the plant production efficiency of the grassland to be measured based on the meteorological data comprises the following steps:
calculating plant production efficiency of the grassland to be detected based on the air temperature data and the precipitation data; the plant production efficiency is calculated according to the following calculation formula, and the calculation method specifically comprises the following steps:
NPP(t)＝r 1 ·P(t)+r 2 ·T(t)+j
wherein r is 1 And r 2 Respectively obtaining fitting coefficients based on historical precipitation data and historical air temperature data, wherein P (T) is precipitation data of a to-be-measured grassland in a T period, T (T) is air temperature data of the to-be-measured grassland in the T period, j is a constant term, and NPP (T) is the to-be-measured grassland in the T periodPlant production efficiency in the room.
In a specific embodiment, the fitting coefficient r is obtained by acquiring historical precipitation data and historical air temperature data and combining a regression equation 1 And r 2 And the acquisition of constant terms can be adaptively adjusted according to the calculation accuracy of the NPP.
Step 102: and calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency.
In this embodiment, the calculating the theoretical grass yield of the grassland to be measured according to the plant production efficiency includes:
Calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency; the theoretical grass yield is calculated according to the following formula, and the theoretical grass yield is calculated specifically as follows:
Figure BDA0004138632770000111
wherein B (t) is the theoretical grass yield of the grassland to be detected in unit area within t time, and the unit is g/(m-2*a-1); NPP (t) is the plant production efficiency of the grassland to be detected within t time, and the unit gC/(m-2*a-1); s is S bn Converting grassland biomass of a grassland to be detected into a conversion coefficient of NPP; s is S ug Is the ratio coefficient of biomass of underground and overground parts of the grassland to be measured.
In a specific embodiment, S bn S corresponding to warm grassland of 0.45 ug 4.25.
Step 103: and acquiring the quantity of livestock of the grassland to be tested and feeding data of the livestock, and determining the grazing intensity of the grassland to be tested based on the theoretical grass yield, the quantity of the livestock and the feeding data.
In this embodiment, the feeding data includes: daily feed and days of eating for single livestock; the determining of the grazing intensity of the grassland to be tested based on the theoretical grass yield, the livestock quantity and the feeding data comprises:
according to the theoretical grass yield, the livestock quantity and the feeding data, calculating and obtaining grazing intensity of the grassland to be detected; the grazing intensity is calculated according to the following formula, and the grazing intensity is calculated specifically as follows:
Figure BDA0004138632770000121
Wherein G is w (t) is the feed intake of livestock in t time, GI (t) is grazing intensity of grassland to be tested in t time, N w (t) is the number of animals in t time, I is the daily ration of a single animal, D w Is days of eating, B w(t) The theoretical grass yield of the grassland to be measured in the t time is obtained.
In this embodiment, obtaining the number of livestock in the grassland to be tested includes:
acquiring a thermal imaging image of the back of livestock of a grassland to be detected;
extracting the number of hot spots and a plurality of hot spot areas corresponding to the livestock back thermal imaging map;
determining the quantity of livestock of the grassland to be detected according to the quantity of the hot spots;
obtaining single livestock daily feed of the livestock, comprising: and determining the livestock body type corresponding to each hot spot area according to the hot spot areas, and obtaining the daily feed quantity of the single livestock corresponding to each hot spot area based on the livestock body type matching.
In a specific embodiment, through combining infrared thermal imaging equipment with unmanned aerial vehicle equipment, carrying out hot spot quantity statistics on livestock grazing land in high air, dividing a grassland to be detected into a plurality of grazing areas according to the definition of each infrared thermal imaging equipment, distributing a combining device of the unmanned aerial vehicle and the infrared thermal imaging equipment in each livestock grazing area, and carrying out infrared thermal imaging shooting on the back of the livestock based on the combining device to obtain a thermal imaging image of the back of the livestock;
The number of livestock is summarized by counting the number of hot spots obtained by each infrared thermal imaging device.
In a specific embodiment, the hot spots obtained by the infrared thermal imaging device are respectively identified, the hot spot area is judged, and the body type of the livestock is judged based on the hot spot area: if the area of the hot spot is larger than a first pixel threshold (the pixel threshold can be adaptively adjusted according to the climbing height of the infrared thermal imaging equipment and the unmanned aerial vehicle used by a user), the adult livestock is judged to be adult, and the feeding amount of the adult livestock is three jin of pasture; if the area of the hot spot is smaller than the first pixel threshold value and larger than the second pixel threshold value, the young livestock is judged, and the feeding amount of the annual livestock is three jin of pasture; if the hot spot is smaller than the second pixel point threshold value, the hot spot is not considered to be livestock; the first pixel threshold is greater than the second pixel threshold.
Step 104: determining the pasture availability of the to-be-detected grassland based on the type of the to-be-detected grassland; wherein, different types of the grasslands to be detected correspond to different forage grass availability rates respectively.
In this embodiment, the pasture availability is determined according to the types of different to-be-detected grasslands, if the pasture tolerance corresponding to the type to which the to-be-detected grassland belongs is strong, the pasture availability is high, and if the pasture tolerance corresponding to the type to which the to-be-detected grassland belongs, the pasture availability is low.
In one specific embodiment, the following examples are provided for illustration of grass availability: the specific utilization rate of Ningxia grasslands in the normal grazing period of the growing season is as follows: the dry desert grassland is 40% -45%; the desert grassland and the grassland desertification are 50% -55%; the dry grasslands and the bush grasslands are 65% -70%; the meadow grassland and the bush meadow are 70% -75%; the utilization rate may be 10% higher than the above-mentioned regulation in the period of non-growing season. The management condition is good, and the artificial grassland which can fertilize, irrigate and cut off weeds after grazing can be 85-95%.
Step 105: calculating a production efficiency ratio of the plant production efficiency to a preset plant production efficiency threshold, and a grazing intensity difference value of the grazing intensity and the preset grazing intensity threshold, and determining a vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value.
In a specific embodiment, the predetermined plant productivity threshold is 200 g/(m) 2 * a) As a critical value NPP min (i.e. when grassland NPP is at 200g +.(m 2 * a) In the following, the degradation risk of the grassland is further increased, so that the available forage grass quantity starts to decay, the self-adaption adjustment can be carried out by the user demand, and the production efficiency ratio is obtained by calculating the ratio of the plant production efficiency to the preset plant production efficiency threshold;
The preset grazing intensity threshold value is 60%, and if the calculated grazing intensity is greater than 60%, the difference value between the grazing intensity and the grazing intensity threshold value is calculated to obtain a grazing intensity difference value;
adding the production efficiency ratio and the grazing intensity difference to obtain the vegetation attenuation rate of the grassland to be detected, for example: when the NPP is equal to or greater than the NPP critical value, and the grazing intensity obtained by calculation is greater than 60%, the grassland is degraded, and if the grazing intensity is 70%, the vegetation attenuation rate is 10% compared with 60% which is increased by 10%; when grazing intensity is less than or equal to 60%, the NPP obtained by calculation is 400 gC/(m) 2 * a) Whereas the NPP minimum is 800 gC/(m) 2 * a) When the grasslands are degraded, the production efficiency ratio is calculated to be 50%, and the vegetation attenuation rate is calculated to be 50%; when the grazing intensity obtained by calculation is more than 60%, the grassland is degraded, if the grazing intensity is 70%, the grazing intensity is increased by 10% compared with 60%, and the NPP obtained by calculation is 400 gC/(m) 2 * a) Whereas the NPP minimum is 800 gC/(m) 2 * a) When the grasslands are degraded, the production efficiency ratio is calculated to be 50%, and the vegetation attenuation rate is 60%; thereby taking into account the degradation of the grassland and the reduction of the grass yield due to the grazing-related reasons.
In a specific embodiment, the determination of the degradation degree of the grassland can be described by determining the grass yield based on theory, by taking the following examples:
the reduction rate of the grassland degraded grassy yield is heavy degradation when the reduction rate is more than 50 percent, moderate degradation when 21 to 50 percent and light degradation when less than 20 percent;
NPP data from remote sensing monitoring in 1981-2015 showed that the case zone warm season NPP maximum was about 400 gC/(m) 2 * a) The corresponding theoretical grass yield is about 170 g/(m) 2 * a) About, if this is taken as the case zone, the grass yield is not degraded, and then 85-136 g/(m) is obtained 2 * a) The grass yield of (2) is moderately degraded, 136-170 g/(m) 2 * a) Is slightly degraded, assuming that if the yield of degraded grasslands is greater than 80% less than that of undegraded grasslands for three consecutive years, the grasslands cannot be restored.
Step 106: and calculating the edible pasture quantity of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass conservation rate.
In this embodiment, the edible pasture amount includes a warm season edible pasture amount and a cold season edible pasture amount, the theoretical yield includes a warm season theoretical yield and a cold season theoretical yield, the pasture availability includes a warm season pasture availability and a cold season pasture availability, the vegetation decay rate includes a warm season vegetation decay rate and a cold season vegetation decay rate, the meadow area includes a warm season meadow area and a cold season meadow area, and the meadow preservation rate includes a warm season meadow preservation rate and a cold season meadow preservation rate; the calculating the edible pasture amount of the to-be-measured grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass conservation rate comprises the following steps:
When the grassland to be measured is in a warm season time period, calculating and obtaining warm season edible grassland quantity of the grassland to be measured according to the warm season theoretical grassland yield, the warm season grassland availability, the warm season vegetation attenuation rate, the warm season grassland area and the warm season grassland preservation rate; the warm season edible pasture amount is calculated according to the following formula, and the method is as follows:
F lw (t)＝B (t1) ·A w(t1) ·η 1 ·a (t1) ·γ 1
wherein F is lw B, the warm season edible pasture quantity of the grassland to be detected is (t1) For the theoretical grass yield per unit area in the t1 time period, A w(t1) Is the area of the grassland in warm season, eta 1 A is the availability of pasture in warm season (t1) Is the vegetation attenuation rate in warm seasons, gamma 1 The preservation rate of the warm season grass is 1.
In this embodiment, the calculating the edible pasture amount of the to-be-measured grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation, the grazing intensity, the grassland area, and the grass conservation rate further includes:
when the grassland to be detected is in a cold season time period, calculating and obtaining the cold season edible grassland quantity of the grassland to be detected according to the cold season theoretical grassland yield, the cold season grassland availability, the cold season vegetation attenuation rate, the grazing intensity, the cold season grassland area and the cold season grassland preservation rate; the calculation of the cold season edible pasture amount is carried out according to the following formula, and the method is as follows:
Figure BDA0004138632770000151
Wherein G is w (t 1) is the grass feed intake of the grasslands in the period of t1 of the warm season, GI (t 1) is the grazing intensity of the grasslands to be detected in the period of t1 of the warm season, F lc (t 2) is the cold season edible pasture quantity of the grassland to be detected, B (t2) For the theoretical grass yield per unit area in the t2 period, A c(t2) Is the area of the grassland in cold seasons, eta 2 A is the availability of forage grass in cold season (t2) Is the vegetation attenuation rate in cold seasons, gamma 2 Is the preservation rate of the cold season grass.
In a specific embodiment, a month average temperature greater than 10 ℃ is a warm season period and a month average temperature less than 10 ℃ is a cold season period.
Referring to fig. 3, correspondingly, an embodiment of the present invention further provides a livestock breeding monitoring system, including: the artificial intelligence-based grassland ecological monitoring method is applied to a data processor 301, a weather detection device 302, a livestock quantity counting device 303, a livestock breeding decision module 304 and a basic database 305; the input end of the data processor 301 is respectively connected with the weather detection device 302 and the livestock quantity counting device 303; the output end of the data processor 301 is connected with the livestock breeding decision module 304, and the basic database 305 is respectively connected with the data processor 301, the weather detection device 302, the livestock quantity counting device 303 and the livestock breeding decision module 304; the base database 305 includes stock farming base data and grassland weather base data;
The weather detection device 302 is used for detecting and obtaining weather data of a grassland to be detected;
the livestock quantity counting device 303 is used for counting the livestock quantity of the grassland to be tested;
the data processor 301 is used for calculating the edible pasture amount according to the meteorological data and the livestock data;
the livestock breeding decision module 304 is used for generating a breeding decision according to the edible pasture amount, the theoretical pasture amount and the breeding cost of herders; wherein the cultivation decision comprises: increasing livestock cultivation strategies, reducing livestock cultivation strategies and replacing cultivation grassland strategies.
In a specific embodiment, the method for calculating the number of the livestock by the livestock number calculating device may be:
Figure BDA0004138632770000161
wherein N is 1 -N 7 The female animals are male lamb, female lamb, backup female sheep, 3 year old female sheep, 4 year old female sheep, 5 year old female sheep and 6 year old female sheep, wherein female animals 3 to 6 years old constitute breeding female animals;
lamb (N) w1 ) And lamb (N) w2 ) The number:
N w1 (t)＝0.5·N ce3 (t-1)·α
N w2 (t)=0.5·N ce3 (t-1)·α
wherein N is ce3 The number of female animals bred in the cold season of the last year is the breeding survival rate alpha;
the other livestock quantity is the last year cold season quantity (N cf ) Livestock (N) with death in cold season was abated dc )：
N w3 (t)＝N cf (t-1)-N dc (t-1)(i＞2）
The number of cold-season dead livestock was calculated from the mortality rate (β):
N cd (t）＝N cf （t)·β
in a specific embodiment, the livestock breeding decision module makes a decision by comparing the theoretical grass yield, the edible grass yield, and the vegetation decay rate: if the vegetation attenuation rate is greater than 80% for three consecutive years, generating a cultivation grassland replacement strategy; if the vegetation attenuation rate is not greater than 80% for three consecutive years, and when the theoretical grass yield is equal to the edible pasture, the cultivation strategy is not replaced; if the vegetation attenuation rate is not greater than 80% for three consecutive years, and when the theoretical grass yield is greater than the edible grass yield, generating a livestock raising strategy; if the vegetation decay rate is not greater than 80% for three consecutive years, and if the theoretical grass yield is less than the edible grass yield is equal, then a livestock cultivation strategy is generated.
In a specific embodiment, the livestock cultivation decision module performs cultivation decision generation according to the edible pasture amount, the theoretical pasture amount and the cultivation cost of the herder, specifically: adaptive actions of the herd body such as selling livestock, buying forage and the like, and adaptive strategies and policies adopted by the community body and the government body when dealing with climate change and grassland degradation.
Calculating the breeding income of the herd through the breeding cost based on the herd:
it is assumed that all revenues for the herd's home come from the sales of livestock. Thus, the total income (Inc) of the livestock is determined by the total income (Q) i ) And corresponding price (p i ) And (3) determining:
Figure BDA0004138632770000171
wherein p is i (t) is the livestock price for category i of year t;
costs for livestock include fixed costs and variable costs. Since the key factor affecting the behavior of the herding user is the current cash flow, the present embodiment assumes a fixed cost to be the cost of the payroll, and does not consider the fixed cost. The variable cost (C) includes a feed fee (C f ) Energy cost (C) en ) Employee payroll (C) labor ) And epidemic prevention fee(C v )。
The net income of livestock is the total income of livestock minus the variable cost:
NinC＝Inc (t) -C (t)
at the end of the warm season, the vital decision-making activities of the grazing house subject include selling livestock, purchasing feed, etc., and occur simultaneously, and decisions and activities may be adjusted due to the occurrence of drought and the occurrence of grassland degradation. It is assumed here that the basic idea of the out-of-stock decision of the grazing house main body is to maximize the stock quantity of the livestock on the premise that the forage supply and demand balance and the balance are satisfied if the grassland is not degraded or is slightly degraded. In the face of different conditions (frequency and intensity) of drought, different adaptation strategies are adopted by a grazing family main body as different experimental scenes to evaluate the influence of the different adaptation strategies on a land system, socioeconomic performance and ecology, and generally, decision processes are carried out on the premise of balance and feed supply and demand.
First, the expected cash receipts (cash demand, labeled Tinc) to sell livestock are equal to the living expenses (C 1 ) Feed purchase expense (C) f1 ) Variable costs other than feed purchasing (C o ) Minus the income of livestock sold in summer (S (t-1)):
Tinc (t) ＝C l (t)+C f1 (t)+C o (t)-Inc trans (t)-S(t-1)
when in sales, the herd first goes out all the old, weak, sick and disabled (N ill ) And lamb (N) w1 )：
Q1＝N ill (t)+N w1 (t)＝(N w3 (t)+N w2 (t))·ε+N w1 (t)
Where ε is the disability rate of the animal.
Thereafter, if the income is lower than expected (lnC) 1 <Tinc, unbalanced balance) or stock animal pasture demand exceeds the actual availability ((N) w -Q 1 )*I*Dc>Flc, unbalanced grass stock), then further marketing or buying is required;
according to balance of balance and forage supplyThe balance constraint is needed to calculate the number of other animals (Q 2 )：
Figure BDA0004138632770000191
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Wherein P' (t) is the price of buying grass in autumn, and P (t) is the price of feeding livestock in autumn;
savings in year t equals all revenues minus all expenses:
S 0 (t)=Tinc(t)-C 1 (t）-C (t)
according to the method, the plant production efficiency is obtained through calculation by acquiring real-time air temperature data and real-time precipitation data of the grassland to be detected, the theoretical grass yield is calculated based on the plant production efficiency, the grass availability and grazing intensity of the grassland to be detected are determined through acquiring the quantity of livestock and feeding data of the grassland to be detected, the production efficiency wallpaper and grazing intensity interpolation is obtained through calculation based on a plant production efficiency threshold value and a grazing intensity threshold value, and the vegetation attenuation rate is obtained; and calculating the pasture amount based on the theoretical pasture yield, the pasture availability, the vegetation attenuation rate, the pasture intensity, the pasture area and the pasture preservation rate to obtain the edible pasture amount of the pasture to be detected, thereby completing the monitoring of the edible pasture amount of the pasture. According to the method, the operation state of the grassland ecological system is simulated more accurately and the prediction is carried out more scientifically on the basis of grassland animal husbandry and based on calculation of ecological rules.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a grassland ecological monitoring system based on artificial intelligence according to an embodiment of the invention, including: a plant production efficiency calculation 201, a theoretical grass yield calculation module 202, a grazing intensity calculation module 203, a grass availability calculation module 204, a vegetation attenuation calculation module 205 and an edible grass yield calculation module 206;
the plant production efficiency calculation 201 is configured to obtain meteorological data of a to-be-detected grassland, and calculate to obtain plant production efficiency of the to-be-detected grassland based on the meteorological data;
the theoretical grass yield calculation module 202 is configured to calculate and obtain the theoretical grass yield of the grassland to be tested according to the plant production efficiency;
the grazing intensity calculation module 203 is configured to obtain the number of animals in the grassland to be detected and feeding data of the animals, and determine the grazing intensity of the grassland to be detected based on the theoretical grass yield, the number of animals and the feeding data;
the pasture availability calculation module 204 is configured to determine the pasture availability of the to-be-detected grassland based on the type of the to-be-detected grassland; wherein different types of grasslands to be detected correspond to different forage grass availability rates respectively;
The vegetation attenuation rate calculation module 205 is configured to calculate a production efficiency ratio of the plant production efficiency to a preset plant production efficiency threshold, and a grazing intensity difference value between the grazing intensity and the preset grazing intensity threshold, and determine a vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value;
the edible pasture amount calculating module 206 is configured to calculate an edible pasture amount of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area, and the grass conservation rate.
As an improvement of the above, the weather data includes: temperature data and precipitation data; the calculating the plant production efficiency of the grassland to be measured based on the meteorological data comprises the following steps:
calculating plant production efficiency of the grassland to be detected based on the air temperature data and the precipitation data; the plant production efficiency is calculated according to the following calculation formula, and the calculation method specifically comprises the following steps:
NPP(t)＝r 1 ·P(t)+r 2 ·T(t)+j
wherein r is 1 And r 2 Fitting coefficients obtained based on historical precipitation data and historical air temperature data are calculated, P (T) is precipitation data of a to-be-measured grassland in a T period, T (T) is air temperature data of the to-be-measured grassland in the T period, j is a constant term, NPP (t) is the plant production efficiency of the grassland to be tested in the t time.
As an improvement of the above solution, the theoretical grass yield calculation module 202 includes:
calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency; the theoretical grass yield is calculated according to the following formula, and the theoretical grass yield is calculated specifically as follows:
Figure BDA0004138632770000211
wherein B (t) is the theoretical grass yield of the grassland to be detected in unit area within t time, and the unit is g/(m-2*a-1); NPP (t) is the plant production efficiency of the grassland to be detected within t time, and the unit gC/(m-2*a-1); s is S bn Converting grassland biomass of a grassland to be detected into a conversion coefficient of NPP; s is S ug Is the ratio coefficient of biomass of underground and overground parts of the grassland to be measured.
As an improvement to the above, the feeding data includes: daily feed and days of eating for single livestock; the determining of the grazing intensity of the grassland to be tested based on the theoretical grass yield, the livestock quantity and the feeding data comprises:
according to the theoretical grass yield, the livestock quantity and the feeding data, calculating and obtaining grazing intensity of the grassland to be detected; the grazing intensity is calculated according to the following formula, and the grazing intensity is calculated specifically as follows:
Figure BDA0004138632770000212
wherein G is w (t) is the feed intake of livestock in t time, GI (t) is grazing intensity of grassland to be tested in t time, N w (t) is the number of animals in t time, I is the daily ration of a single animal, D w Is days of eating, B w(t) The theoretical grass yield of the grassland to be measured in the t time is obtained.
As an improvement of the above-described aspect, the edible pasture amount includes a warm season edible pasture amount and a cold season edible pasture amount, the theoretical yield includes a warm season theoretical yield and a cold season theoretical yield, the pasture availability includes a warm season pasture availability and a cold season pasture availability, the vegetation decay rate includes a warm season vegetation decay rate and a cold season vegetation decay rate, the meadow area includes a warm season meadow area and a cold season meadow area, and the meadow preservation rate includes a warm season meadow preservation rate and a cold season meadow preservation rate; the edible pasture amount calculation module 206 includes: a warm season edible pasture amount calculating unit;
the warm season edible pasture amount calculating unit is used for calculating and obtaining the warm season edible pasture amount of the to-be-measured grassland according to the warm season theoretical grass yield, the warm season pasture availability, the warm season vegetation attenuation rate, the warm season grass field area and the warm season grass preservation rate when the to-be-measured grassland is in a warm season time period; the warm season edible pasture amount is calculated according to the following formula, and the method is as follows:
F lw (t)＝B (t1) ·A w(t1) ·η 1 ·a (t1) ·γ 1
Wherein F is lw B, the warm season edible pasture quantity of the grassland to be detected is (t1) For the theoretical grass yield per unit area in the t1 time period, A w(t1) Is the area of the grassland in warm season, eta 1 A is the availability of pasture in warm season (t1) Is the vegetation attenuation rate in warm seasons, gamma 1 The preservation rate of the warm season grass is 1.
As an improvement to the above, the edible pasture amount calculating module 206 further includes: a cold season edible pasture amount calculating unit;
the cold season edible pasture amount calculating unit is used for calculating and obtaining the cold season edible pasture amount of the to-be-detected grassland according to the cold season theoretical pasture amount, the cold season pasture availability, the cold season vegetation attenuation rate, the grazing intensity, the cold season grassland area and the cold season pasture preservation rate when the to-be-detected grassland is in the cold season time period; the calculation of the cold season edible pasture amount is carried out according to the following formula, and the method is as follows:
Figure BDA0004138632770000221
wherein G is w (t 1) is the grass feed intake of the grasslands in the period of t1 of the warm season, GI (t 1) is the grazing intensity of the grasslands to be detected in the period of t1 of the warm season, F lc (t 2) is the cold season edible pasture quantity of the grassland to be detected, B (t2) For the theoretical grass yield per unit area in the t2 period, A c(t2) Is the area of the grassland in cold seasons, eta 2 A is the availability of forage grass in cold season (t2) Is the vegetation attenuation rate in cold seasons, gamma 2 Is the preservation rate of the cold season grass.
In the embodiment, meteorological data of a grassland to be detected is obtained through a plant production efficiency calculation module, plant production efficiency of the grassland to be detected is calculated, and theoretical grass yield of the grassland to be detected is calculated based on the plant production efficiency through a theoretical grass yield calculation module; the method comprises the steps of obtaining the quantity of livestock and feeding data of the grassland to be detected through a grazing intensity calculation module, so as to determine the grazing intensity of the grassland to be detected; determining the pasture availability based on the type of the grassland to be detected by a pasture availability calculation module; determining the vegetation attenuation rate of the grassland to be detected based on a preset plant production efficiency threshold value and a grazing intensity threshold value through a vegetation attenuation rate calculation module; and finally, calculating the edible pasture quantity of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass preservation rate through an edible pasture quantity calculation module. The monitoring of the edible pasture quantity of the grasslands is completed, the pasture quantity monitoring method is favorable for pastures to judge whether the pasture quantity exceeds the pasture feeding quantity of the livestock cultivated by the grasslands based on the monitoring result of the edible pasture quantity of the grasslands, and further the pasture intensity is controlled, so that the damage of degradation of the grasslands caused by overlarge pasture intensity is avoided, and sustainable development of ecology of the grasslands is facilitated.
Example III
Referring to fig. 4, fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
A terminal device of this embodiment includes: a processor 401, a memory 402 and a computer program stored in the memory 402 and executable on the processor 401. The processor 401, when executing the computer program, implements the steps of the various artificial intelligence based grassland ecological monitoring methods described above in embodiments, such as all the steps of the artificial intelligence based grassland ecological monitoring method shown in fig. 1. Alternatively, the processor may implement functions of each module in the above-described device embodiments when executing the computer program, for example: all modules of the artificial intelligence based grassland ecological monitoring device shown in fig. 2.
In addition, the embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the artificial intelligence-based grassland ecological monitoring method according to any embodiment.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 401 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 401 is a control center of the terminal device, and connects various parts of the entire terminal device using various interfaces and lines.
The memory 402 may be used to store the computer program and/or module, and the processor 401 may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory 402. The memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims (10)

1. The grassland ecology monitoring method based on artificial intelligence is characterized by comprising the following steps of:
Acquiring meteorological data of a grassland to be detected, and calculating plant production efficiency of the grassland to be detected based on the meteorological data;
calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency;
acquiring the quantity of livestock of the grassland to be tested and feeding data of the livestock, and determining grazing intensity of the grassland to be tested based on the theoretical grass yield, the quantity of the livestock and the feeding data;
determining the pasture availability of the to-be-detected grassland based on the type of the to-be-detected grassland; wherein different types of grasslands to be detected correspond to different forage grass availability rates respectively;
calculating a production efficiency ratio of the plant production efficiency to a preset plant production efficiency threshold value, and a grazing intensity difference value of the grazing intensity to the preset grazing intensity threshold value, and determining a vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value;
and calculating the edible pasture quantity of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass conservation rate.
2. The artificial intelligence based grassland ecology monitoring method of claim 1, wherein the meteorological data comprises: temperature data and precipitation data; the calculating the plant production efficiency of the grassland to be measured based on the meteorological data comprises the following steps:
Calculating plant production efficiency of the grassland to be detected based on the air temperature data and the precipitation data; the plant production efficiency is calculated according to the following calculation formula, and the calculation method specifically comprises the following steps:
NPP(t)＝r 1 ·P(t)+r 2 ·T(t)+j
wherein r is 1 And r 2 The fitting coefficients obtained based on historical precipitation data and historical air temperature data are calculated, P (T) is precipitation data of a to-be-measured grassland in a T time period, T (T) is air temperature data of the to-be-measured grassland in the T time period, j is a constant term, and NPP (T) is plant production efficiency of the to-be-measured grassland in the T time period.
3. The artificial intelligence-based grassland ecological monitoring method according to claim 1, wherein the calculating the theoretical grass yield of the grassland to be measured according to the plant production efficiency comprises:
calculating the theoretical grass yield of the grassland to be detected according to the plant production efficiency; the theoretical grass yield is calculated according to the following formula, and the theoretical grass yield is calculated specifically as follows:
Figure FDA0004138632710000021
wherein B (t) is the theoretical grass yield of the grassland to be detected in unit area within t time, and the unit is g/(m-2*a-1); NPP (t) is the plant production efficiency of the grassland to be detected within t time, and the unit gC/(m-2*a-1); s is S bn Converting grassland biomass of a grassland to be detected into a conversion coefficient of NPP; s is S ug Is the ratio coefficient of biomass of underground and overground parts of the grassland to be measured.
4. The artificial intelligence based grassland ecology monitoring method of claim 1, wherein the feeding data comprises: daily feed and days of eating for single livestock; the determining of the grazing intensity of the grassland to be tested based on the theoretical grass yield, the livestock quantity and the feeding data comprises:
according to the theoretical grass yield, the livestock quantity and the feeding data, calculating and obtaining grazing intensity of the grassland to be detected; the grazing intensity is calculated according to the following formula, and the grazing intensity is calculated specifically as follows:
Figure FDA0004138632710000022
wherein G is w (t) is the feed intake of livestock in t time, GI (t) is grazing intensity of grassland to be tested in t time, N w (t) is the number of animals in t time, I is the daily ration of a single animal, D w Is days of eating, B w(t) The theoretical grass yield of the grassland to be measured in the t time is obtained.
5. The artificial intelligence-based grassland ecological monitoring method of claim 1, wherein the edible grass amounts comprise warm season edible grass amounts and cold season edible grass amounts, the theoretical grass amounts comprise warm season theoretical grass amounts and cold season theoretical grass amounts, the grass availability comprises warm season grass availability and cold season grass availability, the vegetation decay rate comprises warm season vegetation decay rate and cold season vegetation decay rate, the grassland areas comprise warm season grassland areas and cold season grassland areas, and the grass preservation rates comprise warm season grass preservation rates and cold season grass preservation rates; the calculating the edible pasture amount of the to-be-measured grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass conservation rate comprises the following steps:
When the grassland to be measured is in a warm season time period, calculating and obtaining warm season edible grassland quantity of the grassland to be measured according to the warm season theoretical grassland yield, the warm season grassland availability, the warm season vegetation attenuation rate, the warm season grassland area and the warm season grassland preservation rate; the warm season edible pasture amount is calculated according to the following formula, and the method is as follows:
F lw (t)＝B （t1) ·A w(t1） ·η 1 ·a (t1) ·γ 1
wherein F is lw B, the warm season edible pasture quantity of the grassland to be detected is (t1) For the theoretical grass yield per unit area in the t1 time period, A w(t1) Is the area of the grassland in warm season, eta 1 A is the availability of pasture in warm season (t1) Is the vegetation attenuation rate in warm seasons, gamma 1 The preservation rate of the warm season grass is 1.
6. The artificial intelligence based grassland ecological monitoring method of claim 5, wherein the calculating the edible grassland amount of the grassland to be measured based on the theoretical grass yield, the grass availability, the vegetation decay rate, the grazing intensity, the grassland area, and the grass conservation rate further comprises:
when the grassland to be detected is in a cold season time period, calculating and obtaining the cold season edible grassland quantity of the grassland to be detected according to the cold season theoretical grassland yield, the cold season grassland availability, the cold season vegetation attenuation rate, the grazing intensity, the cold season grassland area and the cold season grassland preservation rate; the calculation of the cold season edible pasture amount is carried out according to the following formula, and the method is as follows:
Figure FDA0004138632710000041
Wherein G is w (t 1) is the grass feed intake of the grasslands in the period of t1 of the warm season, GI (t 1) is the grazing intensity of the grasslands to be detected in the period of t1 of the warm season, F lc (t 2) is the cold season edible pasture quantity of the grassland to be detected, B (t2) For the theoretical grass yield per unit area in the t2 period, A c(t2) Is the area of the grassland in cold seasons, eta 2 Can be used for forage grass in cold seasonRate, a (t2) Is the vegetation attenuation rate in cold seasons, gamma 2 Is the preservation rate of the cold season grass.
7. An artificial intelligence-based grassland ecological monitoring system, comprising: the system comprises a plant production efficiency calculation module, a theoretical grass yield calculation module, a grazing intensity calculation module, a grass availability calculation module, a vegetation attenuation rate calculation module and an edible grass amount calculation module;
the plant production efficiency calculation module is used for acquiring meteorological data of a grassland to be detected and calculating plant production efficiency of the grassland to be detected based on the meteorological data;
the theoretical grass yield calculation module is used for calculating and obtaining the theoretical grass yield of the grassland to be detected according to the plant production efficiency;
the grazing intensity calculation module is used for acquiring the quantity of livestock of the grassland to be detected and feeding data of the livestock, and determining the grazing intensity of the grassland to be detected based on the theoretical grass yield, the quantity of the livestock and the feeding data;
The pasture availability calculation module is used for determining the pasture availability of the to-be-detected grassland based on the type of the to-be-detected grassland; wherein different types of grasslands to be detected correspond to different forage grass availability rates respectively;
the vegetation attenuation rate calculation module is used for calculating a production efficiency ratio of the plant production efficiency to a preset plant production efficiency threshold value and a grazing intensity difference value of the grazing intensity and the preset grazing intensity threshold value, and determining the vegetation attenuation rate of the grassland to be detected based on the production efficiency ratio and the grazing intensity difference value;
the edible pasture amount calculating module is used for calculating the edible pasture amount of the to-be-detected grassland based on the theoretical grass yield, the pasture availability, the vegetation attenuation rate, the grazing intensity, the grassland area and the grass preservation rate.
8. An artificial intelligence-based grassland ecological monitoring system, comprising: a data processor, a weather detection device, a livestock quantity statistics device, a livestock breeding decision module and a basic database which are applied to the artificial intelligence-based grassland ecological monitoring method according to claims 1 to 6; the input end of the data processor is respectively connected with the weather detection device and the livestock quantity counting device; the output end of the data processor is connected with the livestock breeding decision module, and the basic database is respectively connected with the data processor, the weather detection device, the livestock quantity counting device and the livestock breeding decision module; the basic database comprises animal husbandry basic data and grassland meteorological basic data;
The weather detection device is used for detecting and obtaining weather data of a grassland to be detected;
the livestock quantity counting device is used for counting the livestock quantity of the grassland to be tested;
the data processor is used for calculating the edible pasture amount according to the meteorological data and the livestock data;
the livestock cultivation decision-making module is used for making cultivation decision according to the edible pasture quantity, the theoretical pasture yield and the cultivation cost of herders; wherein the cultivation decision comprises: increasing livestock cultivation strategies, reducing livestock cultivation strategies and replacing cultivation grassland strategies.
9. A computer terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing an artificial intelligence based grassland ecological monitoring method according to any one of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform an artificial intelligence based grassland ecological monitoring method according to any one of claims 1 to 6.
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