A smart agricultural monitoring and management system based on big data and the Internet of Things

Abstract

The present invention relates to the field of data processing technology, and in particular to a smart agricultural monitoring and management system based on big data and the Internet of Things. The analysis module of the system can determine the period of the year to be tested in the current period of the year; the first determination module can comprehensively consider the changes in water temperature and air temperature, and determine the possibility of interference every day in the reference period and the corresponding high temperature duration period every day; the second determination module can determine the high temperature index of the current day of the year to be tested, thereby reflecting the possibility of the phenomenon of excessive aquaculture water temperature on the current day of the year to be tested; the determination module can respond to the high temperature index of the current day of the year to be tested being greater than or equal to the high temperature index threshold, and determine that the temperature of the aquaculture water body on the current day of the year to be tested is abnormal. The system can reduce the impact of periodic changes in temperature, promptly discover abnormalities in water temperature, ensure the timeliness of manual intervention, improve the accuracy of water temperature regulation for hairy crab aquaculture, and ensure that the temperature of the aquaculture water body meets the growth conditions of hairy crabs.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a smart agricultural monitoring and management system based on big data and the Internet of Things. Background Art

[0002] In the process of hairy crab farming, water temperature monitoring is very important, as water temperature directly affects the growth rate, appetite, metabolism and immunity of hairy crabs. Therefore, real-time monitoring of water temperature to ensure that it is within an appropriate range is particularly important for hairy crab farming.

[0003] At present, water temperature data can be collected through temperature sensors and the water temperature can be monitored in real time, helping farmers to adjust the environment in a timely manner according to climate changes and water conditions, avoiding growth stagnation, disease or death of hairy crabs due to abnormal water temperature, and ultimately improving breeding efficiency and profits.

[0004] However, when monitoring the water temperature of hairy crab farming, the above method only uses a fixed water temperature threshold to judge the water temperature collected by the temperature sensor. Since temperature changes are not just short-term fluctuations, the water temperature is periodic under the influence of factors such as seasons, day and night, and climate. The fixed temperature threshold alone cannot make the judgment result meet the periodic requirements, which may affect the global judgment of the hairy crab growth environment. Summary of the invention

[0005] In order to solve the technical problem of inaccurate water temperature control for hairy crab farming caused by periodic temperature changes, the purpose of the present invention is to provide a smart agricultural monitoring and management system based on big data and the Internet of Things. The technical solutions adopted are as follows:

[0006] The present invention provides a smart agricultural monitoring and management system based on big data and the Internet of Things, the system comprising:

[0007] An acquisition module is used to acquire a middle temperature sequence, a bottom temperature sequence and an air temperature sequence of a culture water body, wherein the middle temperature sequence includes the middle temperature sequence of the year to be measured and the middle temperature sequence of previous years, the bottom temperature sequence includes the bottom temperature sequence of the year to be measured and the bottom temperature sequence of previous years, and the air temperature sequence includes the air temperature sequence of the year to be measured and the air temperature sequence of previous years, wherein the elements of the middle temperature sequence are an array composed of all middle temperature data of corresponding days, the elements of the bottom temperature sequence are an array composed of all bottom temperature data of corresponding days, and the elements of the air temperature sequence are an array composed of all air temperature data of corresponding days;

[0008] An analysis module is used to analyze the similarity between the bottom temperature sequence of the current period of the year to be measured and the bottom temperature sequence of any year in the past years, and determine a reference period corresponding to the current period of the year to be measured, wherein the reference period is a period of any year in the past years, and is used to reflect the period of the current period of the year to be measured in the year;

[0009] The first determination module is used to determine the possibility of interference every day in the reference period according to the difference in the change trend of the central temperature sequence corresponding to the reference period and the air temperature sequence, and to determine the high temperature duration period corresponding to each day in the reference period according to the high temperature continuity of the central temperature sequence corresponding to the reference period before the artificial intervention;

[0010] The second determination module is used to determine the high temperature index of the current day of the year to be tested;

[0011] The determination module is used to determine that the temperature of the aquaculture water body on the current day of the year to be measured is abnormal in response to the high temperature index on the current day of the year to be measured being greater than or equal to the high temperature index threshold.

[0012] In some embodiments, the analysis module comprises:

[0013] The first construction unit is used to construct a reference window of threshold days based on the bottom temperature sequence of any year in the past years, and the initial reference window satisfies: an array consisting of all bottom temperature data corresponding to the first day of any year in the past years is the last element in the first reference window, and the reference window is used to slide with a step length of one day, and the multiple reference windows obtained by sliding have a corresponding relationship with the bottom temperature sequence of any year in the past years;

[0014] The second construction unit is used to construct a fixed window of threshold days for the bottom temperature sequence of the year to be measured, and the fixed window satisfies: the last element of the bottom temperature sequence of the year to be measured is an array composed of all bottom temperature data of a certain day closest to the current time;

[0015] The first determination unit is used to traverse the bottom temperature sequence of any year in the past years through a reference window, and determine the period of the year in which the bottom temperature sequence corresponding to the fixed window is located based on the similarity between the minimum temperature corresponding to the reference window and the minimum temperature corresponding to the fixed window, so as to obtain a reference period corresponding to the current period of the year to be measured.

[0016] In some embodiments, the reference degree of the bottom temperature sequence of any year in the calendar year corresponding to the reference window is determined according to the following formula:

[0017] ;

[0018] In the formula, Indicates The reference degree of the bottom temperature series of any year in the historical years corresponding to the reference window, represents the number of days of the bottom temperature series in any year in the calendar year corresponding to the reference window, Indicates The bottom temperature series of any year in the reference window corresponding to the The lowest temperature of the day, Indicates the bottom temperature sequence of the year to be measured corresponding to the fixed window. The lowest temperature of the day, Indicates the number of reference windows. Represents the day number in the temperature series, represents the negative correlation normalization function;

[0019] The period of the reference window corresponding to the maximum reference degree is taken as the reference period corresponding to the current period of the year to be tested.

[0020] In some embodiments, the first determining module includes:

[0021] A first calculation unit is used to calculate the air temperature rate and the water temperature rate of the middle temperature sequence, wherein the water temperature rate is the normalized value of the difference between the middle temperature data corresponding to adjacent elements in the middle temperature sequence of any year in the past years, and the air temperature rate is the normalized value of the difference between the air temperature data corresponding to adjacent elements in the air temperature sequence of any year in the past years;

[0022] A traversal unit is used to traverse the middle temperature series of any year in the calendar year through a reference window;

[0023] The second determining unit is used to determine the degree of change difference based on the air temperature rate and the water temperature rate according to the following formula:

[0024] ;

[0025] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The water temperature rate of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The temperature rate of the temperature data, Indicates the number of reference windows. Represents the day number in the temperature series, Indicates the number of temperature data on a certain day in the temperature sequence corresponding to the reference window;

[0026] A third determining unit, configured to determine a possible pre-interference index and a possible post-interference index based on the degree of change difference;

[0027] a fourth determination unit, configured to determine the possibility of intervention for each day in a reference period based on the pre-intervention index and the possible post-intervention index;

[0028] A fitting unit is used to fit the temperature data of each day in the central temperature sequence of any year in the past years to obtain a fitting curve of the central temperature of each day in any year in the past years. The fitting curve of the central temperature of each day in any year in the past years is used to determine the central temperature value at a certain moment;

[0029] The second calculation unit is used to take any day as a target day and calculate the high temperature similarity between each day and the target day in sequence starting from the target day;

[0030] The high temperature duration unit is used to take the duration of the middle temperature value corresponding to the high temperature similarity greater than or equal to the similarity threshold as the high temperature duration corresponding to the target day.

[0031] In some embodiments, the possible pre-interference index is determined based on the degree of difference in the change according to the following formula:

[0032] ;

[0033] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible pre-interference index of the day, Indicates the number of daily temperature data in the central temperature series of any year in the reference window. Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the The temperature data from the first to the The maximum value of the difference in the degree of change of temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, represents the normalized exponential function, represents the negative correlation normalization function;

[0034] According to the following formula, the possible post-intervention index is determined based on the degree of difference in the changes:

[0035] ;

[0036] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible post-intervention index of the day, Represents the normalization function.

[0037] In some embodiments, the possibility of intervention for each day in the reference period is determined based on the pre-intervention index and the possible post-intervention index according to the following formula:

[0038] ;

[0039] In the formula, For the The middle temperature series of any year in the reference window corresponding to the The possibility of heaven's intervention.

[0040] In some embodiments, according to the following formula, any day is taken as the target day, and the high temperature similarity of each day with the target day is calculated in sequence starting from the target day:

[0041] ;

[0042] In the formula, Indicates the target day in any year and the number of days counting forward from the target day. The similarity of high temperatures of the day, Indicates the number of sampling times per day, Indicates the day in any year in the calendar counting forward from the target day. Day's The middle temperature value on the middle fitting curve corresponding to the sampling moment, It represents the maximum value of the middle temperature on the middle fitting curve of the target day in any year in the calendar year. Indicates the day in any year in the calendar counting forward from the target day. Day's The duration of the middle temperature value corresponding to the sampling moment, Indicates the first day of the target day in any calendar year. The duration of the middle temperature value corresponding to the sampling moment.

[0043] In some embodiments, the second determining module includes:

[0044] An abnormality possibility unit, used to use the interference possibility of each day in the reference period of the year to be measured as the abnormality possibility of each day in the reference period of the year to be measured;

[0045] The third calculation unit is used to calculate the high temperature similarity between each day in the high temperature continuous period corresponding to the current day and each day in the high temperature continuous period corresponding to each day before the current day within the reference period of the year to be measured;

[0046] A similarity index unit is used to take the maximum value of the high temperature similarity between the high temperature duration period corresponding to the current day and the high temperature duration period corresponding to each day before the current day within the reference period of the year to be tested as the similarity index between the current day and each day of the year to be tested;

[0047] The fifth determination unit is used to determine the high temperature index of the current day of the year to be measured according to the abnormal possibility of each day in the reference period of the year to be measured and the similarity index between the current day of the year to be measured and each day.

[0048] In some embodiments, the high temperature level of the reference window corresponding to the current day of the year to be measured is determined according to the following formula:

[0049] ;

[0050] In the formula, Indicates the high temperature level corresponding to the a-th reference window on the current day, represents the number of days of the reference period corresponding to the a-th reference window, Indicates the ath reference window corresponding to the reference period The abnormal possibility of the day, Indicates the current day and the ath reference window. Similarity index of the day;

[0051] The average of the high temperature levels of all reference windows corresponding to the current day of the year to be measured is calculated to obtain the high temperature index of the current day of the year to be measured.

[0052] In some embodiments, the system further comprises:

[0053] The monitoring module is used to continue monitoring the temperature of the aquaculture water body in response to the high temperature index of the current day of the year to be tested being less than the high temperature index threshold.

[0054] The present invention has the following beneficial effects:

[0055] The present invention provides a smart agricultural monitoring and management system based on big data and the Internet of Things, which includes an acquisition module, an analysis module, a first determination module, a second determination module, and a judgment module. The acquisition module can acquire the middle temperature sequence, the bottom temperature sequence, and the air temperature sequence of the aquaculture water body; the analysis module can determine the period of the current period of the year to be measured in a year, so as to facilitate subsequent analysis by referring to the temperature sequences of previous years in the same period as the current period of the year to be measured; the first determination module can determine the possibility of interference every day in the reference period according to the difference in the change trend of the middle temperature sequence and the air temperature sequence corresponding to the reference period, and determine the high temperature duration period corresponding to each day in the reference period according to the high temperature continuity of the middle temperature sequence corresponding to the reference period before manual intervention, comprehensively considering the water body temperature change and the air temperature change, and can accurately grasp the high temperature duration period with abnormal water body temperature; the second determination module can determine the high temperature index of the current day of the year to be measured, so as to reflect the possible degree of the phenomenon of excessive aquaculture water temperature on the current day of the year to be measured; the judgment module can determine the abnormal temperature of the aquaculture water body on the current day of the year to be measured in response to the high temperature index of the current day of the year to be measured being greater than or equal to the high temperature index threshold. The system can reduce the impact of periodic temperature changes and detect abnormal water temperature in a timely manner, thereby ensuring the timeliness of human intervention and improving the accuracy of water temperature regulation in hairy crab farming, thereby ensuring that the temperature of the farming water is in line with the growth conditions of hairy crabs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of the structure of a smart agricultural monitoring and management system based on big data and the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0060] The following is a detailed description of a specific solution of a smart agricultural monitoring and management system based on big data and the Internet of Things provided by the present invention in conjunction with the accompanying drawings. Figure 1 The system includes an acquisition module 101, an analysis module 102, a first determination module 103, a second determination module 104 and a judgment module 105. Each module is described in detail below.

[0061] The acquisition module 101 is used to obtain the middle temperature sequence, bottom temperature sequence and air temperature sequence of the aquaculture water body, wherein the middle temperature sequence includes the middle temperature sequence of the year to be measured and the middle temperature sequence of previous years, the bottom temperature sequence includes the bottom temperature sequence of the year to be measured and the bottom temperature sequence of previous years, and the air temperature sequence includes the air temperature sequence of the year to be measured and the air temperature sequence of previous years, wherein the elements of the middle temperature sequence are an array composed of all middle temperature data of corresponding days, the elements of the bottom temperature sequence are an array composed of all bottom temperature data of corresponding days, and the elements of the air temperature sequence are an array composed of all air temperature data of corresponding days.

[0062] The middle temperature sequence, bottom temperature sequence and air temperature sequence of the aquaculture water body in the year to be measured and in previous years are obtained to facilitate the subsequent confirmation of the period of the year in which the current period of the year to be measured is located.

[0063] It should be noted that the temperature sensor is generally located in the middle and bottom of the water depth, because the water temperature is relatively stable in these areas and can represent the overall temperature of the breeding pond, so the middle temperature sequence and the bottom temperature sequence are collected. During the hairy crab breeding process, the temperature sensor in the middle of the water depth and the temperature sensor at the bottom will continuously monitor the water temperature of the water body, and the specific collection frequency is once every 5 minutes. The temperature data collected by the temperature sensor in the middle of the water depth and the temperature sensor at the bottom are obtained to obtain the middle temperature sequence and the bottom temperature sequence. The temperature of the breeding pond can reflect the temperature change of the weather. The temperature change of the weather is an important influencing factor of the temperature change of the water body, and the temperature change of the weather is almost unaffected after the artificial intervention of the water body temperature, so obtaining the temperature sequence is conducive to judging whether the temperature of the breeding water body needs to be interfered. The temperature data is obtained, and the collection frequency is the same as the collection frequency of the water body temperature, and the temperature sequence is obtained.

[0064] In the middle temperature series and bottom temperature series of previous years, different growth stages of hairy crabs require different water temperatures. In order to obtain water temperature data at the same growth stage as the current day, the bottom temperature series with a small temperature difference between day and night is used to obtain the reference window. Since the abnormal water temperature in the historical data is manifested as subsequent human intervention, the trend of water temperature data after human intervention will change. Therefore, the possibility of human intervention every day is judged, and then the high temperature duration period is obtained according to the high temperature persistence before human intervention. The higher the high temperature similarity between the current day and the high temperature duration period with a high possibility of interference, it indicates that the temperature of the current day is more likely to be abnormal and human intervention is required.

[0065] The analysis module 102 is used to analyze the similarity between the bottom temperature sequence of the current period of the year to be measured and the bottom temperature sequence of any year in the past years, and determine the reference period corresponding to the current period of the year to be measured. The reference period is the period of any year in the past years, which is used to reflect the period of the current period of the year to be measured in the year.

[0066] Determine the period of the year in which the current period of the year to be measured is located, so as to facilitate subsequent analysis with reference to the temperature series of previous years in the same period as the current period of the year to be measured.

[0067] It should be noted that in the breeding process of hairy crabs, the growth cycle of hairy crabs in a year is mainly divided into the main stages of juvenile crabs, juvenile crabs, adult crabs, mature crabs and hibernation. Different growth stages of hairy crabs require different water temperatures. In order to obtain water temperature data that is in the same growth stage as the current day, the bottom temperature series with a smaller temperature difference between day and night is used for comparison to accurately determine the period of the year in which the current period of the year to be tested is located.

[0068] In some embodiments, the analysis module 102 includes:

[0069] The first construction unit is used to construct a reference window of threshold days based on the bottom temperature sequence of any year in the past years. The initial reference window satisfies: an array consisting of all bottom temperature data corresponding to the first day of any year in the past years is the last element in the first reference window. The reference window is used to slide with a step length of one day. The multiple reference windows obtained by sliding have a corresponding relationship with the bottom temperature sequence of any year in the past years.

[0070] A reference window is constructed to traverse the temperature sequence. The array consisting of all bottom temperature data corresponding to the first day of any year in the calendar year is the last element in the first reference window to ensure that the temperature data before the first element in the bottom temperature sequence of that year is not missed during traversal, so as not to lose the temperature change trend information.

[0071] The second construction unit is used to construct a fixed window of threshold days for the bottom temperature sequence of the year to be measured, and the fixed window satisfies: the last element of the bottom temperature sequence of the year to be measured is an array composed of all bottom temperature data of a certain day closest to the current time.

[0072] The bottom temperature sequence of the year to be measured corresponding to the fixed window includes all bottom temperature information of the current day of the year to be measured and the days corresponding to the threshold number of days before the current day.

[0073] In some embodiments, the threshold number of days may be 60 days.

[0074] The first determination unit is used to traverse the bottom temperature sequence of any year in the past years through a reference window, and determine the period of the year in which the bottom temperature sequence corresponding to the fixed window is located based on the similarity between the minimum temperature corresponding to the reference window and the minimum temperature corresponding to the fixed window, so as to obtain a reference period corresponding to the current period of the year to be measured.

[0075] Determine the period of the year in which the current period of the year to be measured is located, so as to facilitate subsequent analysis with reference to the temperature series of previous years in the same period as the current period of the year to be measured.

[0076] Since the heat in the water body will gradually dissipate at night, that is, the water temperature at night is least affected by solar radiation, the lowest temperature value of the bottom temperature sequence of any year in the past years corresponding to the daily reference window and the lowest temperature value of the bottom temperature sequence of each day in the current period of the year to be tested corresponding to the fixed window are obtained for comparison.

[0077] In some embodiments, in the first determination unit, the reference degree of the bottom temperature sequence of any year in the calendar year corresponding to the reference window is determined according to the following formula:

[0078] ;

[0079] In the formula, Indicates The reference degree of the bottom temperature series of any year in the history corresponding to the reference window reflects the possibility that the bottom temperature data of any day in the history corresponding to the reference window can be used as a reference basis for the bottom temperature data of each day in the current period (i.e., fixed window) of the year to be measured. It represents the number of days of the bottom temperature sequence in any year in the calendar year corresponding to the reference window and its value is not zero. Indicates The bottom temperature series of any year in the reference window corresponding to the The lowest temperature of the day, reflecting the The bottom temperature series of any year in the reference window corresponding to the Temperature information of the day, Indicates the bottom temperature sequence of the year to be measured corresponding to the fixed window. The lowest temperature value of the day reflects the bottom temperature sequence of the year to be measured corresponding to the fixed window. Temperature information of the day, Indicates the number of reference windows. Represents the day number in the temperature series, Represents the negative correlation normalization function, which is used for negative correlation mapping and normalization processing. It can reflect the first The bottom temperature data of the day is the same as the bottom temperature data of the current period (i.e. fixed window) of the year to be measured. The difference between the bottom temperature data of the day, It can reflect the overall difference between the bottom temperature data of any year in the reference window and the bottom temperature data of the current period (i.e. fixed window) of the year to be measured. The bigger, The larger the difference, the greater the The smaller, The smaller it is, the less likely it is that the bottom temperature data of each day in any year in the corresponding years of the reference window can be used as a reference for the bottom temperature data of each day in the current period (i.e., fixed window) of the year to be measured.

[0080] The period where the reference window corresponding to the maximum reference degree is located is used as the reference period corresponding to the current period of the year to be measured, so as to accurately obtain the reference period of the current period of the year to be measured.

[0081] Each growth stage has different requirements for water temperature, so when judging whether the current water body temperature data is abnormal, it is necessary to compare it with the temperature data in the same stage in the historical data. Since the manifestation of water temperature anomalies in the historical data is subsequent manual intervention, the change trend of water temperature data after manual intervention will change, so the possibility of manual intervention every day is judged, and then according to the high temperature continuity of the temperature data before manual intervention, the high temperature duration period corresponding to each day in the reference period is determined, so the first determination module 103 is introduced.

[0082] The first determination module 103 is used to determine the possibility of interference for each day in the reference period according to the difference in the changing trends of the central temperature sequence corresponding to the reference period and the air temperature sequence, and to determine the high temperature duration period corresponding to each day in the reference period according to the high temperature continuity of the central temperature sequence corresponding to the reference period before the artificial intervention.

[0083] In the breeding process of hairy crabs, when the weather is too high and the temperature of the breeding water body is too high, the water temperature of the breeding water body will be artificially intervened. The specific intervention methods are deepening the water level, introducing cold water, etc. The water temperature after the artificial intervention will be significantly different from the temperature change with the temperature difference between day and night and the change of solar radiation. It is shown that the water temperature change trend before the artificial intervention is similar to the temperature change trend. The water body after the artificial intervention cools down in a short time or the temperature rise rate slows down, and the water body temperature after the drop is reduced relative to the amplitude of the temperature change. Since the water body after the artificial intervention cools down in a short time or the temperature rise rate slows down, the data that the temperature reduction or the temperature rise rate is different from the temperature change trend can better judge whether artificial intervention has been performed. Therefore, the first determination module 103 is introduced.

[0084] In some embodiments, the first determining module 103 includes:

[0085] The first calculation unit is used to calculate the air temperature rate and the water temperature rate of the middle temperature sequence. The water temperature rate is the normalized value of the difference between the middle temperature data corresponding to adjacent elements in the middle temperature sequence of any year in the past years. The air temperature rate is the normalized value of the difference between the air temperature data corresponding to adjacent elements in the middle temperature sequence of any year in the past years.

[0086] The normalized value of the difference between the central temperature data corresponding to adjacent elements in the central temperature sequence of any year in the past years reflects the speed of change of the central water temperature, and the normalized value of the difference between the air temperature data corresponding to adjacent elements in the air temperature sequence of any year in the past years reflects the speed of change of the air temperature.

[0087] The traversal unit is used to traverse the central temperature series of any year in the calendar year through the reference window.

[0088] Since the air temperature of the breeding pond can reflect the temperature changes of the weather, which is an important influencing factor of the water temperature changes, and after artificial intervention of the water temperature, the water temperature is quite different before and after the artificial intervention, the weather temperature changes are almost unaffected, therefore, the second determination unit is introduced.

[0089] The second determining unit is used to determine the degree of change difference based on the air temperature rate and the water temperature rate according to the following formula:

[0090] ;

[0091] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in the temperature data reflects whether the water temperature change is caused by human intervention. Indicates The middle temperature series of any year in the reference window corresponding to the Day's The water temperature rate of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The temperature rate of the temperature data, Indicates the number of reference windows. Represents the day number in the temperature series, Indicates the number of temperature data on a certain day in the temperature sequence corresponding to the reference window. The bigger, The smaller, The larger the value, the greater the change in water temperature, while the change in air temperature is small. The larger it is, the more likely it is that water temperature changes during this period were caused by human intervention.

[0092] Before the degree of change difference is large, that is, before human intervention, the water temperature is at a high temperature for many consecutive days, so the water temperature value is similar to the temperature value of the previous day. After the degree of change difference is large, that is, after human intervention, the amplitude of the water temperature is large different from the temperature at the same time of the previous day. Therefore, before the degree of change difference is large, the similarity of the water temperature can be mainly judged, and after the degree of change difference is large, the difference of the water temperature can be mainly judged. Therefore, the third determination unit is introduced.

[0093] The third determining unit is used to determine a possible pre-interference index and a possible post-interference index based on the degree of change difference.

[0094] In some embodiments, in the third determination unit, the possible pre-interference index is determined based on the degree of change difference according to the following formula:

[0095] ;

[0096] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible pre-intervention index of the day reflects the possibility that the period is the period before artificial intervention. It indicates the number of daily temperature data in the central temperature series of any year in the reference window, and the value is not zero. Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the The temperature data from the first to the The maximum value of the difference in the change of temperature data indirectly reflects the most likely time for human intervention in this period. Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, represents the normalized exponential function, represents the negative correlation normalization function; The larger the value, the greater the temperature difference between the two days before and after at the same sampling time. The larger the The middle temperature series of any year in the reference window corresponding to the The temperature data from the first to the The greater the maximum value of the difference in the degree of change of the temperature data, the The smaller, The smaller the The smaller, that is The smaller it is, the less likely it is that the period was before human intervention.

[0097] According to the following formula, the possible post-intervention index is determined based on the degree of change difference:

[0098] ;

[0099] In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible post-intervention index of the day reflects the possibility that the period is the period after artificial intervention. represents the normalization function, The larger the value, the greater the temperature difference between the two days before and after at the same sampling time. The larger the The middle temperature series of any year in the reference window corresponding to the The temperature data from the first to the The greater the maximum value of the difference in the degree of change of the temperature data, the The bigger, The larger the The larger the The larger it is, the more likely it is that the period is the period after human intervention.

[0100] The fourth determination unit is used to determine the possibility of interference for each day in the reference period based on the pre-interference index and the possible post-interference index, so as to grasp the manual intervention situation for each day in the reference period.

[0101] In some embodiments, in the fourth determination unit, the possibility of intervention for each day in the reference period is determined based on the pre-intervention index and the possible post-intervention index according to the following formula:

[0102] ;

[0103] In the formula, For the The middle temperature series of any year in the reference window corresponding to the The possibility of interference on that day reflects the possibility of human intervention on that day. and When both are larger, The middle temperature series of any year in the reference window corresponding to the There is a high possibility of human intervention.

[0104] Artificial intervention is because the water temperature is too high for many consecutive days, which is not conducive to the growth of hairy crabs. The higher the possibility of intervention on a certain day, the more likely it is that the water temperature has been too high for many consecutive days before that day, and artificial intervention is needed. For any day, the higher the possibility of intervention on the day after that day, the higher the possibility of abnormal water temperature on that day and the consecutive days before that day. Therefore, it is necessary to determine the duration of high temperature.

[0105] The fitting unit is used to fit the temperature data of each day in the central temperature sequence of any year in the past years to obtain the central temperature fitting curve of each day in any year in the past years. The central temperature fitting curve of each day in any year in the past years is used to determine the central temperature value at a certain moment.

[0106] The daily temperature data in the middle temperature series before fitting reflects the temperature value at the sampling time, while after fitting it can reflect the trend information of temperature change.

[0107] The second calculation unit is used for taking any day as a target day, and calculating the high temperature similarity between each day and the target day in sequence from the target day forward, so as to obtain the high temperature similarity between the target day and each day before it.

[0108] In some embodiments, in the second calculation unit, according to the following formula, any day is taken as the target day, and the high temperature similarity of each day with the target day is calculated in sequence starting from the target day:

[0109] ;

[0110] In the formula, Indicates the target day in any year and the number of days counting forward from the target day. The high temperature similarity of the day reflects the similarity between the target day and the previous days’ high temperatures. Indicates the number of sampling times per day and its value is not zero. Indicates the day in any year in the calendar counting forward from the target day. Day's The middle temperature value on the middle fitting curve corresponding to the sampling moment, Indicates the maximum value of the middle temperature value on the middle fitting curve of the target day in any year in the past years, and the value is not zero. Indicates the day in any year in the calendar counting forward from the target day. Day's The duration of the middle temperature value corresponding to the sampling moment, Indicates the first day of the target day in any calendar year. The duration of the middle temperature value corresponding to the sampling moment, The larger the number, the more Day's The closer the middle temperature value on the middle fitting curve corresponding to each sampling moment is to the maximum value of the middle temperature value on the middle fitting curve of the target day, The larger the number, the more Day's The duration of the middle temperature value corresponding to the sampling time is the same as the duration of the target day The greater the difference in the duration of the middle temperature values ​​corresponding to the sampling moments, the The bigger, The smaller it is, the less similar the high temperature of the target day is to that of the previous days.

[0111] In some embodiments, on the central temperature fitting curve of each day in any year in the calendar years, for the sampling moment corresponding to the central temperature value on the central fitting curve of a certain day being greater than the temperature threshold, the time interval formed by the minimum moment of the central temperature value greater than the sampling moment and the maximum moment of the central temperature value greater than the sampling moment is used as the duration of the central temperature value at the sampling moment. The central temperature value greater than the sampling moment indicates that the central temperature value at the moment is higher, reaching the higher temperature value that needs to be regulated, the minimum moment of the central temperature value greater than the sampling moment and the maximum moment of the central temperature value greater than the sampling moment may be the starting and ending moments of the higher central temperature value, and the time interval formed by the minimum moment of the central temperature value greater than the sampling moment and the maximum moment of the central temperature value greater than the sampling moment is the duration of the central temperature value at the sampling moment.

[0112] The duration of high temperature may involve multiple consecutive days. The higher the central temperature value at a certain sampling time and the longer the central temperature value lasts, the stronger the persistence of high temperature.

[0113] The high temperature duration unit is used to take the duration of the middle temperature value corresponding to the high temperature similarity greater than or equal to the similarity threshold as the high temperature duration corresponding to the target day.

[0114] A high temperature similarity greater than or equal to the similarity threshold indicates that the temperature for this duration is continuously high, and this duration may be within the high temperature duration period corresponding to the target day.

[0115] In some embodiments, the similarity threshold may be 0.5.

[0116] The second determination module 104 is used to determine the high temperature index of the current day of the year to be measured.

[0117] The high temperature index on the current day of the year to be tested can reflect the possibility of excessively high aquaculture water temperature on the current day of the year to be tested.

[0118] In some embodiments, the second determining module 104 includes:

[0119] The abnormality possibility unit is used to use the interference possibility of each day in the reference period of the year to be measured as the abnormality possibility of each day in the reference period of the year to be measured.

[0120] If the possibility of interference on a certain day is higher, the temperature of the previous day and the days before that is continuously high, so the possibility of abnormality on the previous day is higher, and the water temperature data of the previous day during the high temperature period has a greater reference value for judging the abnormal water temperature on the current day. Therefore, the possibility of interference on each day of the reference period of the year to be measured is used as the possibility of abnormality on each day of the reference period of the year to be measured.

[0121] The third calculation unit is used to calculate the high temperature similarity between each day in the high temperature continuous period corresponding to the current day and each day in the high temperature continuous period corresponding to each day before the current day within the reference period of the year to be measured, thereby obtaining the high temperature similarity between the high temperature continuous period corresponding to the current day and the high temperature continuous period corresponding to each day before the current day.

[0122] In some embodiments, the high temperature similarity calculation formula in the second calculation unit is used to calculate the high temperature similarity between each day in the high temperature duration period corresponding to the current day and each day in the high temperature duration period corresponding to each day before the current day within the reference period of the year to be measured.

[0123] The similarity index unit is used to take the maximum value of the high temperature similarity between each day in the high temperature duration period corresponding to the current day within the reference period of the year to be tested and each day in the high temperature duration period corresponding to each day before the current day as the similarity index between the current day and each day of the year to be tested.

[0124] The similarity index can reflect the similarity between the high temperature duration period on the current day of the year to be tested and each day before that day.

[0125] The fifth determination unit is used to determine the high temperature index of the current day of the year to be measured according to the abnormal possibility of each day in the reference period of the year to be measured and the similarity index between the current day of the year to be measured and each day.

[0126] The high temperature index reflects whether the water temperature of the current day is abnormal.

[0127] In some embodiments, in the fifth determination unit, the high temperature level of the reference window corresponding to the current day of the year to be measured is determined according to the following formula:

[0128] ;

[0129] In the formula, Indicates the high temperature level corresponding to the a-th reference window on the current day, reflecting the high temperature situation in the a-th reference window on the current day. represents the number of days of the reference period corresponding to the a-th reference window, Indicates the ath reference window corresponding to the reference period The abnormal possibility of the day, Indicates the current day and the ath reference window. The similarity index of the day, The larger the value, the greater the value is. The more abnormal the water temperature, the more likely it is that The larger the value, the more the current day is different from the first day in the reference window. The greater the similarity index of the day, The larger it is, the higher the temperature corresponding to the reference window on the current day.

[0130] The average of the high temperature levels of all reference windows corresponding to the current day of the year to be measured is calculated to obtain the high temperature index of the current day of the year to be measured.

[0131] By calculating the average of the high temperature levels of the current day in different reference windows, we can obtain a high temperature index that overall reflects the high temperature conditions (i.e., abnormal conditions) of the current day.

[0132] The determination module 105 is used to determine that the temperature of the aquaculture water body on the current day of the year to be measured is abnormal in response to the high temperature index on the current day of the year to be measured being greater than or equal to the high temperature index threshold.

[0133] If the high temperature index is greater than or equal to the high temperature index threshold, it means that the water temperature on the current day of the year to be tested is abnormal and manual intervention is required, such as deepening the water level or introducing cold water to regulate the water temperature in time to ensure that the temperature of the breeding water body is suitable for the growth of hairy crabs.

[0134] In some embodiments, the high temperature index threshold may be 0.7.

[0135] In some embodiments, the system further includes a monitoring module for continuing to monitor the temperature of the aquaculture water body in response to the high temperature index of the current day of the year to be measured being less than a high temperature index threshold.

[0136] In summary, the present invention provides a smart agricultural monitoring and management system based on big data and the Internet of Things. The system can reduce the impact of periodic temperature changes, promptly detect abnormal water temperature, thereby ensuring the timeliness of manual intervention and improving the accuracy of water temperature regulation for hairy crab farming, thereby ensuring that the temperature of the farming water body meets the growth conditions of hairy crabs.

[0137] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A smart agricultural monitoring and management system based on big data and the Internet of Things, characterized in that: The system comprises: An acquisition module is used to acquire a middle temperature sequence, a bottom temperature sequence and an air temperature sequence of a culture water body, wherein the middle temperature sequence includes the middle temperature sequence of the year to be measured and the middle temperature sequence of previous years, the bottom temperature sequence includes the bottom temperature sequence of the year to be measured and the bottom temperature sequence of previous years, and the air temperature sequence includes the air temperature sequence of the year to be measured and the air temperature sequence of previous years, wherein the elements of the middle temperature sequence are an array composed of all middle temperature data of corresponding days, the elements of the bottom temperature sequence are an array composed of all bottom temperature data of corresponding days, and the elements of the air temperature sequence are an array composed of all air temperature data of corresponding days; An analysis module is used to analyze the similarity between the bottom temperature sequence of the current period of the year to be measured and the bottom temperature sequence of any year in the past years, and determine a reference period corresponding to the current period of the year to be measured, wherein the reference period is a period of any year in the past years, and is used to reflect the period of the current period of the year to be measured in the year; The first determination module is used to determine the possibility of interference every day in the reference period according to the difference in the change trend of the central temperature sequence corresponding to the reference period and the air temperature sequence, and to determine the high temperature duration period corresponding to each day in the reference period according to the high temperature continuity of the central temperature sequence corresponding to the reference period before the artificial intervention; The second determination module is used to determine the high temperature index of the current day of the year to be tested; A determination module, configured to determine that the temperature of the aquaculture water body on the current day of the year to be tested is abnormal in response to the high temperature index on the current day of the year to be tested being greater than or equal to the high temperature index threshold; The second determining module includes: An abnormality possibility unit, used to use the interference possibility of each day in the reference period of the year to be measured as the abnormality possibility of each day in the reference period of the year to be measured; The third calculation unit is used to calculate the high temperature similarity between each day in the high temperature continuous period corresponding to the current day and each day in the high temperature continuous period corresponding to each day before the current day within the reference period of the year to be measured; A similarity index unit is used to take the maximum value of the high temperature similarity between the high temperature duration period corresponding to the current day and the high temperature duration period corresponding to each day before the current day within the reference period of the year to be tested as the similarity index between the current day and each day of the year to be tested; The fifth determination unit is used to determine the high temperature index of the current day of the year to be measured according to the abnormal possibility of each day in the reference period of the year to be measured and the similarity index between the current day of the year to be measured and each day.

2. According to claim 1, a smart agricultural monitoring and management system based on big data and the Internet of Things is characterized in that: The analysis module comprises: The first construction unit is used to construct a reference window of threshold days based on the bottom temperature sequence of any year in the past years, and the initial reference window satisfies: an array consisting of all bottom temperature data corresponding to the first day of any year in the past years is the last element in the first reference window, and the reference window is used to slide with a step length of one day, and the multiple reference windows obtained by sliding have a corresponding relationship with the bottom temperature sequence of any year in the past years; The second construction unit is used to construct a fixed window of threshold days for the bottom temperature sequence of the year to be measured, and the fixed window satisfies: the last element of the bottom temperature sequence of the year to be measured is an array composed of all bottom temperature data of a certain day closest to the current time; The first determination unit is used to traverse the bottom temperature sequence of any year in the past years through a reference window, and determine the period of the year in which the bottom temperature sequence corresponding to the fixed window is located based on the similarity between the minimum temperature corresponding to the reference window and the minimum temperature corresponding to the fixed window, so as to obtain a reference period corresponding to the current period of the year to be measured.

3. According to claim 2, a smart agricultural monitoring and management system based on big data and the Internet of Things is characterized in that: The reference degree of the bottom temperature series of any year in the reference window is determined according to the following formula: ； In the formula, Indicates The reference degree of the bottom temperature series of any year in the historical years corresponding to the reference window, represents the number of days of the bottom temperature series in any year in the calendar year corresponding to the reference window, Indicates The bottom temperature series of any year in the reference window corresponding to the The lowest temperature of the day, Indicates the bottom temperature sequence of the year to be measured corresponding to the fixed window. The lowest temperature of the day, Indicates the number of reference windows. Indicates the day number in the temperature series, represents the negative correlation normalization function; The period of the reference window corresponding to the maximum reference degree is taken as the reference period corresponding to the current period of the year to be tested.

4. According to claim 1, a smart agricultural monitoring and management system based on big data and the Internet of Things is characterized in that: The first determining module includes: A first calculation unit is used to calculate the air temperature rate and the water temperature rate of the middle temperature sequence, wherein the water temperature rate is the normalized value of the difference between the middle temperature data corresponding to adjacent elements in the middle temperature sequence of any year in the past years, and the air temperature rate is the normalized value of the difference between the air temperature data corresponding to adjacent elements in the air temperature sequence of any year in the past years; A traversal unit is used to traverse the middle temperature series of any year in the calendar year through a reference window; The second determining unit is used to determine the degree of change difference based on the air temperature rate and the water temperature rate according to the following formula: ； In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The water temperature rate of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The temperature rate of the temperature data, Indicates the number of reference windows. Indicates the day number in the temperature series, Indicates the number of temperature data on a certain day in the temperature sequence corresponding to the reference window; A third determining unit, configured to determine a possible pre-interference index and a possible post-interference index based on the degree of change difference; a fourth determination unit, configured to determine the possibility of intervention for each day in a reference period based on the pre-intervention index and the possible post-intervention index; A fitting unit is used to fit the temperature data of each day in the central temperature sequence of any year in the past years to obtain a fitting curve of the central temperature of each day in any year in the past years. The fitting curve of the central temperature of each day in any year in the past years is used to determine the central temperature value at a certain moment; The second calculation unit is used to take any day as a target day and calculate the high temperature similarity between each day and the target day in sequence starting from the target day; The high temperature duration unit is used to take the duration of the middle temperature value corresponding to the high temperature similarity greater than or equal to the similarity threshold as the high temperature duration corresponding to the target day.

5. According to claim 4, a smart agricultural monitoring and management system based on big data and the Internet of Things is characterized in that: According to the following formula, the possible pre-intervention index is determined based on the degree of difference in the changes: ； In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible pre-interference index of the day, Indicates the number of daily temperature data in the central temperature series of any year in the reference window. Indicates The middle temperature series of any year in the reference window corresponding to the Day's The degree of difference in temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the The temperature data from the first to the The maximum value of the difference in the degree of change of temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, Indicates The middle temperature series of any year in the reference window corresponding to the Day's The value of the temperature data, represents the normalized exponential function, represents the negative correlation normalization function; According to the following formula, the possible post-intervention index is determined based on the degree of difference in the changes: ； In the formula, Indicates The middle temperature series of any year in the reference window corresponding to the The possible post-intervention index of the day, Represents the normalization function.

6. The smart agricultural monitoring and management system based on big data and the Internet of Things according to claim 5 is characterized in that: The possibility of intervention for each day in the reference period is determined based on the pre-intervention index and the possible post-intervention index according to the following formula: ； In the formula, For the The middle temperature series of any year in the reference window corresponding to the The possibility of heaven's intervention.

7. The smart agricultural monitoring and management system based on big data and the Internet of Things according to claim 4 is characterized in that: According to the following formula, take any day as the target day, and calculate the high temperature similarity of each day with the target day in sequence from the target day forward: ； In the formula, Indicates the target day in any year and the number of days counting forward from the target day. The similarity of high temperatures of the day, Indicates the number of sampling times per day, Indicates the day in any year in the calendar counting forward from the target day. Day's The middle temperature value on the middle fitting curve corresponding to the sampling moment, It represents the maximum value of the middle temperature on the middle fitting curve of the target day in any year in the calendar year. Indicates the day in any year in the calendar counting forward from the target day. Day's The duration of the middle temperature value corresponding to the sampling moment, Indicates the first day of the target day in any calendar year. The duration of the middle temperature value corresponding to the sampling moment.

8. The smart agricultural monitoring and management system based on big data and the Internet of Things according to claim 1 is characterized in that: According to the following formula, determine the high temperature degree of the reference window corresponding to the current day of the year to be measured: ； In the formula, Indicates the high temperature level corresponding to the a-th reference window on the current day, represents the number of days of the reference period corresponding to the a-th reference window, Indicates the ath reference window corresponding to the reference period The abnormal possibility of the day, Indicates the current day and the ath reference window. Similarity index of the day; The average of the high temperature levels of all reference windows corresponding to the current day of the year to be measured is calculated to obtain the high temperature index of the current day of the year to be measured.

9. The smart agricultural monitoring and management system based on big data and the Internet of Things according to claim 1 is characterized in that: The system further comprises: The monitoring module is used to continue monitoring the temperature of the aquaculture water body in response to the high temperature index of the current day of the year to be tested being less than the high temperature index threshold.

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