Intelligent monitoring method and system for Chinese medicinal materials growth information based on air quality

Abstract

The present invention belongs to the technical field of Chinese medicinal material growth monitoring, and discloses an intelligent monitoring method and system for Chinese medicinal material growth information based on air quality: obtaining planting monitoring data of each monitoring point of a medicinal material planting base through a data acquisition device, preprocessing the planting monitoring data, obtaining planting environment data, calculating the growth index of the Chinese medicinal materials in the medicinal material planting base based on the planting environment data, extracting the data acquisition time of the planting monitoring data, determining the growth regulation period of the Chinese medicinal materials in the medicinal material planting base based on the data acquisition time, and obtaining the preset index range corresponding to the growth regulation period, and notifying relevant staff to take measures when the growth index does not meet the preset index range. The technical solution of the present invention can improve the real-time and accuracy of Chinese medicinal material growth monitoring, and help to ensure the growth quality of Chinese medicinal materials and increase the yield of Chinese medicinal materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of Chinese medicinal material growth monitoring, and in particular relates to an intelligent monitoring method and system for Chinese medicinal material growth information based on air quality. Background Art

[0002] As a treasure of traditional Chinese medicine, the quality of Chinese medicinal materials is closely related to the environment in which they grow. Air quality, as one of the important factors affecting the growth of Chinese medicinal materials, is directly related to the accumulation of effective ingredients, the occurrence of pests and diseases, and the overall quality of medicinal materials. Therefore, real-time monitoring of air quality in the growth environment of Chinese medicinal materials is of great significance for optimizing planting strategies and improving the quality of medicinal materials.

[0003] Similar prior art includes a Chinese patent application with publication number CN107844089A, which discloses a planting early warning method, system and planting management system, which obtains the growth data of the planted crops in real time; receives the sensing data sent by the data acquisition device in real time; determines whether the planted crops are in the preset reminder stage according to the growth data, and issues an early warning message when the planted crops are in the preset reminder stage; and compares the sensing data with the sample data in the database according to the growth data; and issues an early warning message when the comparison result is not within the preset range. There is also a Chinese patent application with publication number CN112882517A, which discloses a smart agricultural planting environment monitoring method and cloud monitoring platform based on big data and the Internet of Things, which detects air humidity, temperature, carbon dioxide concentration, nutrient solution height, light intensity and nutrient solution element concentration; compares the collected data with the corresponding standard data; calculates the fruit and vegetable growth influence coefficient, nutrient solution supplement ratio coefficient and nutrient solution supplement amount; supplements the nutrient solution in each hydroponic box according to the nutrient solution supplement amount; and displays the fruit and vegetable growth influence coefficient and nutrient solution supplement amount.

[0004] The above two methods only consider the comparison of a single parameter when monitoring plant growth, while plant growth is a complex process, and the range of growth indicators varies according to the growth stage of the plant. Therefore, it is an urgent problem to provide an intelligent monitoring method and system for Chinese medicinal materials growth information based on air quality to improve the real-time and accuracy of Chinese medicinal materials growth monitoring. Summary of the invention

[0005] In response to the above-mentioned technical problems, the present invention provides a method and system for intelligently monitoring the growth information of Chinese medicinal materials based on air quality.

[0006] In a first aspect, the present invention provides an intelligent monitoring method for Chinese medicinal material growth information based on air quality, the method comprising the following steps:

[0007] Step 1: Obtain the planting monitoring data of each monitoring point of the medicinal material planting base through data acquisition equipment;

[0008] Step 2: pre-process the planting monitoring data to obtain planting environment data;

[0009] Step 3: Calculate the growth index of Chinese medicinal materials in the medicinal material planting base based on the planting environment data;

[0010] Step 4: extract the data collection time of the planting monitoring data, determine the growth regulation period of the medicinal materials in the medicinal material planting base based on the data collection time, and obtain the preset index range corresponding to the growth regulation period;

[0011] Step 5: Determine whether the growth index meets the preset index range. If not, trigger the early warning mechanism and notify relevant staff to take measures.

[0012] Specifically, step 1 includes:

[0013] Step 11, dividing all data acquisition devices into first-category data acquisition devices and second-category data acquisition devices, and setting a first data acquisition frequency for the first-category data acquisition devices and a second data acquisition frequency for the second-category data acquisition devices, wherein the first data acquisition frequency is higher than the second data acquisition frequency;

[0014] Step 12: periodically obtain monitoring data of the first type of data acquisition equipment and the second type of data acquisition equipment;

[0015] Step 13: Calculate the estimated monitoring data corresponding to the second type of data acquisition device based on the first monitoring data collected by the first type of data acquisition device;

[0016] Step 14, determining whether the monitoring data acquired at the current moment only includes the first monitoring data, if so, storing the first monitoring data and the estimated monitoring data as the planting monitoring data at the current moment, and then returning to step 12, if not, storing the first monitoring data and the second monitoring data collected by the second type of data collection equipment as the planting monitoring data at the current moment, and then entering step 15;

[0017] Step 15, calculate the first difference between any presumed monitoring data and the second monitoring data collected by the second type of data acquisition device corresponding to any presumed monitoring data, and determine whether the first difference is greater than or equal to a first preset value. If not, return to step 12. If so, set the second type of data acquisition device corresponding to any presumed monitoring data to the first type of data acquisition device, and set the data acquisition frequency of the second type of data acquisition device corresponding to any presumed monitoring data to the first data acquisition frequency, and then return to step 12.

[0018] Specifically, in step 15, after the data collection frequency of the second type of data collection device corresponding to any inferred monitoring data is set to the first data collection frequency, the first value of all newly set first type of data collection devices is calculated. If the first value is greater than or equal to the second preset value, return to step 11.

[0019] Specifically, in step 11, the data acquisition devices are divided into the first type of data acquisition devices and the second type of data acquisition devices, including:

[0020] Step 111, obtaining the third monitoring data collected by all data collection devices at the first moment and the fourth monitoring data collected at the second moment, wherein the second moment is the moment before the first moment;

[0021] Step 112: extract any data acquisition device, take the third monitoring data collected by any data acquisition device at the first moment as the dependent variable, take the fourth monitoring data collected by all data acquisition devices at the second moment as the independent variable, and construct a first linear equation corresponding to any data acquisition device;

[0022] Step 113, fitting the first linear equation to determine the weight coefficient corresponding to each independent variable;

[0023] Step 114, extracting a weight coefficient corresponding to any independent variable and defining it as a first weight coefficient, determining whether the first weight coefficient is greater than or equal to a third preset value, and if so, defining the data acquisition device corresponding to any independent variable as a driving acquisition device of any data acquisition device, and defining any data acquisition device as a driven acquisition device of the data acquisition device corresponding to any independent variable;

[0024] Step 115: after traversing all data acquisition devices, extract any data acquisition device, calculate the second value of the driving acquisition device and the third value of the driven acquisition device corresponding to any data acquisition device, and use the ratio of the third value to the second value as the influence value of any data acquisition device;

[0025] Step 116: after traversing all data acquisition devices, extract any data acquisition device, and determine whether the influence value corresponding to any data acquisition device is greater than or equal to a fourth preset value; if so, define any data acquisition device as a first-category data acquisition device; if not, define any data acquisition device as a second-category data acquisition device;

[0026] Step 117, extract any second-category data acquisition device, take the third monitoring data of any second-category data acquisition device at the first moment as the dependent variable, take the third monitoring data and the fourth monitoring data of all first-category data acquisition devices at the first moment and the second moment as independent variables, and construct a second linear equation corresponding to any second-category data acquisition device;

[0027] Step 118, fitting the second linear equation to determine the second weight coefficient corresponding to each independent variable, then substituting the monitoring data value and the second weight coefficient corresponding to each independent variable into the formula on the right side of the second linear equation to calculate the estimated value of any second type data acquisition device;

[0028] Step 119, determine whether the estimated value is less than or equal to the fifth preset value. If so, set the data acquisition frequency of the first type of data acquisition equipment to the first data acquisition frequency, and set the data acquisition frequency of the second type of data acquisition equipment to the second data acquisition frequency. At the same time, substitute the second weight coefficient corresponding to each independent variable into the second linear equation to generate a formula for calculating the estimated monitoring data. If not, reduce the fourth preset value by the preset value and define it as a new fourth preset value, and then return to step 116.

[0029] Specifically, the planting monitoring data includes a first temperature of the air, a second temperature of a preset position of the plant, air humidity, a specific gas content in the air, and specific sunlight parameters.

[0030] Specifically, step 3 includes:

[0031] Based on the growth regulation period, the target value of air humidity corresponding to the data collection time is obtained, and then the growth index CI is calculated based on the first formula, which is:

[0032] ,

[0033] in, , , and is the parameter weight coefficient, T1 is the first temperature, T2 is the second temperature, SC is the air humidity, SCT is the target value, SG is the specific gas content, and SR is the specific sunlight parameter.

[0034] Specifically, the planting environment data includes the daily average temperature. After step 5, the method further includes periodically calculating the growth period of the Chinese medicinal materials in the medicinal material planting base, including the following steps:

[0035] Step 61, obtaining the daily average temperature from the planting date to the current date, and calculating the daily first temperature difference value based on the daily average temperature and the first temperature preset value;

[0036] Step 62: any first temperature difference value is defined as a natural number as a second temperature difference value, and all second temperature difference values ​​are accumulated to obtain a first accumulated value;

[0037] Step 63, determining whether the first accumulated value is greater than or equal to a sixth preset value, if so, defining the current date as the growth end date, and sending the growth end date to relevant staff, if not, proceeding to step 64;

[0038] Step 64, predicting the third temperature difference value of the i-th day after the current date, summing the first accumulated value and the third temperature difference to obtain a second accumulated value, and determining whether the second accumulated value is greater than or equal to a sixth preset value, if so, proceeding to step 65, if not, defining the second accumulated value as the first accumulated value, and setting i=i+1, and then returning to step 64, wherein i is a positive integer greater than or equal to 1;

[0039] Step 65: define the i-th day as the growth end date, and send the growth end date to relevant staff.

[0040] Specifically, in step 64, predicting the third temperature difference value on the i-th day after the current date includes:

[0041] Step 641, obtaining the historical daily average temperature of multiple dates with the same date as the i-th day within a preset time before the current date;

[0042] Step 642, respectively calculating the difference between the average temperature of each historical day and the first temperature preset value, which is defined as a fourth temperature difference;

[0043] Step 643: When any fourth temperature difference is a negative number, any fourth temperature difference is set to 0, and an average value of all fourth temperature differences is calculated and defined as the third temperature difference.

[0044] In a second aspect, the present invention also provides an intelligent monitoring system for Chinese medicinal material growth information based on air quality, the system comprising: a data acquisition module, a data processing module, a data analysis module, and a message notification module;

[0045] A data acquisition module is used to acquire the planting monitoring data of each monitoring point of the medicinal material planting base through data acquisition equipment;

[0046] A data processing module is used to pre-process the planting monitoring data to obtain planting environment data;

[0047] A data analysis module is used to calculate the growth index of Chinese medicinal materials in the medicinal material planting base according to the planting environment data, extract the data collection time of the planting monitoring data, determine the growth regulation period of the Chinese medicinal materials in the medicinal material planting base based on the data collection time, and obtain the preset index range corresponding to the growth regulation period;

[0048] The message notification module is used to determine whether the growth index meets the preset index range. If not, the early warning mechanism is triggered to notify relevant personnel to take measures.

[0049] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0050] 1. Collect and analyze data in real time, promptly discover the impact of environmental changes on the growth of Chinese medicinal materials, and promptly notify relevant personnel to take measures when environmental conditions are not conducive to the growth of Chinese medicinal materials. By precisely controlling the growth environment, it helps to improve the growth quality and yield of Chinese medicinal materials.

[0051] 2. By optimizing the data collection frequency of data collection equipment, the efficiency and accuracy of data collection are improved, while unnecessary energy consumption is reduced.

[0052] 3. Predict the growth period of Chinese medicinal materials and help growers better arrange the picking and sales plans of Chinese medicinal materials in a scientific and reasonable manner.

[0053] 4. The system can be adjusted according to different environmental conditions and types of medicinal materials, and has good adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0055] Figure 1 It is a flow chart of the intelligent monitoring method of Chinese medicinal materials growth information based on air quality of the present invention;

[0056] Figure 2 It is a modular schematic diagram of the intelligent monitoring system for Chinese medicinal materials growth information based on air quality of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0058] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0059] Figure 1 FIG. 1 is a flow chart of an embodiment of an intelligent monitoring method for growth information of Chinese medicinal materials based on air quality provided by the present invention, and the flow chart specifically includes the following steps:

[0060] Step 1: Obtain the planting monitoring data of each monitoring point in the medicinal material planting base through data acquisition equipment.

[0061] When the medicinal material planting base is large, the base can be divided into multiple grids of uniform size, monitoring points can be set in each grid, data acquisition equipment can be placed at each monitoring point, and each grid can be monitored separately.

[0062] Data acquisition equipment includes various types of sensors, thermal imagers and / or solar radiation meters, etc.

[0063] Specifically, step 1 includes:

[0064] Step 11, divide all data acquisition devices into first category data acquisition devices and second category data acquisition devices, set a first data acquisition frequency for the first category data acquisition devices, and set a second data acquisition frequency for the second category data acquisition devices, the first data acquisition frequency is higher than the second data acquisition frequency.

[0065] Step 12: Periodically obtain monitoring data from the first type of data acquisition equipment and the second type of data acquisition equipment.

[0066] Step 13: Calculate the estimated monitoring data corresponding to the second type of data acquisition device based on the first monitoring data acquired by the first type of data acquisition device.

[0067] Step 14, determine whether the monitoring data obtained at the current moment only includes the first monitoring data. If so, store the first monitoring data and the inferred monitoring data as the planting monitoring data at the current moment, and then return to step 12. If not, store the first monitoring data and the second monitoring data collected by the second type of data acquisition equipment as the planting monitoring data at the current moment, and then enter step 15.

[0068] Step 15, calculate the first difference between any presumed monitoring data and the second monitoring data collected by the second type of data acquisition device corresponding to any presumed monitoring data, and determine whether the first difference is greater than or equal to a first preset value. If not, return to step 12. If so, set the second type of data acquisition device corresponding to any presumed monitoring data to the first type of data acquisition device, and set the data acquisition frequency of the second type of data acquisition device corresponding to any presumed monitoring data to the first data acquisition frequency, and then return to step 12.

[0069] The first preset value is set according to the experience of those skilled in the art or according to an actual application scenario, and the embodiments of the present application are not limited to this.

[0070] The data acquisition equipment at each monitoring point is divided into two categories, one with a higher data acquisition frequency and the other with a lower data acquisition frequency. The monitoring data of the data acquisition equipment with a higher data acquisition frequency is used to supplement the data of the data acquisition equipment with a lower data acquisition frequency. Based on this, while ensuring effective data acquisition, the energy consumption of the data acquisition equipment is reduced, the service life of the data acquisition equipment is extended, and the overall operating cost is reduced.

[0071] During the data collection process, the estimated monitoring data and the actual monitoring data are compared periodically. When the error is greater than the preset value, it means that the accuracy of the estimated monitoring data has decreased. In this case, the data collection frequency of the second-class data collection equipment with a larger error is increased to ensure the accuracy and quality of the data, providing strong support for subsequent analysis.

[0072] Specifically, in step 15, after the data collection frequency of the second type of data collection device corresponding to any inferred monitoring data is set to the first data collection frequency, the first value of all newly set first type of data collection devices is calculated. If the first value is greater than or equal to the second preset value, return to step 11.

[0073] The second preset value is set according to the experience of those skilled in the art or according to an actual application scenario, and the embodiment of the present application does not limit this.

[0074] During the data collection process, the second-category data collection equipment with decreased accuracy of the calculated inferred monitoring data is set as a new first-category data collection equipment. When the number of this type of data collection equipment is greater than the second preset value, it means that there are a large number of data collection equipment using a higher data collection frequency, and the initially set combination of the first-category data collection equipment and the second-category data collection equipment is not stable enough, resulting in large errors. All data collection equipment is re-divided to generate a new data collection equipment combination pattern to reduce the overall energy consumption of the data collection equipment.

[0075] Specifically, in step 11, the data acquisition devices are divided into the first type of data acquisition devices and the second type of data acquisition devices, including:

[0076] Step 111: Acquire the third monitoring data collected by all data collection devices at a first moment and the fourth monitoring data collected at a second moment, wherein the second moment is a moment before the first moment.

[0077] Step 112: extract any data acquisition device, take the third monitoring data collected by any data acquisition device at the first moment as the dependent variable, take the fourth monitoring data collected by all data acquisition devices at the second moment as the independent variable, and construct a first linear equation corresponding to any data acquisition device.

[0078] Step 113: Fit the first linear equation to determine the weight coefficient corresponding to each independent variable.

[0079] Step 114, extract the weight coefficient corresponding to any independent variable and define it as the first weight coefficient, determine whether the first weight coefficient is greater than or equal to the third preset value, and if so, define the data acquisition device corresponding to any independent variable as the driving acquisition device of any data acquisition device, and define any data acquisition device as the driven acquisition device of the data acquisition device corresponding to any independent variable.

[0080] Step 115, after traversing all data acquisition devices, extract any data acquisition device, calculate the second value of the driving acquisition device and the third value of the driven acquisition device corresponding to any data acquisition device, and use the ratio of the third value to the second value as the influence value of any data acquisition device.

[0081] Step 116: After traversing all data acquisition devices, extract any data acquisition device and determine whether the influence value corresponding to any data acquisition device is greater than or equal to the fourth preset value. If so, define any data acquisition device as a first-category data acquisition device; if not, define any data acquisition device as a second-category data acquisition device.

[0082] Step 117, extract any second-category data acquisition device, take the third monitoring data of any second-category data acquisition device at the first moment as the dependent variable, take the third monitoring data and fourth monitoring data of all first-category data acquisition devices at the first moment and the second moment as independent variables, and construct a second linear equation corresponding to any second-category data acquisition device.

[0083] Step 118, fit the second linear equation to determine the second weight coefficient corresponding to each independent variable, then substitute the monitoring data value and the second weight coefficient corresponding to each independent variable into the formula on the right side of the second linear equation to calculate the estimated value of any second-category data acquisition device.

[0084] Step 119, determine whether the estimated value is less than or equal to the fifth preset value. If so, set the data acquisition frequency of the first type of data acquisition equipment to the first data acquisition frequency, and set the data acquisition frequency of the second type of data acquisition equipment to the second data acquisition frequency. At the same time, substitute the second weight coefficient corresponding to each independent variable into the second linear equation to generate a formula for calculating the estimated monitoring data. If not, reduce the fourth preset value by the preset value and define it as a new fourth preset value, and then return to step 116.

[0085] The third preset value, the fourth preset value and the fifth preset value are set according to the experience of those skilled in the art or according to actual application scenarios, and the embodiments of the present application are not limited to this.

[0086] The above first linear equation is a multiple linear regression equation. For example, there are three data acquisition devices A1, A2, A3, A4 and A5. The monitoring data of the three data acquisition devices at the first moment are a1, a2, a3, a4 and a5 respectively. The monitoring data of the three data acquisition devices at the second moment are b1, b2, b3, b4 and b5 respectively. The data acquisition device A1 is extracted, and the first linear equation corresponding to the data acquisition device A1 is constructed: , fit the first linear equation and determine the weight coefficients corresponding to b1, b2, b3, b4 and b5 , , , and If the value of If the value of is greater than the third preset value, it means that the monitoring data b5 of the data acquisition device A5 at the second moment has a great influence on the monitoring data a1 of the data acquisition device A1 at the first moment, and the data acquisition device A5 is the driving acquisition device of the data acquisition device A1, and the data acquisition device A1 is the driven acquisition device of the data acquisition device A5. If the weight coefficient is less than the third preset value, it means that the independent variable contributes less to the dependent variable. By filtering out the independent variables with small contributions, the amount of subsequent calculations can be reduced.

[0087] After traversing all data acquisition devices, data acquisition device A1 is the driving acquisition device of data acquisition devices A2, A3 and A5, data acquisition device A2 is the driving acquisition device of data acquisition devices A2 and A3, data acquisition device A3 is the driving acquisition device of data acquisition device A2, data acquisition device A4 is the driving acquisition device of data acquisition devices A2, A4 and A5, data acquisition device A5 is the driving acquisition device of data acquisition device A1, data acquisition device A1 is the driven acquisition device of data acquisition device A5, data acquisition device A2 is the driven acquisition device of data acquisition devices A1 and A2 , A3 and A4 are driven acquisition devices, data acquisition device A3 is the driven acquisition device of data acquisition devices A1 and A2, data acquisition device A4 is the driven acquisition device of data acquisition device A4, data acquisition device A5 is the driven acquisition device of data acquisition devices A1 and A4, for data acquisition device A1, its influence value is 3 / 1, for data acquisition device A2, its influence value is 2 / 4, for data acquisition device A3, its influence value is 1 / 2, for data acquisition device A4, its influence value is 3 / 1, for data acquisition device A5, its influence value is 1 / 2. If the fourth preset value is 2, the first data acquisition devices are data acquisition devices A1 and A4, and the second type of data acquisition devices are data acquisition devices A2, A3 and A5.

[0088] Exemplarily, the second type of data acquisition device is extracted as data acquisition device A2, and a second linear equation corresponding to data acquisition device A2 is constructed: , substitute the values ​​of a2, b1, a1, b4 and a4 into the second linear equation, fit the second linear equation, and determine the weight coefficient of each independent variable.

[0089] There is a correlation between the data of various data acquisition devices that monitor the planting environment in the same area. By identifying the correlation between multiple data acquisition devices, the data acquisition device that has a large impact on other data acquisition devices is set as the first type of data acquisition device, and the data acquisition device that is greatly affected by other data acquisition devices is set as the second type of data acquisition device. A lower data acquisition frequency is set for the second type of data acquisition device to comprehensively reduce the energy consumption of the data acquisition equipment.

[0090] Step 2: Preprocess the planting monitoring data to obtain planting environment data.

[0091] Exemplarily, the above preprocessing includes data cleaning, data conversion and / or data interpolation, etc.

[0092] Step 3: Calculate the growth index of Chinese medicinal materials in the medicinal material planting base based on the planting environment data.

[0093] Specifically, the planting monitoring data includes a first temperature of the air, a second temperature of a preset position of the plant, air humidity, a specific gas content in the air, and specific sunlight parameters.

[0094] Exemplarily, the specific gas content in the air is oxygen or carbon dioxide, and the specific sunlight parameter is solar radiation illuminance or solar radiation.

[0095] Exemplarily, the preset position of the plant is a position at a predetermined distance from the leaves of the plant, and the second temperature is the surface temperature of the leaves of the plant.

[0096] Specifically, step 3 includes:

[0097] Based on the growth regulation period, the target value of air humidity corresponding to the data collection time is obtained, and then the growth index CI is calculated based on the first formula, which is:

[0098] ,

[0099] in, , , and is the parameter weight coefficient, T1 is the first temperature, T2 is the second temperature, SC is the air humidity, SCT is the target value, SG is the specific gas content, and SR is the specific sunlight parameter.

[0100] Plants can be dormant (or less active) and active during the day, and the time periods corresponding to different states are set as different growth regulation periods. The growth regulation period is pre-set, and different types of Chinese medicinal materials can be set with different growth regulation periods. For example, 7:00 to 11:00 in the morning is the growth rising period, 11:00 to 15:00 is the growth active period, 15:00 to 18:00 is the growth declining period, and the rest of the time is the growth dormant period.

[0101] The target value of any growth regulation period is the humidity value that is most suitable for plant growth in any growth regulation period.

[0102] The growth index calculation formula is constructed by comprehensively considering the photosynthesis, evaporation and respiration of plants. By calculating the growth index, the growth status of Chinese medicinal materials is quantified, so that the monitoring results can more objectively and accurately reflect the current growth status of the plants.

[0103] Step 4: extract the data collection time of the planting monitoring data, determine the growth regulation period of the medicinal materials in the medicinal material planting base based on the data collection time, and obtain the preset index range corresponding to the growth regulation period.

[0104] The preset index range is set according to the experience of those skilled in the art or according to the actual application scenario, and the present application embodiment does not limit this. In different growth regulation periods, the physiological activities and growth states of plants are different, and the corresponding preset index ranges are also different.

[0105] Step 5: Determine whether the growth index meets the preset index range. If not, trigger the early warning mechanism and notify relevant staff to take measures.

[0106] In order to improve the quality and yield of medicinal materials in the medicinal material planting base, when the growth index exceeds the preset index range corresponding to the growth regulation period, the staff will be notified to make adjustments to ensure that the growth index is within the ideal range and reduce the negative impact of the planting environment on plant growth.

[0107] Exemplarily, the measures that staff need to take include: if the humidity is too low, increase the frequency or amount of irrigation; if the humidity is too high, reduce irrigation; if the sunlight parameters are not suitable, improve the lighting conditions by adjusting the shade net or using artificial light sources; use heating or cooling equipment to adjust the ambient temperature to make it more suitable for the growth of Chinese medicinal materials, etc.

[0108] Specifically, the planting environment data includes the daily average temperature. After step 5, the method further includes periodically calculating the growth period of the Chinese medicinal materials in the medicinal material planting base, including the following steps:

[0109] Step 61: Obtain the daily average temperature from the planting date to the current date, and calculate the daily first temperature difference based on the daily average temperature and the first temperature preset value.

[0110] Step 62: any first temperature difference value is defined as a second temperature difference value when it is a natural number, and all second temperature difference values ​​are accumulated to obtain a first accumulated value.

[0111] Step 63, determine whether the first accumulated value is greater than or equal to the sixth preset value, if so, define the current date as the growth end date, and send the growth end date to relevant staff, if not, proceed to step 64.

[0112] Step 64, predict the third temperature difference value on the i-th day after the current date, sum the first accumulated value and the third temperature difference value, obtain the second accumulated value, and determine whether the second accumulated value is greater than or equal to the sixth preset value. If so, proceed to step 65; if not, define the second accumulated value as the first accumulated value, and make i=i+1, and then return to step 64, where i is a positive integer greater than or equal to 1.

[0113] Step 65: define the i-th day as the growth end date, and send the growth end date to relevant staff.

[0114] The first temperature preset value is the development threshold temperature of the plant, and the sixth temperature preset value is the total heat required for the plant to complete its growth. It is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application are not limited to this.

[0115] Each type of plant needs the temperature to reach the first temperature preset value (i.e. the development threshold temperature) before it can begin to develop and grow. When the total effective temperature of the plant's entire growth period reaches the sixth preset value, it means that the plant growth is complete and can be harvested. By calculating the growth period, the growth cycle of Chinese medicinal materials can be managed more accurately to ensure harvesting at the best time.

[0116] Specifically, in step 64, predicting the third temperature difference value on the i-th day after the current date includes:

[0117] Step 641: Obtain the historical daily average temperature of multiple dates that are the same as the i-th date within a preset time before the current date.

[0118] Step 642: Calculate the difference between the average temperature of each historical day and the first temperature preset value, and define it as a fourth temperature difference.

[0119] Step 643: When any fourth temperature difference is a negative number, any fourth temperature difference is set to 0, and an average value of all fourth temperature differences is calculated and defined as the third temperature difference.

[0120] The preset time is set according to the experience of those skilled in the art or according to the actual application scenario, and the present application embodiment does not limit this. Exemplarily, the preset time is 5 years.

[0121] If the i-th day is September 1, 2023, the historical daily average temperatures on September 1, 2022, 2021, 2020, 2019, and 2018 are extracted.

[0122] After obtaining the fourth temperature difference value of each day, the fourth temperature difference value is stored and can be directly used when calculating the temperature difference value again in the future, thereby reducing the amount of calculation.

[0123] Figure 2 FIG. 1 is a schematic diagram of the structure of an embodiment of the intelligent monitoring system for Chinese medicinal material growth information based on air quality provided by the present invention. Figure 2 As shown, the system includes: a data acquisition module 10, a data processing module 20, a data analysis module 30 and a message notification module 40.

[0124] The data acquisition module 10 is used to acquire the planting monitoring data of each monitoring point of the medicinal material planting base through data acquisition equipment.

[0125] The data processing module 20 is used to pre-process the planting monitoring data to obtain planting environment data.

[0126] The data analysis module 30 is used to calculate the growth index of Chinese medicinal materials in the medicinal material planting base according to the planting environment data, extract the data collection time of the planting monitoring data, determine the growth adjustment period of the Chinese medicinal materials in the medicinal material planting base based on the data collection time, and obtain the preset index range corresponding to the growth adjustment period.

[0127] The message notification module 40 is used to determine whether the growth index meets the preset index range. If not, the early warning mechanism is triggered to notify relevant personnel to take measures.

[0128] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0129] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0130] The above-mentioned embodiments only express the preferred implementation mode of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent monitoring method for Chinese medicinal materials growth information based on air quality, characterized in that: The steps include: Step 1: Obtain the planting monitoring data of each monitoring point of the medicinal material planting base through data acquisition equipment; Step 2: preprocessing the planting monitoring data to obtain planting environment data; Step 3: calculating the growth index of the Chinese medicinal materials in the medicinal material planting base based on the planting environment data; Step 4: extracting the data collection time of the planting monitoring data, determining the growth regulation period of the Chinese medicinal materials in the medicinal material planting base based on the data collection time, and obtaining a preset index range corresponding to the growth regulation period; Step 5: determine whether the growth index meets the preset index range. If not, trigger the early warning mechanism to notify relevant staff to take measures; The step 1 comprises: Step 11, dividing all data acquisition devices into first-category data acquisition devices and second-category data acquisition devices, and setting a first data acquisition frequency for the first-category data acquisition devices and a second data acquisition frequency for the second-category data acquisition devices, wherein the first data acquisition frequency is higher than the second data acquisition frequency; Step 12: periodically obtain monitoring data of the first type of data acquisition equipment and the second type of data acquisition equipment; Step 13: Calculate the estimated monitoring data corresponding to the second type of data acquisition device based on the first monitoring data acquired by the first type of data acquisition device; Step 14, determining whether the monitoring data acquired at the current moment only includes the first monitoring data, if so, storing the first monitoring data and the estimated monitoring data as the planting monitoring data at the current moment, and then returning to step 12, if not, storing the first monitoring data and the second monitoring data collected by the second type of data collection device as the planting monitoring data at the current moment, and then entering step 15; Step 15, calculate the first difference between any of the estimated monitoring data and the second monitoring data collected by the second type of data acquisition device corresponding to any of the estimated monitoring data, and determine whether the first difference is greater than or equal to a first preset value; if not, return to step 12; if so, set the second type of data acquisition device corresponding to any of the estimated monitoring data to the first type of data acquisition device, and set the data acquisition frequency of the second type of data acquisition device corresponding to any of the estimated monitoring data to the first data acquisition frequency, and then return to step 12.

2. The method according to claim 1, characterized in that In step 15, after setting the data collection frequency of the second type of data collection device corresponding to any of the inferred monitoring data to the first data collection frequency, calculate the first value of all newly set first type of data collection devices, and if the first value is greater than or equal to the second preset value, return to step 11.

3. The method according to claim 1, characterized in that In step 11, the step of dividing all data acquisition devices into first-category data acquisition devices and second-category data acquisition devices includes: Step 111, obtaining the third monitoring data collected by all the data collection devices at the first moment and the fourth monitoring data collected at the second moment, wherein the second moment is the moment before the first moment; Step 112: extract any data acquisition device, take the third monitoring data collected by any of the data acquisition devices at the first moment as the dependent variable, take the fourth monitoring data collected by all of the data acquisition devices at the second moment as the independent variable, and construct a first linear equation corresponding to any of the data acquisition devices; Step 113: Fit the first linear equation to determine the weight coefficient corresponding to each independent variable; Step 114, extracting the weight coefficient corresponding to any independent variable and defining it as a first weight coefficient, determining whether the first weight coefficient is greater than or equal to a third preset value, and if so, defining the data acquisition device corresponding to any independent variable as a driving acquisition device of any data acquisition device, and defining any data acquisition device as a driven acquisition device of the data acquisition device corresponding to any independent variable; Step 115: after traversing all the data acquisition devices, extract any of the data acquisition devices, calculate the second value of the driving acquisition device and the third value of the driven acquisition device corresponding to any of the data acquisition devices, and use the ratio of the third value to the second value as the influence value of any of the data acquisition devices; Step 116: after traversing all the data acquisition devices, extract any of the data acquisition devices, and determine whether the influence value corresponding to any of the data acquisition devices is greater than or equal to a fourth preset value; if so, define any of the data acquisition devices as the first type of data acquisition device; if not, define any of the data acquisition devices as the second type of data acquisition device; Step 117, extract any second-category data acquisition device, take the third monitoring data of any second-category data acquisition device at the first moment as the dependent variable, take the third monitoring data and the fourth monitoring data of all first-category data acquisition devices at the first moment and the second moment as independent variables, and construct a second linear equation corresponding to any second-category data acquisition device; Step 118, fitting the second linear equation to determine the second weight coefficient corresponding to each independent variable, then substituting the monitoring data value corresponding to each independent variable and the second weight coefficient into the formula on the right side of the second linear equation to calculate the estimated value of any second type of data acquisition device; Step 119, determine whether the estimated value is less than or equal to the fifth preset value. If so, set the data acquisition frequency of the first type of data acquisition equipment to the first data acquisition frequency, set the data acquisition frequency of the second type of data acquisition equipment to the second data acquisition frequency, and substitute the second weight coefficient corresponding to each independent variable into the second linear equation to generate a formula for calculating the estimated monitoring data. If not, reduce the fourth preset value by a preset value and define it as a new fourth preset value, and then return to step 116.

4. The method according to claim 1, characterized in that: The planting monitoring data includes a first temperature of the air, a second temperature of a preset position of the plant, air humidity, a specific gas content in the air, and specific sunlight parameters, wherein the specific gas is oxygen or carbon dioxide, and the specific sunlight parameters are solar radiation illuminance or solar radiation.

5. The method according to claim 4, characterized in that The step 3 comprises: Based on the growth regulation period, the target value of the air humidity corresponding to the data collection time is obtained, and then the growth index CI is calculated based on a first formula, the first formula being: ， in, , , and is the parameter weight coefficient, T1 is the first temperature, T2 is the second temperature, SC is the air humidity, SCT is the target value, SG is the specific gas content, and SR is the specific sunlight parameter.

6. The method according to claim 1, characterized in that The planting environment data includes the daily average temperature. After step 5, the method further includes periodically calculating the growth period of the Chinese medicinal materials in the medicinal material planting base, including the following steps: Step 61, obtaining the daily average temperature from the planting date to the current date, and calculating the daily first temperature difference value based on the daily average temperature and the first temperature preset value; Step 62: any first temperature difference value is defined as a second temperature difference value when it is a natural number, and all the second temperature difference values ​​are accumulated to obtain a first accumulated value; Step 63, determining whether the first accumulated value is greater than or equal to a sixth preset value, if so, defining the current date as the growth end date, and sending the growth end date to relevant staff, if not, proceeding to step 64; Step 64, predicting the third temperature difference value of the i-th day after the current date, summing the first accumulated value and the third temperature difference to obtain a second accumulated value, and determining whether the second accumulated value is greater than or equal to the sixth preset value, if so, proceeding to step 65, if not, defining the second accumulated value as the first accumulated value, and setting i=i+1, and then returning to step 64, wherein i is a positive integer greater than or equal to 1; Step 65: define the i-th day as the growth end date, and send the growth end date to relevant staff.

7. The method according to claim 6, characterized in that In step 64, predicting the third temperature difference value on the i-th day after the current date includes: Step 641, obtaining the historical daily average temperature of multiple dates that are the same as the i-th day within a preset time before the current date; Step 642, respectively calculating the difference between each of the historical daily average temperatures and the first temperature preset value, which is defined as a fourth temperature difference; Step 643: When any of the fourth temperature differences is a negative number, set any of the fourth temperature differences to 0, calculate an average value of all the fourth temperature differences, and define it as the third temperature difference.

8. An intelligent monitoring system for Chinese medicinal material growth information based on air quality, used to implement the method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, data processing module, data analysis module, message notification module; The data acquisition module is used to acquire the planting monitoring data of each monitoring point in the medicinal material planting base through the data acquisition device; The data processing module is used to pre-process the planting monitoring data to obtain planting environment data; The data analysis module is used to calculate the growth index of the Chinese medicinal materials in the medicinal material planting base according to the planting environment data, extract the data collection time of the planting monitoring data, determine the growth regulation period of the Chinese medicinal materials in the medicinal material planting base based on the data collection time, and obtain the preset index range corresponding to the growth regulation period; The message notification module is used to determine whether the growth index meets the preset index range. If not, an early warning mechanism is triggered to notify relevant personnel to take measures.

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