Solution ion content automatic monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of solution detection, in particular to a solution ion content automatic monitoring system based on Internet of Things, comprising: a camera module used for collecting image data of a plant cultivated in a plant factory; the selection module is used for traversing the plant image data acquired in the camera module, and selecting a local area in the plant image data as a monitoring target area of the system operation; the sampling module is used for receiving the monitoring target area selected in the selection module and sampling a solution for cultivating plants in the monitoring target area; according to the invention, through image acquisition of the cultivated plant and analysis of the growth condition of the cultivated plant, two positions with the best growth state and the worst growth state are selected as nutrient solution sampling positions for nutrient solution sampling, and the health state of the nutrient solution is further analyzed based on the set nutrient solution monitoring state analysis logic. And monitoring the safety of the nutrient solution used for cultivating the plant according to the continuously analyzed health state of the nutrient solution.

Description

Technical Field

[0001] The present invention relates to the technical field of solution detection, and in particular to an automatic monitoring system for solution ion content based on the Internet of Things. Background Art

[0002] The plant factory is an innovative achievement of modern agriculture and a highly controllable agricultural production model. In a completely closed environment, it provides the best growth environment for plants by artificially precisely controlling various growth conditions such as light, temperature, humidity, and nutrition. Nutrient solution cultivation is a type of cultivation technology used in plant factories to cultivate plants.

[0003] The invention patent with application number 201710099997.4 discloses a method for detecting the composition of plant hydroponic nutrient solution, which is characterized in that it includes the following steps: Step S10 initial data entry and detection: the ion detection module detects the ion concentration of the hydroponic nutrient solution through a sensor and defines the ion concentration data as the initial concentration; Step S20 nutritional analysis: the biological species analysis module calls the data in the tissue culture management module, and analyzes the impact of the current plant species on the nutrient composition of the nutrient solution in the corresponding hydroponic stage according to the hydroponic stage and plant species combined with the data in the local database; Step S30 detects the real-time concentration of ions: the ion detection module continuously detects the ion concentration through the sensor Detect the ion concentration of the hydroponic nutrient solution, which is defined as the real-time ion concentration; Step S40 nutrient solution nutrient change trend calculation: the nutrient analysis module calls the nutrient solution formula ion concentration data, and calculates the real-time standard ion concentration according to the analysis result of step S20; Step S50 ion component comparison: the central processing unit compares the real-time ion concentration obtained in step S30 with the real-time standard ion concentration analyzed in step S40, and issues a reminder to replace the nutrient solution when the comparison results are inconsistent; Step S60 nutrient component monitoring: the central processing unit monitors the real-time ion concentration obtained in step S30, and issues a reminder to replace the nutrient solution when the real-time ion concentration is lower than the set alarm value.

[0004] The application aims to solve the problem that "however, due to the lack of a method to detect the nutrient content of the nutrient solution in current tissue culture, the nutrient solution of the plants is replaced at a fixed time. This practice causes some plants with higher nutrient consumption to face an environment of insufficient nutrition, while plants with slower nutrient consumption will cause waste."

[0005] However, for large-scale nutrient solution-cultivated plants, there are differences in plant growth, and the absorption capacity of nutrient solution cannot be balanced. Due to the large area of ​​nutrient solution-cultivated plants, it is impossible to accurately monitor the composition of the nutrient solution, and thus it is impossible to base it on the best growth environment for the plants.

[0006] To this end, we proposed an automatic monitoring system for solution ion content based on the Internet of Things. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an automatic monitoring system for solution ion content based on the Internet of Things, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An automatic monitoring system for solution ion content based on the Internet of Things, comprising:

[0010] The camera module is used to collect image data of plants cultivated in the plant factory; the selection module is used to traverse the plant image data collected in the camera module, and select a local area in the plant image data as the monitoring target area for this system operation; the sampling module is used to receive the monitoring target area selected in the selection module, and sample the solution for cultivating plants in the monitoring target area; the perception module is used to receive the sampled solution in the sampling module, and synchronously perceive the state parameters of each group of solutions; the analysis module is used to record in real time the state parameters of the sampled solution perceived by the perception module during operation, and analyze the health status of the nutrient solution based on the state parameters of the sampled solution; the monitoring module is used to set the health judgment threshold of the nutrient solution state, continuously receive the health status of the nutrient solution analyzed in the analysis module, and monitor the safety of the nutrient solution based on the health judgment threshold of the nutrient solution state and the analysis results of the nutrient solution health state;

[0011] The camera module is interactively connected to a segmentation unit via a wireless network, the camera module and the segmentation unit are interactively connected to a selection module via a wireless network, the selection module is interactively connected to a sampling module via a wireless network, the sampling module is electrically connected to a robotic arm module and a sampling pump at a lower level via a medium, and the sampling module is interactively connected to a perception module, an analysis module and a monitoring module via a wireless network.

[0012] Furthermore, the camera module is deployed directly above the cultivated plants in the plant factory, and the camera end of the camera module is vertically downward, and the camera module has an operation cycle defined by the system end user, and the camera module continuously runs to collect plant image data based on the operation cycle;

[0013] Among them, the plants cultivated in the plant factory are cultivated with nutrient solution. The plants arranged in the plant factory are distributed in a matrix shape, and the cultivated plants in the matrix shape are of the same variety and nutrient solution.

[0014] Furthermore, the image edge in the plant image data collected by the camera module coincides with the boundary of the cultivated plants distributed in a matrix shape, and the camera module is internally provided with submodules, including:

[0015] A segmentation unit, used for receiving plant image data collected by the camera module and performing segmentation processing on the plant image data;

[0016] Among them, the plant image data segmentation logic is set in the segmentation unit, and the segmentation unit segments the plant image data based on the plant image data segmentation logic. Each image block obtained by segmentation is recorded as sub-plant image data. The number of each sub-plant image data is not less than 2×2 groups, and the size and aspect ratio of each sub-plant image data are equal.

[0017] Furthermore, the plant image data segmentation logic set in the processing unit is expressed as:

[0018]

[0019] Where: m is the total amount of sub-plant image data after segmentation; q is the image segmentation cardinality; f(i, j) is the frequency of the feature binary (i, j); n is the scale of the image; λ is the correction;

[0020] Among them, the correction λ≥1, and obey The larger the value, the larger the modified λ value. Conversely, the smaller the modified λ value, the setting logic, the value of the image segmentation cardinality q is 2×2 groups, and the total amount of sub-plant image data m after segmentation is rounded up, and The value of is a positive integer, i represents the gray value of the pixel, and j represents the gray mean of the field.

[0021] Furthermore, in the selection module, the selection operation of the local area in the plant image data, that is, the selection operation of the sub-plant image data, during the operation phase of the selection module, the information entropy of each sub-plant image data is calculated, and two groups of sub-plant image data are further selected, and the two groups of sub-plant image data selected are the sub-plant image data with the largest and smallest information entropy;

[0022] Among them, the information entropy calculation logic of sub-plant image data is:

[0023] Furthermore, the sampling module is provided with submodules at the lower level, including:

[0024] The robotic arm module is used to carry the sampling pump and move above the cultivated plants distributed in a matrix shape, reach the center of the cultivated plant area corresponding to the monitored target area, and vertically push the sampling pump into the nutrient solution of the cultivated plants;

[0025] A sampling pump is used to collect nutrient solution subsamples three times in succession at the center of the plant cultivation area corresponding to each monitoring target area, with its pump suction end extended into the nutrient solution;

[0026] Among them, the robotic arm module pushes the sampling pump into the nutrient solution for growing plants, so that the sampling pump reaches the bottom of the nutrient solution at a uniform speed. Each time the sampling pump penetrates into the nutrient solution to one-third of the depth of the nutrient solution, a nutrient solution sub-sample is collected. Three groups of nutrient solution sub-samples collected at the center position of the plant cultivation area corresponding to the same monitoring target area are mixed and recorded as a group of nutrient solution samples to be tested.

[0027] Furthermore, the sensing module is integrated with a conductivity sensor and a microelectrode array sensor, and the sensing module operates to receive the sampling solution in the sampling module, that is, the nutrient solution sample to be tested;

[0028] The state parameters of the nutrient solution sample to be tested sensed by the sensing module include: ion content, ammonia ion content, phosphorus ion content, potassium ion content, chloride ion content, and nitrite ion content in the nutrient solution.

[0029] Furthermore, the health status analysis logic of the nutrient solution in the analysis module is expressed as:

[0030]

[0031] Where: F is the health status performance value of the nutrient solution presented by a set of state parameters of the nutrient solution samples to be tested; ρ NH , HPO , K , CL , NO is the content of ammonium ion, phosphorus ion, potassium ion, chloride ion and nitrite ion in the nutrient solution; ρ all is the ion content in the nutrient solution; γ is a constant; F is the average health status of the nutrient solution; MAX 、F MIN The state parameters of the nutrient solution samples to be tested are collected for the plant cultivation area corresponding to the sub-plant image data with the maximum information entropy and the minimum information entropy, and the nutrient solution health state performance value is obtained based on formula (1);

[0032] Among them, the average health status performance value of the nutrient solution The larger the value is, the better the state of the nutrient solution is. Conversely, the smaller the value is, the worse the state of the nutrient solution is. The constant γ is defined by the system user, and the constant γ>0.

[0033] Furthermore, the nutrient solution monitoring state determination threshold value set in the monitoring module is [a, b];

[0034] The monitoring logic of nutrient solution safety in the monitoring module is expressed as follows:

[0035]

[0036] Where: is the health average performance value of the three groups of nutrient solutions obtained based on the latest time series; χ is the attenuation factor;

[0037] in, for For any group in , if the attenuation factor χ∈(0,1), equation (1) and equation (2) hold, it means that the nutrient solution is safe, otherwise, it means that the nutrient solution is unsafe.

[0038] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0039] The present invention provides an automatic monitoring system for solution ion content based on the Internet of Things. During operation, the system analyzes the growth conditions of cultivated plants by collecting images of cultivated plants, thereby selecting two positions with the best and worst growth conditions as nutrient solution sampling positions, sampling the nutrient solution, further analyzing the health status of the nutrient solution based on a set nutrient solution monitoring status analysis logic, and then monitoring the safety of the nutrient solution used for cultivating plants with the continuously analyzed nutrient solution health status, thereby providing system-end users with an online, intelligent and efficient real-time health reference for the nutrient solution, and facilitating system-end users to manage the status of the nutrient solution used for cultivating plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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 prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is a structural schematic diagram of an automatic monitoring system for solution ion content based on the Internet of Things. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0043] The present invention will be further described below in conjunction with the embodiments.

[0044] Embodiment 1:

[0045] The present embodiment is a solution ion content automatic monitoring system based on the Internet of Things, such as Figure 1 As shown, including:

[0046] Camera module, used to collect image data of plants grown in plant factories;

[0047] The camera module is internally provided with submodules, including:

[0048] A segmentation unit, used for receiving plant image data collected by the camera module and performing segmentation processing on the plant image data;

[0049] A selection module is used to traverse the plant image data collected by the camera module and select a local area in the plant image data as the monitoring target area for this system operation;

[0050] A sampling module, used for receiving the monitoring target area selected in the selection module, and sampling the solution for cultivating plants in the monitoring target area;

[0051] The sampling module is provided with submodules at the lower level, including:

[0052] The robotic arm module is used to carry the sampling pump and move above the cultivated plants distributed in a matrix shape, reach the center of the cultivated plant area corresponding to the monitored target area, and vertically push the sampling pump into the nutrient solution of the cultivated plants;

[0053] A sampling pump is used to collect nutrient solution subsamples three times in succession at the center of the plant cultivation area corresponding to each monitoring target area, with its pump suction end extended into the nutrient solution;

[0054] Among them, the robotic arm module pushes the sampling pump into the nutrient solution for cultivating plants, so that the sampling pump reaches the bottom of the nutrient solution at a uniform speed. Each time the sampling pump is inserted into the nutrient solution to a depth of one-third of the nutrient solution, a nutrient solution sub-sample is collected. Three groups of nutrient solution sub-samples collected at the center of the plant cultivation area corresponding to the same monitoring target area are mixed and recorded as a group of nutrient solution samples to be tested;

[0055] The sensing module is used to receive the sampled solution from the sampling module and synchronously sense the state parameters of each group of solutions;

[0056] An analysis module is used to record in real time the state parameters of the sampling solution sensed by the sensing module, and analyze the health status of the nutrient solution based on the state parameters of the sampling solution;

[0057] The health status analysis logic of the nutrient solution in the analysis module is expressed as:

[0058]

[0059] Where: F is the health status performance value of the nutrient solution presented by a set of state parameters of the nutrient solution samples to be tested; ρ NH , HPO , K , CL , NO is the content of ammonium ion, phosphorus ion, potassium ion, chloride ion and nitrite ion in the nutrient solution; ρ all is the ion content in the nutrient solution; γ is a constant; F is the average health status of the nutrient solution; MAX 、F MIN The state parameters of the nutrient solution samples to be tested are collected for the plant cultivation area corresponding to the sub-plant image data with the maximum information entropy and the minimum information entropy, and the nutrient solution health state performance value is obtained based on formula (1);

[0060] Among them, the average health status performance value of the nutrient solution The larger the value is, the better the nutrient solution condition is. Conversely, the smaller the value is, the worse the nutrient solution condition is. The constant γ is defined by the system user and the constant γ>0.

[0061] The monitoring module is used to set the health determination threshold of the nutrient solution status, continuously receive the health status of the nutrient solution analyzed in the analysis module, and monitor the safety of the nutrient solution based on the health determination threshold of the nutrient solution status and the analysis result of the health status of the nutrient solution;

[0062] The nutrient solution monitoring status determination threshold set in the monitoring module is [a, b];

[0063] The monitoring logic of nutrient solution safety in the monitoring module is expressed as:

[0064]

[0065] Where: is the health average performance value of the three groups of nutrient solutions obtained based on the latest time series; χ is the attenuation factor;

[0066] in, for For any group, if the attenuation factor χ∈(0,1), equation (1) and equation (2) hold, it means that the nutrient solution is safe, otherwise, it means that the nutrient solution is unsafe;

[0067] The camera module is interactively connected to a segmentation unit via a wireless network, the camera module and the segmentation unit are interactively connected to a selection module via a wireless network, the selection module is interactively connected to a sampling module via a wireless network, the sampling module is electrically connected to a robotic arm module and a sampling pump at the lower level via a medium, and the sampling module is interactively connected to a perception module, an analysis module and a monitoring module via a wireless network.

[0068] In this embodiment, the camera module operates to collect image data of plants cultivated in the plant factory, the segmentation unit synchronously receives the plant image data collected in the camera module, and performs segmentation processing on the plant image data, the selection module post-operates to traverse the plant image data collected in the camera module, and selects a local area in the plant image data as the monitoring target area for this system operation, the sampling module further receives the monitoring target area selected in the selection module, samples the solution for cultivating plants in the monitoring target area, the robotic arm module synchronously carries the sampling pump and moves above the cultivated plants distributed in a matrix shape, reaches the center position of the cultivated plant area corresponding to the monitoring target area, and vertically pushes the sampling pump into the cultivated plant area. In the nutrient solution of the plants, the sampling pump is in real time at the center of the plant cultivation area corresponding to each monitoring target area, and continuously performs three nutrient solution sub-sample collections with its own pump suction end inserted into the nutrient solution. The perception module further receives the sampled solution in the sampling module, and synchronously senses the state parameters of each group of solutions. The analysis module then records in real time the state parameters of the sampled solution sensed by the perception module, and analyzes the health status of the nutrient solution based on the state parameters of the sampled solution. Finally, the nutrient solution health judgment threshold is set through the monitoring module, and the nutrient solution health status analyzed in the analysis module is continuously received. The nutrient solution safety is monitored based on the nutrient solution health judgment threshold and the nutrient solution health status analysis results.

[0069] Through the operation of the system in the above embodiment, a nutrient solution nutrient component monitoring and management service is provided for plants cultivated in plant factories using nutrient solution, ensuring that the nutrient solution contacted by the cultivated plants remains in a healthier state for better results, continuously providing sufficient nutrients for the cultivated plants, and providing guarantees for the growth of the cultivated plants.

[0070] Embodiment 2:

[0071] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic monitoring system for solution ion content based on the Internet of Things in Example 1 is further described in detail:

[0072] The camera module is deployed directly above the cultivated plants in the plant factory, with the camera end of the camera module facing vertically downward. The camera module has an operation cycle defined by the system end user. The camera module continuously runs to collect plant image data based on the operation cycle.

[0073] Among them, the plants cultivated in the plant factory are cultivated with nutrient solution. The plants configured in the plant factory are arranged in a matrix shape, and the cultivated plants in the matrix shape are of the same variety and nutrient solution;

[0074] The image edge in the plant image data collected by the camera module coincides with the boundary of the cultivated plants distributed in a matrix shape;

[0075] The segmentation unit sets a plant image data segmentation logic, and the segmentation unit segments the plant image data based on the plant image data segmentation logic. Each image block obtained by segmentation is recorded as sub-plant image data. The number of each sub-plant image data is not less than 2×2 groups, and the size and aspect ratio of each sub-plant image data are equal.

[0076] The plant image data segmentation logic set in the processing unit is expressed as:

[0077]

[0078] Where: m is the total amount of sub-plant image data after segmentation; q is the image segmentation cardinality; f(i, j) is the frequency of the feature binary (i, j); n is the scale of the image; λ is the correction;

[0079] Among them, the correction λ≥1, and obey The larger the value, the larger the modified λ value. Conversely, the smaller the modified λ value, the setting logic, the value of the image segmentation cardinality q is 2×2 groups, and the total amount of sub-plant image data m after segmentation is rounded up, and The value of is a positive integer, i represents the gray value of the pixel, and j represents the gray mean of the field.

[0080] In this embodiment, through the above-mentioned settings, further operation logic support is provided for the operation of the system in Example 1, and the above-mentioned plant image data segmentation logic formula is configured, so that when sampling the nutrient solution sample to be tested, the sampling position has the conditions for precision control, so that the system can serve nutrient solution plant cultivation scenes of different scales and improve the system adaptability.

[0081] like Figure 1 As shown, the selection operation of the local area in the plant image data in the selection module, that is, the selection operation of the sub-plant image data, during the operation stage of the selection module, the information entropy of each sub-plant image data is calculated, and two groups of sub-plant image data are further selected, and the two groups of sub-plant image data selected are the sub-plant image data with the largest and smallest information entropy;

[0082] Among them, the information entropy calculation logic of sub-plant image data is:

[0083] Through the above settings, the sampling position of the nutrient solution sample to be tested is further limited to ensure that the nutrient solution sample to be tested is sampled at the designated position.

[0084] like Figure 1 As shown, the sensing module is integrated with a conductivity sensor and a microelectrode array sensor, and the sensing module operates the sampling solution received in the sampling module, that is, the nutrient solution sample to be tested;

[0085] The state parameters of the nutrient solution sample to be tested sensed by the sensing module include: ion content, ammonia ion content, phosphorus ion content, potassium ion content, chloride ion content, and nitrite ion content in the nutrient solution.

[0086] Through the above settings, the state parameters of the nutrient solution sample to be tested are further limited.

[0087] In summary, during operation, the system in the above embodiment analyzes the growth conditions of the cultivated plants by collecting images of the cultivated plants, thereby selecting the two locations with the best and worst growth conditions as nutrient solution sampling locations, and sampling the nutrient solution. The health status of the nutrient solution is further analyzed based on the set nutrient solution monitoring status analysis logic, and the safety of the nutrient solution used for cultivating plants is monitored by continuously analyzing the health status of the nutrient solution, thereby providing system-end users with an online, intelligent and efficient real-time health reference for the nutrient solution, which helps system-end users to manage the status of the nutrient solution used for cultivating plants.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automatic monitoring system for solution ion content based on the Internet of Things, characterized in that: include: Camera module, used to collect image data of plants grown in plant factories; A selection module is used to traverse the plant image data collected by the camera module and select a local area in the plant image data as the monitoring target area for this system operation; A sampling module, used for receiving the monitoring target area selected in the selection module, and sampling the solution for cultivating plants in the monitoring target area; The sensing module is used to receive the sampled solution from the sampling module and synchronously sense the state parameters of each group of solutions; An analysis module is used to record in real time the state parameters of the sampling solution sensed by the sensing module, and analyze the health status of the nutrient solution based on the state parameters of the sampling solution; The monitoring module is used to set the health judgment threshold of the nutrient solution status, continuously receive the health status of the nutrient solution analyzed in the analysis module, and monitor the safety of the nutrient solution based on the health judgment threshold of the nutrient solution status and the analysis results of the nutrient solution health status.

2. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1 is characterized in that: The camera module is deployed directly above the cultivated plants in the plant factory, and the camera end of the camera module is vertically downward, and the camera module has an operation cycle defined by a system end user, and the camera module continuously runs to collect plant image data based on the operation cycle; Among them, the plants cultivated in the plant factory are cultivated with nutrient solution. The plants arranged in the plant factory are distributed in a matrix shape, and the cultivated plants in the matrix shape are of the same variety and nutrient solution.

3. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1 is characterized in that: The image edge in the plant image data collected by the camera module coincides with the boundary of the cultivated plants distributed in a matrix shape. The camera module is internally provided with submodules, including: A segmentation unit, used for receiving plant image data collected by the camera module and performing segmentation processing on the plant image data; Among them, the plant image data segmentation logic is set in the segmentation unit, and the segmentation unit segments the plant image data based on the plant image data segmentation logic. Each image block obtained by segmentation is recorded as sub-plant image data. The number of each sub-plant image data is not less than 2×2 groups, and the size and aspect ratio of each sub-plant image data are equal.

4. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 3 is characterized in that: The plant image data segmentation logic set in the processing unit is expressed as: Where: m is the total amount of sub-plant image data after segmentation; q is the image segmentation cardinality; f(i,j) is the frequency of occurrence of feature binary group (i,j); n is the scale of the image; λ is the correction; Among them, the correction λ≥1, and obey The larger the value, the larger the modified λ value. Conversely, the smaller the modified λ value, the setting logic, the value of the image segmentation cardinality q is 2×2 groups, and the total amount of sub-plant image data m after segmentation is rounded up, and The value of is a positive integer, i represents the gray value of the pixel, and j represents the gray mean of the field.

5. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1 is characterized in that: The selection module performs a selection operation on a local area in the plant image data, i.e., a selection operation on the sub-plant image data. During the operation phase of the selection module, the information entropy of each sub-plant image data is calculated, and two groups of sub-plant image data are further selected, and the two groups of sub-plant image data selected are the sub-plant image data with the largest and smallest information entropy; Among them, the information entropy calculation logic of sub-plant image data is:

6. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1, characterized in that: The sampling module is provided with submodules at the lower level, including: The robotic arm module is used to carry the sampling pump and move above the cultivated plants distributed in a matrix shape, reach the center of the cultivated plant area corresponding to the monitored target area, and vertically push the sampling pump into the nutrient solution of the cultivated plants; A sampling pump is used to collect nutrient solution subsamples three times in succession at the center of the plant cultivation area corresponding to each monitoring target area, with its pump suction end extended into the nutrient solution; Among them, the robotic arm module pushes the sampling pump into the nutrient solution for growing plants, so that the sampling pump reaches the bottom of the nutrient solution at a uniform speed. Each time the sampling pump penetrates into the nutrient solution to one-third of the depth of the nutrient solution, a nutrient solution sub-sample is collected. Three groups of nutrient solution sub-samples collected at the center position of the plant cultivation area corresponding to the same monitoring target area are mixed and recorded as a group of nutrient solution samples to be tested.

7. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1 is characterized in that: The sensing module is integrated with a conductivity sensor and a microelectrode array sensor, and the sensing module operates to receive the sample solution in the sampling module, that is, the nutrient solution sample to be tested; The state parameters of the nutrient solution sample to be tested sensed by the sensing module include: ion content, ammonia ion content, phosphorus ion content, potassium ion content, chloride ion content, and nitrite ion content in the nutrient solution.

8. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1, characterized in that: The health status analysis logic of the nutrient solution in the analysis module is expressed as: Where: F is the health status performance value of the nutrient solution presented by a set of state parameters of the nutrient solution samples to be tested; ρ NH , HPO , K , CL , NO is the content of ammonia ion, phosphorus ion, potassium ion, chloride ion and nitrite ion in the nutrient solution; ρ all is the ion content in the nutrient solution; γ is a constant; F is the average health status of the nutrient solution; MAX 、F MIN The state parameters of the nutrient solution samples to be tested are collected for the plant cultivation area corresponding to the sub-plant image data with the maximum information entropy and the minimum information entropy, and the nutrient solution health state performance value is obtained based on formula (1); Among them, the average health status performance value of the nutrient solution The larger the value is, the better the state of the nutrient solution is. Conversely, the smaller the value is, the worse the state of the nutrient solution is. The constant γ is defined by the system user and the constant γ>

0.

9. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1, characterized in that: The nutrient solution monitoring state determination threshold value set in the monitoring module is [a, b]; The monitoring logic of nutrient solution safety in the monitoring module is expressed as: Where: is the health average performance value of the three groups of nutrient solutions obtained based on the latest time series; χ is the attenuation factor; in, for For any group in , if the attenuation factor χ∈(0,1), equation (1) and equation (2) hold, it means that the nutrient solution is safe, otherwise, it means that the nutrient solution is unsafe.

10. The automatic monitoring system for solution ion content based on the Internet of Things according to claim 1, characterized in that: The camera module is interactively connected to a segmentation unit via a wireless network, the camera module and the segmentation unit are interactively connected to a selection module via a wireless network, the selection module is interactively connected to a sampling module via a wireless network, the sampling module is electrically connected to a robotic arm module and a sampling pump at a lower level via a medium, and the sampling module is interactively connected to a perception module, an analysis module and a monitoring module via a wireless network.

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