Intelligent control agricultural ditch water purification and recycling system

Abstract

The invention discloses an intelligent control agricultural ditch water purification and recycling system, which relates to the technical field of sewage purification and comprises a water inlet, a primary filtering unit, a biological purification unit, an advanced treatment unit, a water quality monitoring unit, an intelligent control unit and a water outlet, the water inlet is firstly connected with the primary filtering unit, and the primary filtering unit is used for primarily filtering ditch water; the water outlet end of the primary filtering unit is connected with a biological purification unit; the biological purification unit is used for carrying out biological purification treatment on water subjected to primary filtering; the water outlet end of the biological purification unit is connected with the deep treatment unit; a water quality monitoring unit is arranged in each of the primary filtering unit, the biological purification unit and the deep treatment unit; the water quality monitoring unit is connected with the intelligent control unit and transmits monitored water quality data to the intelligent control unit; the water outlet end of the advanced treatment unit is connected with a water outlet, and the purified water is conveyed into an irrigation system by the water outlet, so that the recycling of water resources is realized.

Description

Technical Field

[0001] The invention relates to the technical field of sewage purification, and in particular to an intelligent control agricultural ditch water purification and reuse system. Background Art

[0002] In the agricultural production process, irrigation is an important link to ensure the growth of crops. In farmland, there are numerous irrigation ditches. The water in these ditches often contains a large amount of impurities after irrigation, such as soil particles, residual pesticides and fertilizers, and crop residues. With the development of agriculture, agricultural non-point source pollution is becoming increasingly serious. If these pollutants are not treated and discharged into the surrounding environment, they will cause great harm to the soil, water and ecosystem.

[0003] On the one hand, a large amount of pesticides and fertilizers flow into the ditches with irrigation water. These chemicals will change the chemical properties of the soil, causing soil compaction and reduced fertility. For example, excessive nitrogen fertilizers will acidify the soil, and the phosphorus in phosphate fertilizers will easily combine with calcium, iron, aluminum and other elements in the soil to form precipitation that is difficult for plants to absorb. Moreover, after these chemicals flow into rivers, lakes and other water bodies with ditch water, they will cause eutrophication of the water bodies, leading to the massive reproduction of algae and destroying the ecological balance of the water bodies. The algal bloom phenomenon that occurs in some lakes is a typical manifestation of eutrophication of water bodies. The massive growth of algae will consume oxygen in the water, causing fish and other aquatic organisms in the water to die due to lack of oxygen.

[0004] On the other hand, soil particles flow into the ditches along with the irrigation water, causing the ditches to silt up. According to statistics, the amount of soil that flows into the irrigation ditches due to soil erosion can reach several tons each year. These silted soils will reduce the water storage and water delivery capacity of the ditches, affecting the irrigation efficiency. Moreover, as time goes by, a lot of manpower and material resources are required to desilt the ditches.

[0005] In addition, crop residues rot and decompose in ditches, producing some harmful substances and breeding bacteria and viruses. These harmful substances and pathogens may spread with the water flow, causing harm to surrounding crops and leading to the occurrence and spread of crop diseases and insect pests.

[0006] Existing agricultural ditch water purification technologies have many shortcomings. Traditional physical filtration methods, such as sand filtration and screen filtration, can remove some larger particles of impurities, but are not effective in removing soluble pollutants such as pesticides and fertilizers. Chemical treatment methods often require a large amount of chemical agents, which is costly and may cause secondary pollution. Although biological treatment methods have certain advantages, they have problems such as unstable treatment efficiency and high requirements for environmental conditions.

[0007] At present, the application of intelligent technology in agriculture is becoming more and more extensive, but there is still a lack of intelligent systems for the purification and reuse of agricultural ditch water. Some existing simple automation equipment cannot accurately control and optimize operations according to factors such as real-time changes in ditch water quality and irrigation needs, making it difficult to achieve efficient purification and reuse.

[0008] In summary, it is of great practical significance to develop a system that can effectively purify agricultural ditch water, realize water resource reuse, and can be intelligently controlled. Summary of the invention

[0009] The purpose of the present invention is to provide an intelligent control agricultural ditch water purification and reuse system to solve the problem that the existing automation equipment in the prior art cannot accurately control and optimize operations according to factors such as real-time changes in ditch water quality and irrigation needs, making it difficult to achieve efficient purification and reuse.

[0010] To achieve the above-mentioned purpose, the present invention provides an intelligent control agricultural ditch water purification and reuse system, comprising a water inlet, a primary filtration unit, a biological purification unit, a deep treatment unit, a water quality monitoring unit, an intelligent control unit and a water outlet;

[0011] The water inlet is first connected to the primary filter unit, which performs preliminary filtration on the ditch water flowing into the water inlet;

[0012] The water outlet of the primary filtration unit is connected to the biological purification unit, and the biological purification unit performs biological purification treatment on the water after the primary filtration;

[0013] The water outlet of the biological purification unit is connected to the deep treatment unit, which further improves the water quality;

[0014] Water quality monitoring units are provided in the primary filtration unit, biological purification unit, and deep treatment unit to monitor the water quality parameters at each stage in real time;

[0015] The water quality monitoring unit is connected to the intelligent control unit and transmits the monitored water quality data to the intelligent control unit. The intelligent control unit controls the operating parameters of the primary filtration unit, the biological purification unit and the deep treatment unit according to these data. The corresponding operating parameters of the primary filtration unit, the biological purification unit and the deep treatment unit are: filter cleaning frequency, aeration intensity and membrane flux.

[0016] The water outlet of the deep treatment unit is connected to the water outlet, and the water outlet transports the purified water to the irrigation system to realize the reuse of water resources.

[0017] Preferably, the primary filter unit adopts a multi-layer filter structure, including a coarse filter and a fine filter; the coarse filter has a mesh diameter of 5-10 mm to intercept large crop residues and soil particles; the fine filter has a mesh diameter of 0.5-1 mm to further filter small impurities.

[0018] Preferably, the biological purification unit comprises a plurality of biological filters, which are filled with biological fillers, including expanded clay and activated carbon fibers; and a variety of microorganisms are inoculated in the biological filters to decompose organic pollutants in the ditch water, including residual pesticides, fertilizers and organic matter produced by decomposition of crop residues.

[0019] Preferably, the deep treatment unit adopts membrane treatment technology, including ultrafiltration membrane and reverse osmosis membrane; ultrafiltration membrane removes large molecular organic matter and colloids in water; reverse osmosis membrane removes soluble salts and small molecular organic matter (such as bacteria, viruses, pyrogens, colloids, organic matter, heavy metals, pesticides) in water.

[0020] Preferably, the water quality monitoring unit includes a pH sensor, a dissolved oxygen sensor, a chemical oxygen demand COD sensor and an ammonia nitrogen sensor; and the water quality parameters are monitored in real time by setting the pH sensor, the dissolved oxygen sensor, the chemical oxygen demand COD sensor and the ammonia nitrogen sensor at different processing stages of the system.

[0021] Preferably, the intelligent control unit uses a fuzzy logic algorithm to regulate the filter cleaning frequency of the primary filtration unit, the aeration intensity of the biological purification unit, and the membrane flux of the deep treatment unit according to the data transmitted by the water quality monitoring unit, as well as the preset water quality standards and irrigation needs.

[0022] Preferably, the process of using fuzzy logic algorithm to control the filter cleaning frequency of the primary filter unit is as follows:

[0023] S11, obtaining input data; the intelligent control unit obtains water quality data related to the filter cleaning frequency from the water quality monitoring unit, including particle concentration x and turbidity y;

[0024] S12, define fuzzy sets of particle concentration x and turbidity y, and calculate their membership degrees to each fuzzy set respectively;

[0025] S13, determine the fuzzy rules, the form of the fuzzy rules is: if the particle concentration is "A" and the turbidity is "B", the filter cleaning frequency is "F", there are n fuzzy rules, where A and B are fuzzy sets of particle concentration x and turbidity y respectively, and F is the filter cleaning frequency;

[0026] S14. Calculate the activation strength ω of each rule i , the expression is as follows:

[0027] ω i=μ x-对应模糊集 (x)×μ y-对应模糊集 (y);

[0028] In the formula, μ x-对应模糊集 (x) represents the membership function of the fuzzy set corresponding to the particle concentration x; μ y-对应模糊集 (y) represents the membership function of the fuzzy set corresponding to turbidity y;

[0029] S15. Determine the membership of the output fuzzy set; assume that the filter cleaning frequency F has m fuzzy sets F1, F2, …, F m , for each fuzzy set F j , calculate its total membership, the calculation expression is as follows:

[0030]

[0031] In the formula, In the i-th fuzzy rule, when the output is F i When F i For the fuzzy set F j The degree of membership;

[0032] S16, use the centroid method to defuzzify the method to obtain the accurate filter cleaning frequency f exact , the calculation expression is as follows:

[0033]

[0034] In the formula, f j is the fuzzy set F of filter cleaning frequency F j The corresponding value;

[0035] S17. Regulating the filter cleaning frequency of the primary filter unit according to the precise filter cleaning frequency.

[0036] Preferably, the process of using fuzzy logic algorithm to control the aeration intensity of the biological purification unit is as follows:

[0037] S21, obtaining input data; the intelligent control unit obtains water quality data related to the biological purification unit from the water quality monitoring unit, including chemical oxygen demand a and dissolved oxygen concentration b;

[0038] S22, define the fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b, and calculate the membership degree of each fuzzy set respectively;

[0039] S23, determine the fuzzy rules, the form of the fuzzy rules is: chemical oxygen demand is "C" and dissolved oxygen concentration is "D", then the aeration intensity is "V", there are p fuzzy rules, where C and D are fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b, respectively, and V is the aeration intensity;

[0040] S24. Calculate the activation strength ω of each rule 生i , the expression is as follows:

[0041] ω 生i =μ a-对应模糊集 (a)×μ b-对应模糊集 (b);

[0042] In the formula, μ a-对应模糊集 (a) represents the membership function of the fuzzy set corresponding to the chemical oxygen demand a; μ b-对应模糊集 (b) represents the membership function of the fuzzy set corresponding to the dissolved oxygen concentration b;

[0043] S25. Determine the membership of the output fuzzy set; assume that the aeration intensity V has q fuzzy sets V1, V2, …, V q , for each fuzzy set V j , calculate its total membership, the calculation expression is as follows:

[0044]

[0045] In the formula, In the ith rule, when the output is V i When V i For the fuzzy set V j The degree of membership;

[0046] S26, use the centroid method to defuzzify the method to obtain the accurate aeration intensity v exact , the calculation expression is as follows:

[0047]

[0048] In the formula, v j is the fuzzy set V of aeration intensity V j The corresponding value;

[0049] S27. The aeration intensity of the biological purification unit is regulated according to the precise aeration intensity.

[0050] Preferably, the process of using fuzzy logic algorithm to control the membrane flux of the deep processing unit is as follows:

[0051] S31, obtaining input data; the intelligent control unit obtains water quality data related to the membrane flux from the water quality monitoring unit, including ion concentration c and microbial content d;

[0052] S32, defining fuzzy sets of ion concentration c and microbial content d, and calculating the membership degree of each fuzzy set respectively;

[0053] S33, determine the fuzzy rules, the form of the fuzzy rules is: if the ion concentration is "P" and the microbial content is "Q", then the membrane flux is "M", there are s fuzzy rules, where P and Q are fuzzy sets of ion concentration c and microbial content d, respectively, and M is the membrane flux;

[0054] S34. Calculate the activation strength ω of each rule 膜i , the expression is as follows:

[0055] ω 膜i =μ c-对应模糊集 (c)×μ d-对应模糊集 (d);

[0056] In the formula, μ c-对应模糊集 (c) represents the membership function of the fuzzy set corresponding to the ion concentration c; μ d-对应模糊集 (d) represents the membership function of the fuzzy set corresponding to the microbial content d;

[0057] S35, determine the membership of the output fuzzy set; assume that the membrane flux M has s fuzzy sets M1, M2, ..., M s , for each fuzzy set M j , calculate its total membership, the calculation expression is as follows:

[0058]

[0059] In the formula, In the i-th rule, when the output is M i When M i For the fuzzy set M j The degree of membership;

[0060] S36. The centroid method is used to defuzzify the membrane flux to obtain the accurate membrane flux. The calculation expression is as follows:

[0061]

[0062] In the formula, m j is the fuzzy set M of membrane flux M j The corresponding value;

[0063] S37. Regulating the membrane flux of the deep processing unit according to the precise membrane flux.

[0064] Preferably, the shape of the membership function is selected according to actual conditions, and is one of a triangular membership function, a trapezoidal membership function, and a Gaussian membership function.

[0065] Therefore, the present invention adopts the above-mentioned intelligent control agricultural ditch water purification and reuse system, which has the following beneficial effects:

[0066] (1) This system can effectively purify agricultural ditch water, converting the originally polluted water that cannot be directly used for irrigation into water resources that meet irrigation requirements, reducing dependence on fresh water sources, and improving the overall utilization rate of water resources. In addition, by treating pollutants such as pesticides, fertilizers, and crop residues in ditch water, the amount of these pollutants discharged into the surrounding environment is reduced, reducing the harm of agricultural non-point source pollution to soil, water bodies, and ecosystems;

[0067] (2) The use of an intelligent control unit combined with a fuzzy logic algorithm can automatically adjust the system's operating parameters according to real-time changes in water quality and irrigation needs, ensuring the efficient operation of the system and the stability of the purification effect.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is an overall block diagram of an intelligent control agricultural ditch water purification and reuse system of the present invention. DETAILED DESCRIPTION

[0070] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0071] See also Figure 1 , an intelligent control agricultural ditch water purification and reuse system, including a water inlet, a primary filtration unit, a biological purification unit, a deep treatment unit, a water quality monitoring unit, an intelligent control unit and a water outlet;

[0072] The water inlet is first connected to the primary filter unit, which performs preliminary filtration on the ditch water flowing into the water inlet; the primary filter unit adopts a multi-layer filter structure, including a coarse filter and a fine filter; the mesh diameter of the coarse filter is 5-10mm, which intercepts large crop residues and soil particles; the mesh diameter of the fine filter is 0.5-1mm, which further filters small impurities.

[0073] The water outlet of the primary filtration unit is connected to the biological purification unit, which performs biological purification on the water after primary filtration; the biological purification unit includes multiple biological filter tanks, which are filled with biological fillers, including ceramsite and activated carbon fiber; and a variety of microorganisms are inoculated in the biological filter tanks, which decompose organic pollutants in the ditch water, including residual pesticides, fertilizers and organic matter produced by the decomposition of crop residues.

[0074] The water outlet of the biological purification unit is connected to the deep treatment unit, which further improves the water quality; the water outlet of the deep treatment unit is connected to the outlet, which transports the purified water to the irrigation system to achieve the reuse of water resources. The deep treatment unit uses membrane treatment technology, including ultrafiltration membrane and reverse osmosis membrane; the ultrafiltration membrane removes large molecular organic matter and colloids in the water; the reverse osmosis membrane removes soluble salts and small molecular organic matter (such as bacteria, viruses, pyrogens, colloids, organic matter, heavy metals, pesticides) in the water.

[0075] Water quality monitoring units are provided in the primary filtration unit, biological purification unit, and deep treatment unit to monitor the water quality parameters at each stage in real time;

[0076] The water quality monitoring unit is connected to the intelligent control unit, and the monitored water quality data is transmitted to the intelligent control unit. The intelligent control unit controls the operating parameters of the primary filtration unit, the biological purification unit, and the deep treatment unit according to these data. The corresponding operating parameters of the primary filtration unit, the biological purification unit, and the deep treatment unit are: filter cleaning frequency, aeration intensity, and membrane flux. The water quality monitoring unit includes a pH sensor, a dissolved oxygen sensor, a chemical oxygen demand COD sensor, and an ammonia nitrogen sensor; in different processing stages of the system, the pH sensor, the dissolved oxygen sensor, the chemical oxygen demand COD sensor, and the ammonia nitrogen sensor are set to monitor the water quality parameters in real time. According to the data transmitted by the water quality monitoring unit, as well as the preset water quality standards and irrigation requirements, the intelligent control unit uses a fuzzy logic algorithm to regulate the filter cleaning frequency of the primary filtration unit, the aeration intensity of the biological purification unit, and the membrane flux of the deep treatment unit.

[0077] The process of controlling the filter cleaning frequency of the primary filter unit using the fuzzy logic algorithm is as follows:

[0078] S11, obtaining input data; the intelligent control unit obtains water quality data related to the filter cleaning frequency from the water quality monitoring unit, including particle concentration x and turbidity y;

[0079] S12, define fuzzy sets of particle concentration x and turbidity y, for example, "low", "medium", and "high" for particle concentration, and "small", "medium", and "large" for turbidity; then calculate the membership degree of each fuzzy set respectively;

[0080] S13, determine the fuzzy rules, the form of the fuzzy rules is: if the particle concentration is "A" and the turbidity is "B", then the filter cleaning frequency is "F", there are n fuzzy rules, where A and B are fuzzy sets of particle concentration x and turbidity y respectively, and F is the filter cleaning frequency; for example, if the particle concentration is "high" and the turbidity is "large", then the filter cleaning frequency is "high"; if the particle concentration is "medium" and the turbidity is "medium", then the filter cleaning frequency is "medium", etc.;

[0081] S14. Calculate the activation strength ω of each rule i , the expression is as follows:

[0082] ω i =μ x-对应模糊集 (x)×μ y-对应模糊集 (y);

[0083] In the formula, μ x-对应模糊集 (x) represents the membership function of the fuzzy set corresponding to the particle concentration x; μ y-对应模糊集 (y) represents the membership function of the fuzzy set corresponding to turbidity y;

[0084] S15. Determine the membership of the output fuzzy set; assume that the filter cleaning frequency F has m fuzzy sets F1, F2, …, F m , for each fuzzy set F j , calculate its total membership, the calculation expression is as follows:

[0085]

[0086] In the formula, In the i-th fuzzy rule, when the output is F i When F i For the fuzzy set F j The degree of membership;

[0087] S16, use the centroid method to defuzzify the method to obtain the accurate filter cleaning frequency f exact , the calculation expression is as follows:

[0088]

[0089] In the formula, f j is the fuzzy set F of filter cleaning frequency F j The corresponding value;

[0090] S17. The filter cleaning frequency of the primary filter unit is regulated according to the precise filter cleaning frequency; the calculated precise filter cleaning frequency value is compared with the current filter cleaning frequency value; if the precise filter cleaning frequency value is greater than the current filter cleaning frequency, the filter cleaning frequency is increased by increasing the backwash water flow pressure or increasing the scraping speed; otherwise, the filter cleaning frequency is reduced.

[0091] The process of using fuzzy logic algorithm to control the aeration intensity of the biological purification unit is as follows:

[0092] S21, obtaining input data; the intelligent control unit obtains water quality data related to the biological purification unit from the water quality monitoring unit, including chemical oxygen demand a and dissolved oxygen concentration b;

[0093] S22, define fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b, for example, chemical oxygen demand is "low", "medium", "high", dissolved oxygen concentration is "low", "suitable", "high", and then calculate the membership degree of each fuzzy set respectively;

[0094] S23. Determine the fuzzy rules. The form of the fuzzy rules is: if the chemical oxygen demand is "C" and the dissolved oxygen concentration is "D", then the aeration intensity is "V". There are p fuzzy rules, where C and D are the fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b, respectively, and V is the aeration intensity. For example: if the chemical oxygen demand is "high" and the dissolved oxygen concentration is "low", then the aeration intensity is "high"; if the chemical oxygen demand is "medium" and the dissolved oxygen concentration is "appropriate", then the aeration intensity is "medium", etc.

[0095] S24. Calculate the activation strength ω of each rule 生i , the expression is as follows:

[0096] ω 生i =μ a-对应模糊集 (a)×μ b-对应模糊集 (b);

[0097] In the formula, μ a-对应模糊集 (a) represents the membership function of the fuzzy set corresponding to the chemical oxygen demand a; μ b-对应模糊集 (b) represents the membership function of the fuzzy set corresponding to the dissolved oxygen concentration b;

[0098] S25. Determine the membership of the output fuzzy set; assume that the aeration intensity V has q fuzzy sets V1, V2, …, V q , for each fuzzy set V j , calculate its total membership, the calculation expression is as follows:

[0099]

[0100] In the formula, In the ith rule, when the output is V i When V i For the fuzzy set V j The degree of membership;

[0101] S26, use the centroid method to defuzzify the method to obtain the accurate aeration intensity v exact , the calculation expression is as follows:

[0102]

[0103] In the formula, v j is the fuzzy set V of aeration intensity V j The corresponding value;

[0104] S27. Regulating the aeration intensity of the biological purification unit according to the precise aeration intensity; comparing the precise aeration intensity with the current operating aeration intensity value; if the precise aeration intensity is greater than the current operating aeration intensity value, increasing the aeration intensity by increasing the air flow or increasing the impeller speed; otherwise, reducing the aeration intensity.

[0105] The process of using fuzzy logic algorithm to control the membrane flux of the deep processing unit is as follows:

[0106] S31, obtaining input data; the intelligent control unit obtains water quality data related to the membrane flux from the water quality monitoring unit, including ion concentration c and microbial content d;

[0107] S32, define fuzzy sets of ion concentration c and microbial content d, for example, ion concentration is "low", "medium", "high", and microbial content is "few", "medium", "many"; then calculate the membership degree of each fuzzy set respectively;

[0108] S33. Determine fuzzy rules. The form of fuzzy rules is: if the ion concentration is "P" and the microbial content is "Q", then the membrane flux is "M". There are s fuzzy rules, where P and Q are fuzzy sets of ion concentration c and microbial content d, respectively, and M is the membrane flux. For example, if the ion concentration is "high" and the microbial content is "high", the membrane flux is "low"; if the ion concentration is "medium" and the microbial content is "medium", the membrane flux is "medium", etc.

[0109] S34. Calculate the activation strength ω of each rule 膜i , the expression is as follows:

[0110] ω 膜i =μ c-对应模糊集 (c)×μ d-对应模糊集 (d);

[0111] In the formula, μ c-对应模糊集 (c) represents the membership function of the fuzzy set corresponding to the ion concentration c; μ d-对应模糊集 (d) represents the membership function of the fuzzy set corresponding to the microbial content d;

[0112] S35, determine the membership of the output fuzzy set; assume that the membrane flux M has s fuzzy sets M1, M2, ..., M s , for each fuzzy set M j , calculate its total membership, the calculation expression is as follows:

[0113]

[0114] In the formula, In the i-th rule, when the output is M i When Mi For the fuzzy set M j The degree of membership;

[0115] S36. The centroid method is used to defuzzify the membrane flux to obtain the accurate membrane flux. The calculation expression is as follows:

[0116]

[0117] In the formula, m j is the fuzzy set M of membrane flux M j The corresponding value;

[0118] S37, regulating the membrane flux of the deep processing unit according to the precise membrane flux; comparing the calculated precise membrane flux value with the current operating membrane flux value; if the precise membrane flux value is greater than the current membrane flux value, it means that the membrane flux needs to be increased. At this time, according to the operating status of the membrane assembly and the performance of the equipment, it is preferred to increase the membrane flux by increasing the transmembrane pressure or increasing the cross-flow velocity; if the precise membrane flux value is less than the current membrane flux value, it means that the membrane flux needs to be reduced. At this time, it can be achieved by reducing the transmembrane pressure or reducing the cross-flow velocity.

[0119] The shape of the membership function is selected according to actual conditions, and is one of a triangular membership function, a trapezoidal membership function, and a Gaussian membership function.

[0120] Therefore, the present invention adopts the above-mentioned intelligent control agricultural ditch water purification and reuse system, which can effectively solve the problem of agricultural ditch water purification and reuse and achieve sustainable development of agriculture.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent control agricultural ditch water purification and reuse system, characterized in that: It includes a water inlet, a primary filtration unit, a biological purification unit, a deep treatment unit, a water quality monitoring unit, an intelligent control unit and a water outlet; The water inlet is first connected to the primary filter unit, which performs preliminary filtration on the ditch water flowing into the water inlet; The water outlet of the primary filtration unit is connected to the biological purification unit, and the biological purification unit performs biological purification treatment on the water after the primary filtration; The water outlet of the biological purification unit is connected to the deep treatment unit, which further improves the water quality; Water quality monitoring units are provided in the primary filtration unit, biological purification unit, and deep treatment unit to monitor the water quality parameters at each stage in real time; The water quality monitoring unit is connected to the intelligent control unit and transmits the monitored water quality data to the intelligent control unit. The intelligent control unit controls the operating parameters of the primary filtration unit, the biological purification unit and the deep treatment unit according to these data. The corresponding operating parameters of the primary filtration unit, the biological purification unit and the deep treatment unit are: filter cleaning frequency, aeration intensity and membrane flux. The water outlet of the deep treatment unit is connected to the water outlet, and the water outlet transports the purified water to the irrigation system to realize the reuse of water resources.

2. The intelligent control agricultural ditch water purification and reuse system according to claim 1 is characterized by: The primary filtration unit adopts a multi-layer filter structure, including a coarse filter and a fine filter; the mesh diameter of the coarse filter is 5-10mm, which intercepts large crop residues and soil particles; the mesh diameter of the fine filter is 0.5-1mm, which further filters small impurities.

3. The intelligent control agricultural ditch water purification and reuse system according to claim 2 is characterized by: The biological purification unit includes multiple biofilters, which are filled with biological fillers, including expanded clay and activated carbon fibers. Various microorganisms are inoculated in the biofilters to decompose organic pollutants in the ditch water, including residual pesticides, fertilizers, and organic matter produced by the decomposition of crop residues.

4. The intelligent control agricultural ditch water purification and reuse system according to claim 3 is characterized by: The deep treatment unit adopts membrane treatment technology, including ultrafiltration membrane and reverse osmosis membrane; ultrafiltration membrane removes large molecular organic matter and colloids in water; reverse osmosis membrane removes soluble salts and small molecular organic matter in water.

5. The intelligent control agricultural ditch water purification and reuse system according to claim 4 is characterized by: The water quality monitoring unit includes a pH sensor, a dissolved oxygen sensor, a chemical oxygen demand (COD) sensor and an ammonia nitrogen sensor. In different processing stages of the system, the pH sensor, the dissolved oxygen sensor, the chemical oxygen demand (COD) sensor and the ammonia nitrogen sensor are set to monitor the water quality parameters in real time.

6. The intelligent control agricultural ditch water purification and reuse system according to claim 5 is characterized by: The intelligent control unit uses fuzzy logic algorithm to adjust the filter cleaning frequency of the primary filtration unit, the aeration intensity of the biological purification unit, and the membrane flux of the deep treatment unit based on the data transmitted by the water quality monitoring unit, as well as the preset water quality standards and irrigation needs.

7. The intelligent control agricultural ditch water purification and reuse system according to claim 6 is characterized in that: The process of controlling the filter cleaning frequency of the primary filter unit using the fuzzy logic algorithm is as follows: S11, obtaining input data; the intelligent control unit obtains water quality data related to the filter cleaning frequency from the water quality monitoring unit, including particle concentration x and turbidity y; S12, define fuzzy sets of particle concentration x and turbidity y, and calculate their membership degrees to each fuzzy set respectively; S13, determine the fuzzy rules, the form of the fuzzy rules is: if the particle concentration is "A" and the turbidity is "B", the filter cleaning frequency is "F", there are n fuzzy rules, where A and B are fuzzy sets of particle concentration x and turbidity y respectively, and F is the filter cleaning frequency; S14. Calculate the activation strength ω of each rule i , the expression is as follows: oh i =μ x-对应模糊集 (x)×μ y-对应模糊集 (y); In the formula, μ x-对应模糊集 (x) represents the membership function of the fuzzy set corresponding to the particle concentration x; μ y-对应模糊集 (y) represents the membership function of the fuzzy set corresponding to turbidity y; S15. Determine the membership of the output fuzzy set; assume that the filter cleaning frequency F has m fuzzy sets F1, F2, …, F m , for each fuzzy set F j , calculate its total membership, the calculation expression is as follows: In the formula, In the i-th fuzzy rule, when the output is F i When F i For the fuzzy set F j The degree of membership; S16, use the centroid method to defuzzify the filter to obtain the accurate filter cleaning frequency f exact , the calculation expression is as follows: In the formula, f j is the fuzzy set F of filter cleaning frequency F j The corresponding value; S17. Regulating the filter cleaning frequency of the primary filter unit according to the precise filter cleaning frequency.

8. The intelligent control agricultural ditch water purification and reuse system according to claim 7 is characterized in that: The process of using fuzzy logic algorithm to control the aeration intensity of the biological purification unit is as follows: S21, obtaining input data; the intelligent control unit obtains water quality data related to the biological purification unit from the water quality monitoring unit, including chemical oxygen demand a and dissolved oxygen concentration b; S22, define the fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b, and calculate the membership degree of each fuzzy set respectively; S23, determine the fuzzy rules, the form of the fuzzy rules is: chemical oxygen demand is "C" and dissolved oxygen concentration is "D", then the aeration intensity is "V", there are p fuzzy rules, where C and D are the fuzzy sets of chemical oxygen demand a and dissolved oxygen concentration b respectively, and V is the aeration intensity; S24. Calculate the activation strength ω of each rule 生i , the expression is as follows: oh 生i =μ a-对应模糊集 (a)×μ b-对应模糊集 (b); In the formula, μ a-对应模糊集 (a) represents the membership function of the fuzzy set corresponding to the chemical oxygen demand a; μ b-对应模糊集 (b) represents the membership function of the fuzzy set corresponding to the dissolved oxygen concentration b; S25. Determine the membership of the output fuzzy set; assume that the aeration intensity V has q fuzzy sets V1, V2, …, V q , for each fuzzy set V j , calculate its total membership, the calculation expression is as follows: In the formula, In the ith rule, when the output is V i When V i For the fuzzy set V j The degree of membership; S26, use the centroid method to defuzzify the method to obtain the accurate aeration intensity v exact , the calculation expression is as follows: In the formula, v j is the fuzzy set V of aeration intensity V j The corresponding value; S27. The aeration intensity of the biological purification unit is regulated according to the precise aeration intensity.

9. The intelligent control agricultural ditch water purification and reuse system according to claim 8 is characterized in that: The process of using fuzzy logic algorithm to control the membrane flux of the deep processing unit is as follows: S31, obtaining input data; the intelligent control unit obtains water quality data related to the membrane flux from the water quality monitoring unit, including ion concentration c and microbial content d; S32, defining fuzzy sets of ion concentration c and microbial content d, and calculating the membership degree of each fuzzy set respectively; S33, determine the fuzzy rules, the form of the fuzzy rules is: ion concentration is "P" and microbial content is "Q", then the membrane flux is "M", there are s fuzzy rules, where P, Q are fuzzy sets of ion concentration c and microbial content d, respectively, and M is the membrane flux; S34. Calculate the activation strength ω of each rule 膜i , the expression is as follows: oh 膜i =μ c-对应模糊集 (c)×μ d-对应模糊集 (d); In the formula, μ c-对应模糊集 (c) represents the membership function of the fuzzy set corresponding to the ion concentration c; μ d-对应模糊集 (d) represents the membership function of the fuzzy set corresponding to the microbial content d; S35, determine the membership of the output fuzzy set; assume that the membrane flux M has s fuzzy sets M1, M2, ..., M s , for each fuzzy set M j , calculate its total membership, the calculation expression is as follows: In the formula, In the i-th rule, when the output is M i When M i For the fuzzy set M j The degree of membership; S36. The centroid method is used to defuzzify the membrane flux to obtain the accurate membrane flux. The calculation expression is as follows: In the formula, m j is the fuzzy set M of membrane flux M j The corresponding value; S37. Regulating the membrane flux of the deep processing unit according to the precise membrane flux.

10. The intelligent control agricultural ditch water purification and reuse system according to claim 9 is characterized by: The membership function is one of a triangular membership function, a trapezoidal membership function, and a Gaussian membership function.

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