Control system and method of intelligent pump station

Abstract

The invention discloses a control system and method for an intelligent pump station. The control system comprises a data acquisition module, a data processing module and a pump station control module, relates to the technical field of pump station control, and solves the technical problems that the conveying water amount of a pump station is difficult to accurately control and the later maintenance cost of the pump station is low in the prior art. The method comprises the following steps: acquiring equipment operation information and working environment information of each water pump in a pump station; calculating the actual water consumption of the corresponding irrigation area in the set time period based on the irrigation water consumption and rainfall information of each irrigation area; and adjusting the operation condition of the pump station based on the actual water consumption and the water pump scheduling priority sequence. According to the method, the target farmland planting information, the rainfall information and the equipment operation and working environment information of the water pumps in the pump station are comprehensively collected, the optimal irrigation water consumption is obtained through matching of the crop water consumption database, and the pump station operation is optimized according to the water pump dispatching priority sequence, so that the later maintenance cost of the pump station is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of pump station control, and specifically relates to a control system and method for an intelligent pump station. Background Art

[0002] With the continuous development of agriculture, the demand for crop irrigation is increasing. However, due to the uneven regional distribution, uneven seasonal rainfall, and insufficient regulation capacity of water resources in China, the contradiction between supply and demand of agricultural irrigation has become increasingly prominent. To alleviate this contradiction, irrigation reservoirs are mostly built at present to ensure agricultural water use.

[0003] The prior art obtains the historical water use information of the farmland to be irrigated, analyzes the historical water use information to obtain the estimated water consumption, and determines the number of water pumps to be started in the pump station based on the estimated water consumption. However, the irrigation water consumption of farmland is related to factors such as the type of crops, growth cycle, and weather. The solutions of the prior art are difficult to control the water delivery volume of the pump station according to these factors, resulting in inaccurate water supply of the pump station. In addition, during the actual operation of the pump station, the working loads and working environments of different water pumps in the pump station are not the same. For example, the wear rate of the water pump is higher when it operates in a high-load state and in a water body with a high sediment content. The prior art only evaluates the wear degree of the water pump based on the operation duration, making the evaluated wear degree of the water pump inaccurate, resulting in poor wear uniformity of several water pumps in the pump station and increasing the later maintenance cost of the pump station.

[0004] The present invention proposes a control system and method for an intelligent pump station to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a control system and method for an intelligent pump station, which is used to solve the technical problems that the prior art is difficult to accurately control the water delivery volume of the pump station according to the type of crops, growth cycle, and weather; in addition, the prior art only evaluates the wear degree of the water pump based on the operation duration, resulting in poor wear uniformity of several water pumps in the pump station and increasing the later maintenance cost of the pump station.

[0006] To achieve the above object, the first aspect of the present invention provides a control system for an intelligent pump station, including: a data processing module, and a data acquisition module and a pump station control module connected thereto;

[0007] The data acquisition module: is used to collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each water pump in the pump station; wherein, the planting information includes the type and growth cycle of the currently planted crops; the operation information includes the cumulative operation duration and the average working load; the working environment information is the sediment content;

[0008] The data processing module: used to divide the target farmland based on the types of currently planted crops to obtain several irrigation areas; input the planting information of each irrigation area into the crop water consumption database for matching to obtain the corresponding irrigation water consumption; calculate the actual water consumption of the corresponding irrigation area during a set time period based on the irrigation water consumption and rainfall information of each irrigation area; and,

[0009] construct a pump scheduling priority sequence based on the equipment operation information and working environment information of each pump;

[0010] The pump station control module: used to adjust the operation status of the pump station based on the actual water consumption and the pump scheduling priority sequence.

[0011] Preferably, the control system of the intelligent pump station further includes a crop water consumption database, and the crop water consumption database contains the water consumption per unit area of several crop types in different growth cycles.

[0012] Preferably, the dividing the target farmland based on the types of currently planted crops includes:

[0013] Divide the target farmland into several sub-areas with equal areas, and extract the types of currently planted crops in each sub-area; judge whether the types of currently planted crops in adjacent sub-areas are the same; if so, merge the adjacent sub-areas into one irrigation area; if not, use the corresponding sub-area as an independent irrigation area.

[0014] Preferably, the inputting the planting information of each irrigation area into the crop water consumption database for matching includes:

[0015] A1: Extract the planting information of each irrigation area; obtain the planting area of each irrigation area;

[0016] A2: Input the types and growth cycles of the currently planted crops in the planting information into the crop water consumption database to obtain the water consumption per unit area;

[0017] A3: Multiply the water consumption per unit area of the currently planted crops in each irrigation area by the corresponding planting area to obtain the irrigation water consumption of each irrigation area.

[0018] Preferably, the calculating the actual water consumption of the corresponding irrigation area during a set time period based on the irrigation water consumption and rainfall information of each irrigation area includes:

[0019] B1: Extract the irrigation water consumption and rainfall information of each irrigation area in several consecutive cycles;

[0020] B2: Input the irrigation water consumption for several consecutive periods into the irrigation water consumption prediction model to obtain the predicted value of the irrigation water consumption in the park; among them, the irrigation water consumption prediction model is constructed based on an artificial intelligence model;

[0021] B3: Using time as the independent variable and irrigation water consumption and rainfall information as the dependent variables, respectively generate an irrigation water consumption curve fi(t) and a rainfall curve gi(t) through curve fitting; where i = 1, 2,..., n, and n is the total number of irrigation areas;

[0022] B4: Through the formula Calculate the actual water consumption SYLi of irrigation area i within the set time period; where t is the time, t1 is the start time of the set time period, t2 is the end time of the set time period, and t1 ≤ t ≤ t2.

[0023] It should be noted that the length of the set time period is determined according to the growth cycle of the crops planted in the irrigation area.

[0024] Preferably, the irrigation water consumption prediction model is constructed based on an artificial intelligence model, including:

[0025] Extract the irrigation water consumption of each irrigation area in several consecutive periods and integrate it into several groups of training data and test data; use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain an irrigation water consumption prediction model with the input being the irrigation water consumption of the most recent several consecutive periods and the output being the predicted value of the irrigation water consumption in the prediction period; among them, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0026] Preferably, constructing the pump scheduling priority sequence based on the equipment operation information and working environment information of each pump includes:

[0027] Extract the equipment operation information and working environment information of each pump; calculate the wear evaluation coefficient of each pump based on the equipment operation information and working environment information; sort them in ascending order according to the wear evaluation coefficient to obtain the pump scheduling priority sequence.

[0028] Preferably, calculating the wear evaluation coefficient of each pump based on the equipment operation information and working environment information includes:

[0029] Extract the equipment operation information and working environment information of each pump; through the formula MPXj = a × e GFZjCalculate the wear evaluation coefficient MPXj of pump j using a×[b×LJSj + c×ln(1 + NSHj)]; where LJSj is the cumulative operation duration of pump j, GFZj is the average working load of pump j, and NSHj is the sediment content in the working environment where pump j is located; a, b, and c are all proportionality coefficients greater than 0, and the specific values of a, b, and c are set by those skilled in the art according to experience; j = 1, 2, …, m, where m is the total number of pumps in the pumping station; e is the natural constant, and ln() is the logarithmic function with the natural constant as the base.

[0030] Preferably, adjusting the operation status of the pumping station based on the actual water consumption and the pump scheduling priority sequence includes:

[0031] C1: Extract the pump scheduling priority sequence and the actual water consumption of each irrigation area, calculate the sum of the actual water consumption of each irrigation area and mark it as the expected total water consumption;

[0032] C2: Select a corresponding number of pumps from the pump scheduling priority sequence in ascending order of the wear evaluation coefficient, and mark them as the pumps to be started; calculate the sum of the flow rates of the pumps to be started, and mark it as the expected water supply flow rate; where the flow rate refers to the volume of water transported by the pump per unit time;

[0033] C3: Calculate the product of the expected water supply flow rate and the set time period to obtain the expected water supply volume;

[0034] C4: Determine whether the expected water supply volume is less than the expected total water consumption; if so, continue to select a number of pumps from the pump scheduling priority sequence and mark them as the pumps to be started; if not, set the operation status of the pump to be started with the largest wear evaluation coefficient to off;

[0035] C5: Set the operation status of the pumps to be started to on; where the operation status includes on and off.

[0036] The second aspect of the present invention provides a control method for an intelligent pumping station, including:

[0037] S1: Collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each pump in the pumping station;

[0038] S2: Divide the target farmland based on the type of the currently planted crops to obtain several irrigation areas;

[0039] S3: Input the planting information of each irrigation area into the crop water consumption database for matching to obtain the corresponding irrigation water consumption;

[0040] S4: Calculate the actual water consumption of the corresponding irrigation area in the set time period based on the irrigation water consumption and rainfall information of each irrigation area;

[0041] S5: Construct a pump scheduling priority sequence based on the equipment operation information and working environment information of each pump.

[0042] S6: Adjust the operation status of the pumping station based on the actual water consumption and the pump scheduling priority sequence.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. By comprehensively collecting the planting information of the target farmland, rainfall information, and the equipment operation and working environment information of each pump in the pumping station, the present invention realizes the precise division of the farmland and calculates the actual water consumption of each irrigation area. The optimal irrigation water consumption is obtained by matching with the crop water database, and based on this, the operation of the pumping station is optimized in combination with the pump scheduling priority sequence, thereby improving the water resource utilization rate, reducing waste, ensuring that different crops obtain appropriate water supply at different growth stages, prolonging the service life of the pump, reducing energy consumption, improving the irrigation efficiency and the economic benefits of agricultural production, and meeting the requirements of precise and intelligent management of modern agriculture.

[0045] 2. By accurately calculating the actual water consumption of each irrigation area and summarizing it into the estimated total water consumption, combining the pump scheduling priority sequence and the wear evaluation coefficient, the present invention intelligently selects the pumps to be started and calculates the estimated water supply flow, and then determines the estimated water supply according to the set time period. By comparing the estimated water supply with the estimated total water consumption, the number of pumps to be started is dynamically adjusted to ensure sufficient and efficient water supply. After the water supply meets the demand, the pump with the largest wear evaluation coefficient is preferentially shut down to extend the equipment life and save energy consumption. Finally, the determined pumps to be started are set to the on state to achieve precise irrigation. This solution effectively improves the flexibility and water resource utilization efficiency of the irrigation system, reduces the operation cost, and ensures the precision and sustainability of farmland irrigation.

[0046] 3. By extracting the equipment operation information and working environment information of each pump in the pumping station and substituting them into the formula for calculation, the present invention obtains the wear evaluation coefficient of each pump; the present invention comprehensively considers factors such as the cumulative operation duration of the pump, the average working load, and the sediment content in the working environment, and the wear impact caused by the pump during actual operation; compared with the traditional solution that only evaluates the wear degree according to the operation time of the pump, the wear evaluation coefficient calculated by the present invention can more accurately reflect the actual wear degree of the pump; it provides a basis for subsequent scheduling optimization of several pumps in the pumping station, ensuring the uniformity of the wear degree among different pumps in the pumping station, thereby facilitating the reduction of the operation and maintenance degree of the pumping station. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0048] Figure 1 It is the overall flowchart of the control method for the intelligent pumping station of the present invention;

[0049] Figure 2 It is the schematic diagram of the principle of the control system of the intelligent pumping station of the present invention;

[0050] Figure 3 It is the flowchart for adjusting the operation status of the pumping station in the present invention. Detailed implementation manners

[0051] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0052] Please refer to Figures 1 - 3 , the embodiment of the first aspect of the present invention provides a control system for an intelligent pumping station, including: a data processing module, and a data acquisition module and a pumping station control module connected thereto;

[0053] Data acquisition module: used to collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each water pump in the pumping station; wherein, the planting information includes the types and growth cycles of the currently planted crops; the operation information includes the cumulative operation duration and the average working load; the working environment information is the sediment content;

[0054] Data processing module: used to divide the target farmland based on the types of the currently planted crops to obtain several irrigation areas; input the planting information of each irrigation area into the crop water use database for matching to obtain the corresponding irrigation water consumption; calculate the actual water consumption of the corresponding irrigation area in a set time period based on the irrigation water consumption and rainfall information of each irrigation area; and,

[0055] Construct a water pump scheduling priority sequence based on the equipment operation information and working environment information of each water pump;

[0056] Pumping station control module: used to adjust the operation status of the pumping station based on the actual water consumption and the water pump scheduling priority sequence.

[0057] In this embodiment, a control system for an intelligent pumping station further includes a crop water database, and the crop water database includes the water consumption per unit area of several crop types in different growth cycles.

[0058] Exemplarily, when the crop type is rice, the specific data of the crop water database is represented by the following table:

[0059] Growth period Water consumption per unit area Seedling stage 1000 cubic meters per mu Jointing stage 3000 cubic meters per mu Heading stage 5000 cubic meters per mu

[0060] In this embodiment, dividing the target farmland based on the type of currently planted crops includes:

[0061] Dividing the target farmland into several sub-regions of equal area, and extracting the types of currently planted crops in each sub-region; judging whether the types of currently planted crops in adjacent sub-regions are the same; if so, merging the adjacent sub-regions into one irrigation area; if not, taking the corresponding sub-region as an independent irrigation area.

[0062] Exemplarily, it is set that the target farmland is divided into 4 adjacent sub-regions of equal area. The crops planted in sub-region 1 and sub-region 3 are rice, the crop planted in sub-region 2 is cotton, and the crop planted in sub-region 4 is corn; since the types of currently planted crops in sub-region 1 and sub-region 3 are the same, sub-region 1 and sub-region 3 are merged into one irrigation area.

[0063] In this embodiment, inputting the planting information of each irrigation area into the crop water database for matching includes:

[0064] A1: Extracting the planting information of each irrigation area; obtaining the planting area of each irrigation area;

[0065] A2: Inputting the type and growth cycle of the currently planted crops in the planting information into the crop water database to obtain the water consumption per unit area;

[0066] A3: Multiplying the water consumption per unit area of the currently planted crops in each irrigation area by the corresponding planting area to obtain the irrigation water consumption of each irrigation area.

[0067] Exemplarily, it is set that the type of the currently planted crop in irrigation area 1 is rice and the growth cycle is the seedling stage. Then, by inputting into the crop water database, the corresponding water consumption per unit area is obtained as 1000 cubic meters per mu; it is set that the planting area of irrigation area 1 is 3 mu. Then, based on the calculation of the water consumption per unit area of irrigation area 1, the irrigation water consumption of irrigation area 1 is obtained as 3000 cubic meters.

[0068] In this embodiment, calculating the actual water consumption of the corresponding irrigation area in a set time period based on the irrigation water consumption and rainfall information of each irrigation area includes:

[0069] B1: Extract the irrigation water consumption and rainfall information of each irrigation area in a number of consecutive periods;

[0070] B2: Input the irrigation water consumption of a number of consecutive periods into the irrigation water consumption prediction model to obtain the predicted value of the irrigation water consumption in the park; among them, the irrigation water consumption prediction model is constructed based on an artificial intelligence model;

[0071] B3: Taking time as the independent variable and irrigation water consumption and rainfall information as the dependent variables, respectively generate an irrigation water consumption curve fi(t) and a rainfall curve gi(t) through curve fitting; where i = 1, 2,..., n, and n is the total number of irrigation areas;

[0072] B4: Calculate the actual water consumption SYLi of irrigation area i within a set time period through the formula where t is time, t1 is the start time of the set time period, t2 is the end time of the set time period, and t1 ≤ t ≤ t2.

[0073] In this embodiment, the irrigation water consumption prediction model is constructed based on an artificial intelligence model, including:

[0074] Extract the irrigation water consumption of each irrigation area in a number of consecutive periods and integrate it into several groups of original data. Take 80% of the original data as training data and 20% as test data; use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain an irrigation water consumption prediction model with the input being the irrigation water consumption of the most recent several consecutive periods and the output being the predicted value of the irrigation water consumption in the prediction period; among them, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0075] In this embodiment, a pump scheduling priority sequence is constructed based on the equipment operation information and working environment information of each pump, including:

[0076] Extract the equipment operation information and working environment information of each pump; calculate the wear evaluation coefficient of each pump based on the equipment operation information and working environment information; sort them in ascending order of the wear evaluation coefficient to obtain the pump scheduling priority sequence.

[0077] Exemplarily, set the wear evaluation coefficients of pump 1, pump 2, pump 3, pump 4, and pump 5 to MPX1 = 78.48, MPX2 = 45.58, MPX3 = 52.37, MPX4 = 48.63, and MPX5 = 85.61 respectively; sort them in ascending order of the wear evaluation coefficient to obtain the pump scheduling priority sequence as {pump 2, pump 4, pump 3, pump 1, pump 5}.

[0078] In this embodiment, calculating the wear evaluation coefficient of each water pump based on the equipment operation information and the working environment information includes:

[0079] Extracting the equipment operation information and the working environment information of each water pump; calculating the wear evaluation coefficient MPXj of water pump j through the formula MPXj = a×e GFZj ×[b×LJSj + c×ln(1 + NSHj)]; where LJSj is the cumulative operation duration of water pump j, GFZj is the average working load of water pump j, and NSHj is the sediment content in the working environment where water pump j is located; a, b, and c are all proportionality coefficients greater than 0, and the specific values of a, b, and c are set according to the experience of those skilled in the art; j = 1, 2,..., m, where m is the total number of water pumps in the pump station; e is the natural constant, and ln() is the logarithmic function with the natural constant as the base.

[0080] Exemplarily, set the proportionality coefficients a = 1.2, b = 0.1, and c = 3; the cumulative operation duration LJS1 of water pump 1 is 150h, the average working load GFZ1 of water pump 1 is 80%, and the sediment content NSH1 in the working environment where water pump 1 is located is 120g / m3; the wear evaluation coefficient MPX1 of water pump 1 is calculated to be approximately 78.48 through the formula.

[0081] The present invention extracts the equipment operation information and the working environment information of each water pump in the pump station and substitutes them into the formula for calculation to obtain the wear evaluation coefficient of each water pump; the present invention comprehensively considers factors such as the cumulative operation duration, the average working load, and the sediment content in the working environment of the water pump, and the wear impact caused by the water pump during actual operation; compared with the traditional solution that only evaluates the wear degree based on the operation time of the water pump, the wear evaluation coefficient calculated by the present invention can more accurately reflect the actual wear degree of the water pump; it provides a basis for subsequent scheduling optimization of several water pumps in the pump station, ensures the uniformity of the wear degree among different water pumps in the pump station, and thus is beneficial to reducing the operation and maintenance degree of the pump station.

[0082] In this embodiment, adjusting the operation status of the pump station based on the actual water consumption and the water pump scheduling priority sequence includes:

[0083] C1: Extracting the water pump scheduling priority sequence and the actual water consumption of each irrigation area, calculating the sum of the actual water consumption of each irrigation area and marking it as the expected total water consumption;

[0084] C2: Selecting corresponding several water pumps from the water pump scheduling priority sequence in ascending order of the wear evaluation coefficient and marking them as the water pumps to be started; calculating the sum of the flow rates of the water pumps to be started and marking it as the expected water supply flow rate; where the flow rate refers to the volume of water transported by the water pump per unit time;

[0085] C3: Calculate the product of the predicted water supply flow rate and the set time period to obtain the predicted water supply volume.

[0086] C4: Determine whether the predicted water supply volume is less than the predicted total water consumption. If so, continue to select several pumps from the pump scheduling priority sequence and mark them as pumps to be started. If not, set the operating status of the pump with the largest wear assessment coefficient among the pumps to be started to closed.

[0087] C5: Set the operating status of the pumps to be started to open; where the operating status includes open and closed.

[0088] The present invention accurately calculates the actual water consumption of each irrigation area and aggregates it into the predicted total water consumption, combines the pump scheduling priority sequence and the wear assessment coefficient, intelligently selects the pumps to be started and calculates the predicted water supply flow rate, and then determines the predicted water supply volume according to the set time period. By comparing the predicted water supply volume with the predicted total water consumption, the number of pumps to be started is dynamically adjusted to ensure sufficient and efficient water supply. After the water supply meets the demand, the pump with the largest wear assessment coefficient is preferentially closed to extend the equipment life and save energy consumption. Finally, the determined pumps to be started are set to the open state to achieve precise irrigation. This solution effectively improves the flexibility and water resource utilization efficiency of the irrigation system, reduces the operating cost, and ensures the precision and sustainability of farmland irrigation.

[0089] An embodiment of the second aspect of the present invention provides a control method for an intelligent pumping station, including:

[0090] S1: Collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each pump in the pumping station.

[0091] S2: Divide the target farmland based on the type of crops currently planted to obtain several irrigation areas.

[0092] S3: Input the planting information of each irrigation area into the crop water consumption database for matching to obtain the corresponding irrigation water consumption.

[0093] S4: Calculate the actual water consumption of the corresponding irrigation area in the set time period based on the irrigation water consumption and rainfall information of each irrigation area.

[0094] S5: Construct a pump scheduling priority sequence based on the equipment operation information and working environment information of each pump.

[0095] S6: Adjust the operation status of the pumping station based on the actual water consumption and the pump scheduling priority sequence.

[0096] Some of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is the one closest to the actual situation obtained through software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0097] The working principle of the present invention:

[0098] The present invention collects the planting information and rainfall information of the target farmland; obtains the equipment operation information and working environment information of each water pump in the pumping station; divides the target farmland based on the type of currently planted crops to obtain several irrigation areas; inputs the planting information of each irrigation area into the crop water consumption database for matching to obtain the corresponding irrigation water consumption; calculates the actual water consumption of the corresponding irrigation area in the set time period based on the irrigation water consumption and rainfall information of each irrigation area; constructs a water pump scheduling priority sequence based on the equipment operation information and working environment information of each water pump; and adjusts the operation status of the pumping station based on the actual water consumption and the water pump scheduling priority sequence.

[0099] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A control system for a smart pump station, comprising: The data processing module, and the data acquisition module and pump station control module connected thereto are characterized in that: The data acquisition module is used to collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each water pump in the pump station; wherein the planting information includes the type and growth cycle of the currently planted crops; The data processing module is used to divide the target farmland based on the types of crops currently planted to obtain a number of irrigation areas; input the planting information of each irrigation area into the crop water use database for matching to obtain the corresponding irrigation water volume; calculate the actual water volume of the corresponding irrigation area in a set time period based on the irrigation water volume and rainfall information of each irrigation area; and Construct a priority sequence for water pump scheduling based on the equipment operation information and working environment information of each water pump; The pump station control module is used to adjust the operation status of the pump station based on the actual water consumption and the water pump scheduling priority sequence.

2. A control system for a smart pump station according to claim 1, characterized in that: The invention also comprises a crop water use database, wherein the crop water use database contains the water use per unit area of ​​several crop types in different growth cycles.

3. The control system of a smart pump station according to claim 1, characterized in that: The target farmland is divided based on the types of crops currently planted, including: The target farmland is divided into several sub-areas of equal area, and the types of crops currently planted in each sub-area are extracted; it is determined whether the types of crops currently planted in adjacent sub-areas are the same; if yes, the adjacent sub-areas are merged into one irrigation area; if not, the corresponding sub-areas are treated as a separate irrigation area.

4. A control system for a smart pump station according to claim 1, characterized in that: The step of inputting the planting information of each irrigation area into the crop water use database for matching includes: A1: Extract the planting information of each irrigation area; obtain the planting area of ​​each irrigation area; A2: Input the types and growth cycles of the currently planted crops in the planting information into the crop water use database to obtain the water consumption per unit area; A3: Multiply the water consumption per unit area of ​​the crops currently planted in each irrigation area by the corresponding planting area to obtain the irrigation water consumption of each irrigation area.

5. The control system of a smart pump station according to claim 1, characterized in that: The method of calculating the actual water consumption of the corresponding irrigation area in a set time period based on the irrigation water consumption and rainfall information of each irrigation area includes: B1: Extract the irrigation water consumption and rainfall information of each irrigation area in several consecutive cycles; B2: Input the irrigation water consumption of several consecutive periods into the irrigation water consumption prediction model to obtain the irrigation water consumption prediction value of the park; wherein the irrigation water consumption prediction model is constructed based on the artificial intelligence model; B3: With time as the independent variable and irrigation water consumption and rainfall information as the dependent variables, the irrigation water consumption curve fi(t) and rainfall curve gi(t) are generated by curve fitting; where i = 1, 2, ..., n, and n is the total number of irrigation areas; B4: By formula Calculate the actual water consumption SYLi of irrigation area i within the set time period; where t is time, t1 is the start time of the set time period, t2 is the end time of the set time period, and t1≦t≦t2.

6. A control system for a smart pumping station according to claim 5, characterized in that: The irrigation water consumption prediction model is constructed based on an artificial intelligence model and includes: The irrigation water consumption of each irrigation area in several consecutive cycles is extracted and integrated into several groups of training data and test data; the artificial intelligence model is trained using the training data, the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, an irrigation water consumption prediction model is obtained, whose input is the irrigation water consumption of the most recent several consecutive cycles and whose output is the predicted value of the irrigation water consumption in the prediction cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

7. The control system of a smart pump station according to claim 1, characterized in that: The method of constructing a water pump scheduling priority sequence based on the equipment operation information and working environment information of each water pump includes: Extract the equipment operation information and working environment information of each water pump; calculate the wear assessment coefficient of each water pump based on the equipment operation information and working environment information; sort the wear assessment coefficients in ascending order to obtain the water pump scheduling priority sequence.

8. A control system for a smart pump station according to claim 7, characterized in that: The calculation of the wear assessment coefficient of each water pump based on the equipment operation information and the working environment information includes: The equipment operation information and working environment information of each water pump are extracted; the wear assessment coefficient MPXj is calculated through the linear mapping relationship between the sediment content, average workload and accumulated working time in the working environment of the corresponding water pump.

9. A control system for a smart pumping station according to claim 7, characterized in that: The operation status of the pump station is adjusted based on the actual water consumption and the priority sequence of the water pump scheduling, including: C1: Extract the priority sequence of water pump scheduling and the actual water consumption of each irrigation area, calculate the sum of the actual water consumption of each irrigation area and mark it as the estimated total water consumption; C2: Select a number of corresponding water pumps from the priority sequence of water pump scheduling in the order of wear assessment coefficient from small to large, and mark them as water pumps to be activated; calculate the sum of the flow rates of the water pumps to be activated and mark it as the expected water supply flow rate; where the flow rate refers to the volume of water delivered by the water pump per unit time; C3: Calculate the product of the expected water supply flow and the set time period to obtain the expected water supply; C4: Determine whether the expected water supply is less than the expected total water consumption; if yes, continue to select several pumps from the priority sequence of water pump scheduling and mark them as pumps to be activated; if no, set the operating state of the pump to be activated with the largest wear assessment coefficient to be closed; C5: Set the operating status of the water pump to be activated to on; the operating status includes on and off.

10. A control method for a smart pump station, based on the control system of a smart pump station according to any one of claims 1 to 9, characterized in that: include: S1: Collect the planting information and rainfall information of the target farmland; obtain the equipment operation information and working environment information of each water pump in the pump station; S2: Divide the target farmland based on the types of crops currently planted to obtain several irrigation areas; S3: input the planting information of each irrigation area into the crop water use database for matching, and obtain the corresponding irrigation water amount; S4: Calculating the actual water consumption of the corresponding irrigation area in a set time period based on the irrigation water consumption and rainfall information of each irrigation area; S5: constructing a water pump scheduling priority sequence based on the equipment operation information and working environment information of each water pump; S6: Adjust the operating status of the pumping station based on the actual water consumption and the pump scheduling priority sequence.

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