A water conservancy data intelligent analysis system and method based on big data

Abstract

The present invention discloses a water conservancy data intelligent analysis system and method based on big data, belonging to the technical field of data analysis. The present invention classifies irrigation demand into grades, obtains the normal range thresholds of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in water in each grade of irrigation demand; establishes the water flow velocity difference prediction formula of each irrigation equipment in each grade of irrigation demand and the concentration difference prediction formula of impurities in water; the average slope and collection time interval of the water flow velocity difference and the concentration difference of impurities in water in each grade of irrigation demand; judges the operating status of each irrigation equipment according to the real-time irrigation demand, and issues an alarm when an irrigation equipment is abnormal, and dynamically adjusts the collection time interval when the irrigation equipment is operating normally. Timely discover the abnormality of irrigation equipment to avoid continuous collection.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a water conservancy data intelligent analysis system and method based on big data. Background Art

[0002] In the field of water conservancy, irrigation operations are a common means of life. They not only solve the problem of water demand, but also make full use of water resources. However, irrigation requires many irrigation devices to complete the irrigation operations. As the use time increases, the number of abnormalities in the irrigation equipment will also increase. Staff are required to go to the site to check each irrigation device, which makes the efficiency of checking the abnormalities of the irrigation equipment very low, which has a great impact on the irrigation operations. Therefore, it is necessary to remotely monitor the irrigation equipment to detect abnormal equipment in time, provide a reference for the staff to repair the equipment, and avoid checking each irrigation equipment, resulting in a decrease in irrigation efficiency. However, when remotely monitoring the irrigation equipment, if the data of the irrigation equipment is collected all the time, it will lead to excessive consumption, so the time interval for data collection needs to be dynamically adjusted. Summary of the invention

[0003] The purpose of the present invention is to provide a water conservancy data intelligent analysis system and method based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a water conservancy data intelligent analysis system and method based on big data, the method comprising:

[0005] Step S100: for a large-scale irrigation facility, which is composed of a plurality of irrigation equipment, the irrigation demand is divided into grades according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range thresholds of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each grade of irrigation demand;

[0006] Step S200: establishing a water flow velocity difference prediction model for each irrigation device in each level of irrigation demand and a water impurity concentration difference prediction model for each irrigation device in each level of irrigation demand;

[0007] Step S300: Establish a curve chart of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand, obtain the average slope of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand, and the collection time interval of each irrigation device in each level of irrigation demand;

[0008] Step S400: According to the real-time irrigation demand, the operating status of each irrigation device is determined. When an irrigation device is abnormal, an alarm is issued. When the irrigation device is operating normally, the collection time interval is dynamically adjusted.

[0009] Furthermore, step S100 includes:

[0010] Step S101: According to the historical records of the irrigation facilities, the irrigation demand is collected each time, and the average value of the irrigation demand is calculated as A. According to the maximum and minimum values ​​of the irrigation demand, the difference of the irrigation demand is calculated as B. According to the formula: , the number of irrigation demand levels is calculated to be N, and the irrigation demand range of level C is , the irrigation demands that meet the range of irrigation demands of each level are aggregated to obtain the irrigation demand set of each level;

[0011] Step S102: According to each irrigation demand in each level of irrigation demand set, the water flow velocity and the concentration of impurities in the water before and after the i-th irrigation device corresponding to the water demand are collected by a sensor to obtain a set of water flow velocity difference and impurity concentration difference of the i-th irrigation device corresponding to each level of irrigation demand, and then obtain a normal range threshold value of the water flow velocity difference and impurity concentration difference of the i-th irrigation device in each level of irrigation demand;

[0012] First, the irrigation demand is classified according to the historical records of irrigation. Because the range of irrigation demand is too large, if the classification is not performed, the results of subsequent collection and calculation will deviate too much from the actual situation. Therefore, the accuracy of the results will be greatly improved after the classification is performed.

[0013] Secondly, during irrigation operations, there are certain standards for water quality, because unfiltered water contains sediment, suspended matter, organic matter, pathogens and chemical pollutants. The sediment and suspended matter in the water have a greater impact on irrigation, so the water needs to be filtered;

[0014] To judge whether the irrigation equipment is abnormal, we can use the water flow velocity difference and the concentration difference of impurities in the water. Because the water flow velocity difference is the velocity difference obtained after the water passes through the irrigation equipment, the velocity difference of normal operation must be within a range. If an abnormality occurs, the velocity difference will definitely exceed the range. The concentration difference of impurities in the water is because the impurities in the water must be purified after passing through the irrigation equipment. Therefore, the concentration difference of impurities in the water is also a criterion for evaluating whether the irrigation equipment is working normally. In the irrigation equipment, it can be collected by ultrasonic flow meters and online water quality analyzers;

[0015] When the irrigation equipment is working normally according to the same level of irrigation demand, the water flow velocity difference and the concentration difference of impurities in the water are collected to obtain the normal range thresholds of the water flow velocity difference and the concentration difference of impurities in the water. Because of the normal range threshold, whether the irrigation equipment is working normally can be quickly judged, which plays an analytical role in the subsequent judgment of whether the equipment is working normally.

[0016] Further, step S200 includes:

[0017] Step S201: According to the water flow velocity difference set of the i-th irrigation equipment corresponding to the C-th level irrigation demand, the irrigation demand in the C-th level irrigation demand is collected as U C , according to the formula: , where X UCi It is expressed as the predicted value of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, a Ci It is expressed as the first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, b Ci It is expressed as the second weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, and a water flow velocity difference prediction model of the i-th irrigation equipment in the C-th level irrigation demand is established;

[0018] Step S202: The concentration difference set of impurities in the water of the i-th irrigation equipment corresponding to the C-th level irrigation demand is calculated according to the formula: , where Y UCi It is expressed as the predicted value of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, w Ci It is expressed as the first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, r Ci It is expressed as the second weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, and a prediction model for the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is established;

[0019] Step S203: Train the prediction model for the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water in the C-th level irrigation demand, and obtain the new first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand as and the new second weight is , the new first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is and the new second weight is , the prediction formula for the difference in water flow rate and concentration of impurities in water of the i-th irrigation equipment in the C-th level irrigation demand is updated according to the formula: , , where U represents the real-time irrigation demand;

[0020] Because the difference in water flow velocity of irrigation equipment and the difference in impurity concentration in water corresponding to different irrigation demands are different for the same level of irrigation demand, it is necessary to establish a prediction formula for the difference in water flow velocity and the difference in impurity concentration in water for different levels of irrigation demand. The comparison between the real-time difference in water flow velocity and the difference in impurity concentration in water and the prediction provides data support for the subsequent dynamic adjustment of the collection time interval.

[0021] Furthermore, step S300 includes:

[0022] Step S301: according to each irrigation demand in the C-th level irrigation demand, the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water corresponding to the irrigation demand are collected according to a time period, and the water flow velocity difference and the concentration difference of impurities in the water are respectively plotted as curve areas to obtain a curve graph of the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water corresponding to the C-th level irrigation demand;

[0023] Step S302: respectively collect the slopes of the curves of the ith water flow velocity difference and the concentration difference of impurities in water corresponding to the Cth level irrigation demand, and calculate that the average slope of the water flow velocity difference of the ith irrigation equipment in the Cth level irrigation demand is k. Ci1 In the C-level irrigation demand, the average slope of the concentration difference of impurities in the water of the i-th irrigation equipment is k Ci2 ;

[0024] Step S303: Collect the slope sign of the curve of the ith water flow velocity difference and the concentration difference of impurities in water corresponding to the C-th level irrigation demand, collect the time taken for the slope sign to change twice, and calculate the average change period of the water flow velocity difference of the ith irrigation equipment in the C-th level irrigation demand as t Ci1 , the average change period of the concentration difference of impurities in water is t Ci2 , calculate the collection time interval t of the i-th irrigation equipment in the C-th level irrigation demand Ci , according to the formula ;

[0025] The curves of the water flow velocity difference and the concentration difference of impurities in the water corresponding to different levels of irrigation demand are drawn because the water flow velocity difference and the concentration difference of impurities in the water in the irrigation equipment will change with the working time, so drawing the curves can clearly analyze the changing trends of the corresponding water flow velocity difference and the concentration difference of impurities in the water;

[0026] The average slope of the difference in water flow velocity and the difference in concentration of impurities in the water is obtained by analyzing the curve graph. Since the slope is a condition that can be used to analyze the speed of change, the average slope is calculated to provide data support for the subsequent analysis of the speed of change of the difference in water flow velocity and the difference in concentration of impurities in the water corresponding to the irrigation equipment.

[0027] According to the curve chart analysis, the change cycle of the water flow velocity difference and the impurity concentration difference in the water is obtained. Because after a change cycle, the water flow velocity difference and the impurity concentration difference in the water are close to the data at the beginning of the change cycle, the calculated change cycle provides data support for the subsequent time interval for data sampling.

[0028] Furthermore, step S400 includes:

[0029] Step S401: Real-time monitoring of irrigation facilities is performed to obtain real-time irrigation demand, which is compared with the irrigation demand range to obtain the level corresponding to the real-time irrigation demand. If the real-time irrigation demand belongs to the C-th level irrigation demand, the real-time irrigation demand is input into the C-th level irrigation demand, and the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water are used in the prediction model to obtain the predicted value of the water flow velocity difference as X. UCi , the predicted value of the concentration difference of impurities in water is Y UCi ;

[0030] Step S402: The real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are calculated according to t Ci The water flow rate of the i-th irrigation equipment is collected at the time interval of the q-th collection, and the real-time difference is X qi The real-time difference between the concentration of impurities in water and qi , X qi and Y qi The water flow rate difference of the i-th irrigation equipment and the concentration difference of impurities in the water in the C-th level irrigation demand are compared with the normal range thresholds. When the normal range thresholds are exceeded, the i-th irrigation equipment is abnormal and an alarm is issued;

[0031] Step S403: When the normal range threshold is not exceeded, a curve is drawn for the real-time difference of the water flow velocity of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water collected each time, and the slope value of the curve of the real-time difference of the water flow velocity collected for the qth time is k. qi1 The slope value of the real-time difference curve of the concentration of impurities in water is k qi2 , calculate the collection time interval, and get the dynamically adjusted collection time interval t. According to the time interval formula:

[0032] ;

[0033] The real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are collected at a time interval of t, and the time interval is dynamically adjusted through the time interval formula according to the real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water.

[0034] First, the real-time irrigation demand is collected. According to the level corresponding to the real-time irrigation demand, the real-time irrigation demand is input into the prediction formula of the water flow velocity difference and the concentration difference of impurities in the water, and the predicted values ​​of the water flow velocity difference and the concentration difference of impurities in the water are obtained, which provides data support for the subsequent dynamic adjustment of the collection time interval;

[0035] The real-time data of the water flow velocity difference and the concentration difference of impurities in the water are collected at time intervals according to the change cycle, which avoids artificially setting the collection time, resulting in inaccurate real-time data collected, and the accuracy of the dynamic adjustment of the subsequent collection time interval is reduced. The water flow velocity difference and the concentration difference of impurities in the water collected in real time are compared with the normal range threshold to determine whether the irrigation equipment is abnormal. In order to avoid the irrigation equipment having already abnormalities, the collection time interval is still calculated, and the irrigation equipment is maintained in time to avoid affecting the irrigation operation;

[0036] In the time interval formula, if the deviation between the real-time value and the predicted value of the water flow velocity difference and the concentration difference of impurities in the water is too large, it means that the water flow velocity difference and the concentration difference of impurities in the water will exceed the normal range threshold, so the collection time interval needs to be shortened. The relationship between the deviation values ​​between the real-time value and the predicted value can be realized. If the slope of the real-time curve of the water flow velocity difference and the concentration difference of impurities in the water is larger than the average slope, it means that the growth rate of the water flow velocity difference and the concentration difference of impurities in the water is too fast. In order to avoid exceeding the normal threshold range, the collection time interval should be shortened;

[0037] The dynamically adjusted collection interval is calculated according to the time interval formula, and the difference in water flow velocity and the difference in impurity concentration in the water are collected to avoid excessive or untimely collection, which may lead to failure to promptly discover abnormalities in irrigation equipment and reduce irrigation operation efficiency.

[0038] In order to better implement the above method, a water conservancy data intelligent analysis system based on big data is also proposed. The system includes a normal threshold module, a prediction model module, a curve chart module and a real-time monitoring module;

[0039] Normal threshold module: for a large irrigation facility, which is composed of several irrigation equipment, the irrigation demand is divided into grades according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range threshold of the difference in water flow velocity and the difference in concentration of impurities in the water for each irrigation equipment in each grade of irrigation demand;

[0040] Prediction model module: according to the water flow velocity difference of each irrigation equipment in each level of irrigation demand and the concentration difference of impurities in the water, a water flow velocity difference prediction model and a water impurity concentration difference prediction model for each irrigation equipment in each level of irrigation demand are established;

[0041] Curve chart module: establish a curve chart of the water flow velocity difference and the concentration difference of impurities in the water for each irrigation device in each level of irrigation demand, obtain the average slope of the water flow velocity difference and the concentration difference of impurities in the water for each irrigation device in each level of irrigation demand, and the collection time interval of each irrigation device in each level of irrigation demand;

[0042] Real-time monitoring module: judge the operating status of each irrigation equipment according to the real-time irrigation demand. When an irrigation equipment is abnormal, an alarm will be issued. When the irrigation equipment is operating normally, the collection time interval will be dynamically adjusted.

[0043] Further, the normal threshold module includes an irrigation demand grading unit and a normal range threshold calculation unit;

[0044] Irrigation demand grading unit: collects irrigation demand based on historical records of irrigation facilities, obtains the number of irrigation demand levels through calculation, and establishes different levels of irrigation demand based on the number of irrigation demand levels;

[0045] Calculate the normal range threshold unit: according to the normal operation, in each level of irrigation demand, the water flow velocity difference of different equipment and the concentration difference of impurities in the water corresponding to each irrigation demand are collected to obtain the normal range threshold of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand.

[0046] Further, the prediction model module includes a prediction model establishment unit and a prediction model training unit;

[0047] Establishing a prediction model unit: according to the water flow velocity difference of different equipment and the concentration difference of impurities in water corresponding to each irrigation demand at each level of irrigation demand, a prediction model of the water flow velocity difference and the concentration difference of impurities in water for each irrigation equipment at each level of irrigation demand is established;

[0048] Training prediction model unit: training prediction models for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand, obtaining new first and second weights for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand, and updating prediction models for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand.

[0049] Further, the curve graph module includes a curve graph drawing unit and a curve graph analysis unit;

[0050] Curve drawing unit: according to a time period, the water flow rate difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand are collected, and a curve diagram of the water flow rate difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand is established;

[0051] Analysis curve chart unit: According to the curve chart of the water flow rate difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, the slope is collected to obtain the average slope of the water flow rate difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, and the collection time interval is obtained according to the change of the slope.

[0052] Furthermore, the real-time monitoring module includes an abnormality monitoring unit and a dynamic adjustment unit;

[0053] Abnormal monitoring unit: According to the real-time irrigation demand, the corresponding level of the real-time irrigation demand is obtained, and the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water are collected at time intervals, and the real-time values ​​of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation device are compared with the normal range threshold. When the normal range threshold is exceeded, the irrigation device is abnormal and an alarm is issued;

[0054] Dynamic adjustment unit: When the threshold value of the normal range is not exceeded, the dynamically adjusted collection time interval is calculated according to the time interval formula, and collection is performed according to the dynamically adjusted collection time interval.

[0055] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: by classifying the irrigation demand, the normal range thresholds of the water flow velocity difference and the concentration difference of impurities in water of each irrigation equipment in each level of irrigation demand are obtained. This method performs graded processing on the irrigation demand to avoid excessive range of irrigation demand, which leads to a decrease in the accuracy of subsequent calculations; a water flow velocity difference prediction formula and a water impurity concentration difference prediction formula for each irrigation equipment in each level of irrigation demand are established. This method calculates the difference by different irrigation demand, which provides data support for the subsequent adjustment of the collection time interval; the average slope and collection time interval of the water flow velocity difference and the concentration difference of impurities in water of each irrigation equipment in each level of irrigation demand, this method calculates the collection time interval by drawing a curve chart, and avoids the decrease in the accuracy of the calculation result caused by manually setting the collection time interval; according to the real-time irrigation demand, the operating status of each irrigation equipment is judged, when the irrigation equipment is abnormal, an alarm is issued, and when the irrigation equipment is operating normally, the collection time interval is dynamically adjusted. This method can timely discover the abnormality of the irrigation equipment by judging whether the irrigation equipment is abnormal, and can avoid continuous collection and data waste by dynamically adjusting the collection time interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 It is a flow chart of a method for intelligent analysis of water conservancy data based on big data according to the present invention;

[0058] Figure 2 It is a structural schematic diagram of a water conservancy data intelligent analysis system based on big data in the present invention. DETAILED DESCRIPTION

[0059] See also Figure 1 and Figure 2 The present invention provides a technical solution: a method for intelligent analysis of water conservancy data based on big data, the method comprising:

[0060] Step S100: for a large-scale irrigation facility, which is composed of a plurality of irrigation equipment, the irrigation demand is divided into grades according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range thresholds of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each grade of irrigation demand;

[0061] Wherein, step S100 includes:

[0062] Step S101: According to the historical records of the irrigation facilities, the irrigation demand is collected each time, and the average value of the irrigation demand is calculated as A. According to the maximum and minimum values ​​of the irrigation demand, the difference of the irrigation demand is calculated as B. According to the formula: , the number of irrigation demand levels is calculated to be N, and the irrigation demand range of level C is , the irrigation demands that meet the range of irrigation demands of each level are aggregated to obtain the irrigation demand set of each level;

[0063] Step S102: According to each irrigation demand in each level of irrigation demand set, the corresponding water flow velocity flowing through the i-th irrigation equipment and the concentration of impurities in the water are collected through sensors to obtain a set of water flow velocity difference and impurity concentration difference of the i-th irrigation equipment corresponding to each level of irrigation demand, and then obtain the normal range threshold value of the water flow velocity difference and impurity concentration difference of the i-th irrigation equipment in each level of irrigation demand.

[0064] For example, the maximum value of irrigation demand is 110, the minimum value is 20, and the average value is 30. The calculated irrigation demand level is 3;

[0065] The first level irrigation demand range is [20,50), the second level irrigation demand range is [50,80), and the third level irrigation demand range is [80,110].

[0066] Step S200: establishing a water flow velocity difference prediction model for each irrigation device in each level of irrigation demand and a water impurity concentration difference prediction model for each irrigation device in each level of irrigation demand;

[0067] Wherein, step S200 includes:

[0068] Step S201: According to the water flow velocity difference set of the i-th irrigation equipment corresponding to the C-th level irrigation demand, the irrigation demand in the C-th level irrigation demand is collected as U C , according to the formula: , where X UCi It is expressed as the predicted value of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, a Ci It is expressed as the first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, b Ci It is expressed as the second weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, and a water flow velocity difference prediction model of the i-th irrigation equipment in the C-th level irrigation demand is established;

[0069] Step S202: The concentration difference set of impurities in the water of the i-th irrigation equipment corresponding to the C-th level irrigation demand is calculated according to the formula: , where Y UCi It is expressed as the predicted value of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, w Ci It is expressed as the first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, r Ci It is expressed as the second weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, and a prediction model for the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is established;

[0070] Step S203: Train the prediction model for the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water in the C-th level irrigation demand, and obtain the new first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand as and the new second weight is , the new first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is and the new second weight is , the prediction formula for the difference in water flow rate and concentration of impurities in water of the i-th irrigation equipment in the C-th level irrigation demand is updated according to the formula: , , where U represents the real-time irrigation demand.

[0071] Step S300: Establish a curve chart of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand, obtain the average slope of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand, and the collection time interval of each irrigation device in each level of irrigation demand;

[0072] Wherein, step S300 includes:

[0073] Step S301: according to each irrigation demand in the C-th level irrigation demand, the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water corresponding to the irrigation demand are collected according to a time period, and the water flow velocity difference and the concentration difference of impurities in the water are respectively plotted as curve areas to obtain a curve graph of the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water corresponding to the C-th level irrigation demand;

[0074] Step S302: respectively collect the slopes of the curves of the ith water flow velocity difference and the concentration difference of impurities in water corresponding to the Cth level irrigation demand, and calculate that the average slope of the water flow velocity difference of the ith irrigation equipment in the Cth level irrigation demand is k. Ci1 In the C-level irrigation demand, the average slope of the concentration difference of impurities in the water of the i-th irrigation equipment is k Ci2 ;

[0075] Step S303: Collect the slope sign of the curve of the ith water flow velocity difference and the concentration difference of impurities in water corresponding to the C-th level irrigation demand, collect the time taken for the slope sign to change twice, and calculate the average change period of the water flow velocity difference of the ith irrigation equipment in the C-th level irrigation demand as t Ci1 , the average change period of the concentration difference of impurities in water is t Ci2 , calculate the collection time interval t of the i-th irrigation equipment in the C-th level irrigation demand Ci , according to the formula .

[0076] For example, the time taken for the slope sign of the curve graph of the water flow velocity difference of the first irrigation equipment in the first level irrigation demand to change twice is 7, 8, and 12, and the average change period is 9;

[0077] The time taken for the slope sign of the curve graph of the concentration difference of impurities in the water of the ith irrigation equipment in the first-level irrigation demand to change twice is 4, 8, and 3, and the average change period is 5;

[0078] The collection time interval of the first irrigation equipment in the first level irrigation demand is 7.

[0079] Step S400: judging the operating status of each irrigation device according to the real-time irrigation demand, and giving an alarm when an irrigation device is abnormal, and dynamically adjusting the collection time interval when the irrigation device is operating normally;

[0080] Wherein, step S400 includes:

[0081] Step S401: Real-time monitoring of irrigation facilities is performed to obtain real-time irrigation demand, which is compared with the irrigation demand range to obtain the level corresponding to the real-time irrigation demand. If the real-time irrigation demand belongs to the C-th level irrigation demand, the real-time irrigation demand is input into the C-th level irrigation demand, and the water flow velocity difference of the i-th irrigation equipment and the concentration difference of impurities in the water are used in the prediction model to obtain the predicted value of the water flow velocity difference as X. UCi , the predicted value of the concentration difference of impurities in water is Y UCi ;

[0082] For example, the real-time irrigation demand is collected as 30, which meets the first-level irrigation demand. 30 is input into the prediction formula of the water flow velocity difference and the concentration difference of impurities in the water for the first irrigation equipment in the first-level irrigation demand. The predicted value of the water flow velocity difference is calculated to be 4, and the predicted value of the concentration difference of impurities in the water is 3.

[0083] Step S402: The real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are calculated according to t Ci The water flow rate of the i-th irrigation equipment is collected at the time interval of the q-th collection, and the real-time difference is X qi The real-time difference between the concentration of impurities in water and qi , X qi and Y qi The water flow rate difference of the i-th irrigation equipment and the concentration difference of impurities in the water in the C-th level irrigation demand are compared with the normal range thresholds. When the normal range thresholds are exceeded, the i-th irrigation equipment is abnormal and an alarm is issued;

[0084] For example, when data is collected at a collection time interval of 7, the real-time difference in water flow rate of the first irrigation device is 2, and the real-time difference in concentration of impurities in the water is 1.5, which does not exceed the normal range threshold.

[0085] Step S403: When the normal range threshold is not exceeded, a curve is drawn for the real-time difference of the water flow velocity of the i-th irrigation device collected each time and the real-time difference of the concentration of impurities in the water, and the slope value of the curve of the real-time difference of the water flow velocity collected for the qth time is k. qi1 The slope value of the real-time difference curve of the concentration of impurities in water is k qi2 , calculate the collection time interval, and get the dynamically adjusted collection time interval t. According to the time interval formula:

[0086] ;

[0087] The real-time difference in water flow rate of the ith irrigation equipment and the real-time difference in the concentration of impurities in the water are collected at time intervals of t. The time interval is dynamically adjusted through the time interval formula based on the real-time difference in water flow rate of the ith irrigation equipment and the real-time difference in the concentration of impurities in the water.

[0088] For example, the dynamically adjusted collection time interval is calculated to be 5.5.

[0089] In order to better implement the above method, a water conservancy data intelligent analysis system based on big data is also proposed. The system includes a normal threshold module, a prediction model module, a curve chart module and a real-time monitoring module;

[0090] Normal threshold module: for a large irrigation facility, which is composed of several irrigation equipment, the irrigation demand is divided into grades according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range threshold of the difference in water flow velocity and the difference in concentration of impurities in the water for each irrigation equipment in each grade of irrigation demand;

[0091] Among them, the normal threshold module includes an irrigation demand classification unit and a normal range threshold calculation unit;

[0092] Irrigation demand grading unit: collects irrigation demand based on historical records of irrigation facilities, obtains the number of irrigation demand levels through calculation, and establishes different levels of irrigation demand based on the number of irrigation demand levels;

[0093] Calculate the normal range threshold unit: according to the normal operation, in each level of irrigation demand, the water flow velocity difference of different equipment and the concentration difference of impurities in the water corresponding to each irrigation demand are collected to obtain the normal range threshold of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand.

[0094] Prediction model module: according to the water flow velocity difference of each irrigation equipment in each level of irrigation demand and the concentration difference of impurities in the water, a water flow velocity difference prediction model and a water impurity concentration difference prediction model for each irrigation equipment in each level of irrigation demand are established;

[0095] Among them, the prediction model module includes a prediction model establishment unit and a prediction model training unit;

[0096] Establishing a prediction model unit: according to the water flow velocity difference of different equipment and the concentration difference of impurities in water corresponding to each irrigation demand at each level of irrigation demand, a prediction model of the water flow velocity difference and the concentration difference of impurities in water for each irrigation equipment at each level of irrigation demand is established;

[0097] Training prediction model unit: training prediction models for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand, obtaining new first and second weights for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand, and updating prediction models for water flow velocity difference and impurity concentration difference of each irrigation equipment in each level of irrigation demand.

[0098] Curve chart module: establish a curve chart of the water flow velocity difference and the concentration difference of impurities in the water for each irrigation device in each level of irrigation demand, obtain the average slope of the water flow velocity difference and the concentration difference of impurities in the water for each irrigation device in each level of irrigation demand, and the collection time interval of each irrigation device in each level of irrigation demand;

[0099] Wherein, the curve graph module includes a curve graph drawing unit and a curve graph analysis unit;

[0100] Curve drawing unit: according to a time period, the water flow rate difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand are collected, and a curve diagram of the water flow rate difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand is established;

[0101] Analysis curve chart unit: According to the curve chart of the water flow rate difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, the slope is collected to obtain the average slope of the water flow rate difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, and the collection time interval is obtained according to the change of the slope.

[0102] Real-time monitoring module: judge the operating status of each irrigation device according to the real-time irrigation demand. When an irrigation device is abnormal, an alarm is issued. When the irrigation device is operating normally, the collection time interval is dynamically adjusted.

[0103] Among them, the real-time monitoring module includes an abnormal monitoring unit and a dynamic adjustment unit;

[0104] Abnormal monitoring unit: According to the real-time irrigation demand, the corresponding level of the real-time irrigation demand is obtained, and the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water are collected at time intervals, and the real-time values ​​of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation device are compared with the normal range threshold. When the normal range threshold is exceeded, the irrigation device is abnormal and an alarm is issued;

[0105] Dynamic adjustment unit: When the threshold value of the normal range is not exceeded, the dynamically adjusted collection time interval is calculated according to the time interval formula, and collection is performed according to the dynamically adjusted collection time interval.

[0106] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0107] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent analysis of water conservancy data based on big data, characterized in that: Methods include: Step S100: for a large-scale irrigation facility, which is composed of a plurality of irrigation equipment, the irrigation demand is divided into grades according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range thresholds of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each grade of irrigation demand; The obtaining of the normal range thresholds of the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand comprises: according to the normal working conditions, the water flow velocity difference of different irrigation devices and the concentration difference of impurities in the water corresponding to each irrigation demand in each level of irrigation demand are collected to obtain the normal range thresholds of the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand; Step S200: establishing a water flow velocity difference prediction model for each irrigation device in each level of irrigation demand and a water impurity concentration difference prediction model for each irrigation device in each level of irrigation demand; In S200, the specific steps are as follows: Step S201: According to the water flow velocity difference set of the i-th irrigation equipment corresponding to the C-th level irrigation demand, the irrigation demand in the C-th level irrigation demand is collected as U C , according to the formula: , where X UCi It is expressed as the predicted value of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, a Ci It is expressed as the first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, b Ci It is expressed as the second weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand, and a water flow velocity difference prediction model of the i-th irrigation equipment in the C-th level irrigation demand is established; Step S202: The concentration difference set of impurities in the water of the i-th irrigation equipment corresponding to the C-th level irrigation demand is calculated according to the formula: , where Y UCi It is expressed as the predicted value of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, w Ci It is expressed as the first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, r Ci It is expressed as the second weight of the concentration difference of impurities in the water of the ith irrigation equipment in the Cth level irrigation demand, and a prediction model for the concentration difference of impurities in the water of the ith irrigation equipment in the Cth level irrigation demand is established; Step S203: training the prediction model of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand and the concentration difference of impurities in the water, and obtaining a new first weight of the water flow velocity difference of the i-th irrigation equipment in the C-th level irrigation demand as and the new second weight is , the new first weight of the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is and the new second weight is , the prediction formula for the difference in water flow rate and concentration of impurities in water of the i-th irrigation equipment in the C-th level irrigation demand is updated as follows: , , where U represents the real-time irrigation demand; Step S300: Establish a curve chart of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand, and obtain the average slope of the water flow rate difference and the concentration difference of impurities in water for each irrigation device in each level of irrigation demand and the collection time interval of each irrigation device in each level of irrigation demand; In S300, the specific steps are as follows: Step S301: according to the water flow velocity difference of the ith irrigation equipment and the concentration difference of impurities in the water corresponding to each irrigation demand in the C-th level irrigation demand, data are collected according to a time period, and the water flow velocity difference and the concentration difference of impurities in the water are respectively plotted as curve areas to obtain a curve graph of the water flow velocity difference of the ith irrigation equipment and the concentration difference of impurities in the water corresponding to the C-th level irrigation demand; Step S302: respectively collect the slopes of the curves of the ith water flow velocity difference and the water impurity concentration difference corresponding to the C-th level irrigation demand, and calculate the average slope of the water flow velocity difference of the ith irrigation equipment in the C-th level irrigation demand as k. Ci1 The average slope of the difference in concentration of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand is k Ci2 ; Step S303: Collect the slope sign of the curve graph of the ith water flow velocity difference and the concentration difference of impurities in water corresponding to the C-th level irrigation demand, collect the time taken for the slope sign to change twice, and calculate the average change period of the water flow velocity difference of the ith irrigation equipment in the C-th level irrigation demand as t Ci1 , the average change period of the concentration difference of impurities in water is t Ci2 , calculate the collection time interval t of the i-th irrigation equipment in the C-th level irrigation demand Ci , according to the formula ; Step S400: judging the operating status of each irrigation device according to the real-time irrigation demand, and giving an alarm when an irrigation device is abnormal, and dynamically adjusting the collection time interval when the irrigation device is operating normally; In S400, the specific steps are as follows: Step S401: Real-time monitoring of irrigation facilities is performed to obtain real-time irrigation demand, which is compared with the irrigation demand range to obtain the level corresponding to the real-time irrigation demand. If the real-time irrigation demand belongs to the C-th level irrigation demand, the real-time irrigation demand is input into the prediction model of the water flow velocity difference and the concentration difference of impurities in the water of the i-th irrigation equipment in the C-th level irrigation demand, and the predicted value of the water flow velocity difference is obtained as X. UCi , the predicted value of the concentration difference of impurities in water is Y UCi ; Step S402: The real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are calculated according to t Ci The water flow rate of the i-th irrigation equipment is collected at the time interval of the q-th collection, and the real-time difference is X qi The real-time difference between the concentration of impurities in water and qi , X qi and Y qi The water flow rate difference of the i-th irrigation equipment and the concentration difference of impurities in the water in the C-th level irrigation demand are compared with the normal range thresholds. When the normal range thresholds are exceeded, the i-th irrigation equipment is abnormal and an alarm is issued; Step S403: When the normal range threshold is not exceeded, a curve is drawn for the real-time difference of the water flow velocity of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water collected each time, and the slope value of the curve of the real-time difference of the water flow velocity collected for the qth time is k. qi1 The slope value of the real-time difference curve of the concentration of impurities in water is k qi2 , calculate the collection time interval, and get the dynamically adjusted collection time interval t. According to the time interval formula: ； The real-time difference of the water flow rate of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are collected at time intervals of t.

2. A water conservancy data intelligent analysis system based on big data, used to implement the water conservancy data intelligent analysis method based on big data described in claim 1, characterized in that: Normal threshold module, prediction model module, curve chart module and real-time monitoring module; The normal threshold module: for a large-scale irrigation facility, which is composed of a plurality of irrigation equipment, the irrigation demand is graded according to the historical records of the irrigation facility, and the water flow velocity and the concentration of impurities in the water before and after each irrigation equipment are collected to obtain the normal range threshold of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each grade of irrigation demand; The prediction model module: establishes a prediction model for the difference in water flow rate of each irrigation device in each level of irrigation demand and a prediction model for the difference in concentration of impurities in the water according to the difference in water flow rate of each irrigation device in each level of irrigation demand and the difference in concentration of impurities in the water; The curve graph module: establishes a curve graph of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, obtains the average slope of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in the water in each level of irrigation demand, and the collection time interval of each irrigation equipment in each level of irrigation demand; The real-time monitoring module determines the operating status of each irrigation device according to the real-time irrigation demand. When an irrigation device is abnormal, an alarm is issued. When the irrigation device is operating normally, the collection time interval is dynamically adjusted.

3. The water conservancy data intelligent analysis system based on big data according to claim 2 is characterized in that: The normal threshold module includes an irrigation demand grading unit and a normal range threshold calculation unit; The irrigation demand grading unit collects irrigation demand according to the historical records of the irrigation facilities, obtains the number of levels of irrigation demand by calculation, and establishes different levels of irrigation demand according to the number of levels of irrigation demand; The normal range threshold value calculating unit is: based on the normal working state, the water flow velocity difference of different irrigation equipment corresponding to each irrigation demand at each level of irrigation demand and the concentration difference of impurities in the water are collected to obtain the normal range threshold value of the water flow velocity difference of each irrigation equipment and the concentration difference of impurities in the water at each level of irrigation demand.

4. The water conservancy data intelligent analysis system based on big data according to claim 2 is characterized in that: The prediction model module includes a prediction model establishment unit and a prediction model training unit; The prediction model establishment unit: establishes a prediction model for the difference in water flow rate and the difference in concentration of impurities in water for each irrigation device in each level of irrigation demand according to the difference in water flow rate of different devices and the difference in concentration of impurities in water corresponding to each irrigation demand in each level of irrigation demand; The training prediction model unit: trains the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each level of irrigation demand, obtains the new first weight and the new second weight of the water flow velocity difference of each irrigation equipment in each level of irrigation demand, obtains the new first weight and the new second weight of the concentration difference of impurities in the water of each irrigation equipment in each level of irrigation demand, and updates the water flow velocity difference and the concentration difference of impurities in the water of each irrigation equipment in each level of irrigation demand.

5. The water conservancy data intelligent analysis system based on big data according to claim 2 is characterized in that: The curve graph module includes a curve graph drawing unit and a curve graph analysis unit; The curve drawing unit collects the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand according to a time period, and establishes a curve diagram of the water flow velocity difference of each irrigation device and the concentration difference of impurities in the water in each level of irrigation demand; The analysis curve chart unit collects the slopes of the curve charts of the water flow velocity difference and the impurity concentration difference of each irrigation equipment in each level of irrigation demand, and obtains the average slope of the water flow velocity difference and the impurity concentration difference of each irrigation equipment in each level of irrigation demand and the collection time interval of each irrigation equipment in each level of irrigation demand.

6. The water conservancy data intelligent analysis system based on big data according to claim 2 is characterized in that: The real-time monitoring module includes an abnormality monitoring unit and a dynamic adjustment unit; The abnormal monitoring unit: obtains the level corresponding to the real-time irrigation demand according to the real-time irrigation demand, and calculates the water flow velocity difference and the concentration difference of impurities in the water of each irrigation device according to t ci The data is collected at time intervals, and the real-time values ​​of the water flow velocity difference and the concentration difference of impurities in the water of each irrigation device are compared with the normal range threshold. When the normal range threshold is exceeded, the irrigation device is abnormal and an alarm is issued; The dynamic adjustment unit: when the normal range threshold is not exceeded, the dynamically adjusted collection time interval t is calculated according to the time interval formula, and the real-time difference of the water flow velocity of the i-th irrigation equipment and the real-time difference of the concentration of impurities in the water are collected at the time interval t.

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