Smart reservoir management method based on big data

Abstract

The invention relates to the field of hydraulic engineering, in particular to a smart reservoir management method based on big data, which comprises the following steps: S1, monitoring the water quality of a reservoir; s2, reservoir water quality analysis; s3, predicting the water level of the reservoir; s4, water scheduling analysis; s5, dam safety monitoring; s6, equipment state monitoring; according to the method, the pollution time periods of all the detection points of the reservoir are screened by analyzing the water quality parameters of the water samples at all the time points and all the detection points of the reservoir, the water delivery amount of all the areas and all the time periods is analyzed by obtaining the water consumption amount of all the areas and all the time periods, and then water delivery is conducted on all the areas in all the time periods; according to the maximum water level prediction value of each future time point of the reservoir, the safety evaluation coefficient of the reservoir dam and the state evaluation coefficient of the reservoir equipment, the safety comprehensive evaluation index of the reservoir is obtained through analysis and fed back, the safety condition of the reservoir can be comprehensively evaluated, and potential risks and problems can be found in time.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy projects, and in particular to a smart reservoir management method based on big data. Background Art

[0002] In today's era, with the rapid development of science and technology, the concept of "smart reservoir" has come into being.

[0003] As an important water conservancy infrastructure, reservoirs play a vital role in flood control, water supply, irrigation, power generation, etc. However, the traditional reservoir management model has problems such as low efficiency, untimely information, and unscientific decision-making.

[0004] In this context, the emergence of smart reservoirs is of great significance. With the help of advanced information technologies, such as the Internet of Things, big data, and artificial intelligence, smart reservoirs can realize real-time monitoring and accurate analysis of various reservoir data. Through intelligent monitoring systems, key information such as the reservoir's water level, water volume, and water quality can be grasped in real time, and potential risks and problems can be discovered in a timely manner.

[0005] For example, the existing Chinese patent application number 201710087916.9 discloses a reservoir management system. When the water level meter detects that the water level upstream of the gate is higher than the maximum water level, the industrial computer transmits the data to the control console and receives the operation command, controls the frequency converter to output the corresponding power signal to let the winch open the gate, and controls the gate opening according to the gate level meter data; when the water level is lower than the minimum water level, the frequency converter is controlled to let the winch close the gate. At the same time, the water level meter can monitor the rising rate of the reservoir water level. If it is higher than the maximum limit rate, the industrial computer will send an alarm signal to the control console for warning, so as to achieve reasonable regulation and utilization of water resources.

[0006] However, the above patent has the following problems: First, the solution only controls the gate opening according to the water level, and does not involve water use scheduling. It cannot reasonably allocate water resources according to the water demand in different regions and different time periods. It may lead to water shortage in some areas and waste of water resources in other areas, and it is impossible to achieve efficient use and optimal allocation of water resources.

[0007] Second, the plan does not involve fault analysis of reservoir-related equipment, such as dam displacement and stress, vibration frequency of reservoir equipment, etc., which may prevent some potential equipment failures from being detected and prevented in advance, increasing the risk of system operation. Summary of the invention

[0008] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides a smart reservoir management method based on big data, which can effectively solve the problems involved in the above-mentioned background technology.

[0009] The purpose of the present invention can be achieved through the following technical solutions: a smart reservoir management method based on big data, the method comprising the following steps: S1. Reservoir water quality monitoring: testing the water quality parameters of water samples at each testing point at each time point in the reservoir, the water quality parameters including pH, dissolved oxygen concentration, and turbidity.

[0010] S2. Reservoir water quality analysis: Based on the water quality parameter analysis of the water samples at each testing point at each time point in the reservoir, the water quality evaluation coefficient of the water samples at each testing point at each time point in the reservoir is obtained, and then the pollution time period of each testing point in the reservoir is analyzed and feedback is given.

[0011] S3. Reservoir water level prediction: obtain the water level of each detection point in the reservoir at each historical time point, the local rainfall and temperature of the reservoir at each historical time point, and analyze to obtain the predicted maximum water level of the reservoir at each future time point.

[0012] S4. Water use scheduling analysis: Obtain the water consumption in each area in each time period, and combine the real-time deliverable water volume of the reservoir to analyze the water delivery volume in each area in each time period, and then deliver water to each area in each time period.

[0013] S5. Dam safety monitoring: Test the safety parameters of each dam test point and analyze to obtain the safety evaluation factor of the reservoir dam. The safety parameters include displacement and stress compliance.

[0014] S6. Equipment status monitoring: Detect the status parameters of each device in the reservoir and analyze them to obtain the status evaluation coefficient of the reservoir equipment. The status parameters include temperature, vibration frequency, and maintenance time interval at each monitoring time point.

[0015] S7. Reservoir safety analysis: predict the maximum value of the reservoir water level at each future time point Safety evaluation coefficient ω of reservoir dam and status evaluation coefficient of reservoir equipment The comprehensive safety evaluation index of the reservoir is obtained through analysis, where u represents the number of the u-th future time point, u=1,2,...,v, and feedback is given.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. The present invention obtains the water quality evaluation coefficient of the water samples at each testing point at each time point in the reservoir based on the water quality parameter analysis of the water samples at each testing point at each time point in the reservoir, and then analyzes to obtain the pollution time period of each testing point in the reservoir, and provides feedback thereon, so as to timely and accurately understand the water quality status of the reservoir at different time points and testing points, and help to take targeted treatment and protection measures, improve the water quality of the reservoir, and ensure the safety and sustainable use of water resources.

[0017] Second, the present invention obtains the water level of each detection point of the reservoir at each historical time point, the local rainfall and temperature of the reservoir at each historical time point, and analyzes the predicted maximum water level of the reservoir at each future time point, which is conducive to the rational planning and scheduling of water resources. Preparations can be made in advance to deal with different water level conditions, such as rationally allocating water resources during droughts and taking flood prevention measures during floods, etc., to ensure the normal operation of the reservoir and the safety of the surrounding areas.

[0018] 3. The present invention obtains the water consumption in each area in each time period, and analyzes the real-time deliverable water volume of the reservoir to obtain the water delivery volume in each area in each time period, and then delivers water to each area in each time period, which can achieve optimal allocation of water resources, ensure that the water demand of each area is met, improve the utilization efficiency of water resources, and avoid waste or unreasonable allocation of water resources.

[0019] 4. The present invention analyzes and obtains the comprehensive safety evaluation index of the reservoir based on the predicted maximum water level at each future time point of the reservoir, the safety evaluation coefficient of the reservoir dam, and the status evaluation coefficient of the reservoir equipment, and provides feedback thereon, which is helpful to comprehensively evaluate the safety status of the reservoir and promptly discover potential risks and problems so as to take corresponding maintenance, reinforcement or improvement measures to ensure the safe and stable operation of the reservoir dam and related equipment and to ensure the normal functioning of the reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0021] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 for Figure 1 Schematic diagram of the method flow chart of step S2 in FIG.

[0023] Figure 3 for Figure 1 Schematic diagram of the method flow of step S4 in FIG. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1As shown, the present invention provides a smart reservoir management method based on big data, which includes the following steps: S1. Reservoir water quality monitoring: testing the water quality parameters of water samples at each testing point at each time point in the reservoir, and the water quality parameters include pH, dissolved oxygen concentration, and turbidity.

[0026] See also Figure 2 As shown, the specific analysis method for reservoir water quality monitoring is: the first step is to select a number of detection points in the reservoir according to the set interval, and at the same time select a number of time points according to the set interval duration, record them as each time point, and collect a set amount of water samples from each detection point in the reservoir at each time point, record them as water samples of each detection point at each time point, and use a PH sensor to detect the pH of the water samples of each detection point at each time point to obtain the pH of the water samples of each detection point at each time point in the reservoir; this helps to grasp the acid-base distribution law of reservoir water quality, promptly discover abnormal acid-base changes, and provide basic data for subsequent water quality analysis and management, which is of great significance for maintaining the ecological balance of the reservoir and ensuring water safety.

[0027] The second step is to connect the dissolved oxygen meter, insert the dissolved oxygen electrodes into the water samples at each test point at each time point, and read the dissolved oxygen concentration of the water samples at each test point at each time point in the reservoir through the dissolved oxygen meter; this helps to assess the ecological health of the reservoir, and plays a key role in the protection of aquatic organisms, the stability of the water ecosystem, and determining whether the water body may suffer from hypoxia.

[0028] The third step is to detect the turbidity of water samples at each testing point at each time point through a turbidity sensor to obtain the turbidity of water samples at each testing point at each time point in the reservoir; this has guiding significance for judging the cleanliness of water quality, possible pollution, and the selection and adjustment of subsequent water treatment processes. It is also related to the landscape function of the reservoir and the utilization value of water resources.

[0029] S2. Reservoir water quality analysis: Based on the water quality parameter analysis of the water samples at each testing point at each time point in the reservoir, the water quality evaluation coefficient of the water samples at each testing point at each time point in the reservoir is obtained, and then the pollution time period of each testing point in the reservoir is analyzed and feedback is given.

[0030] The specific analysis method of the reservoir water quality analysis is as follows: the first step is to read the pH, dissolved oxygen concentration, and turbidity of the water samples at each detection point in the reservoir at each time point, respectively recorded as pH im , im , im , where i represents the number of the i-th time point, i=1,2,...,n, and m represents the number of the m-th detection point, m=1,2,...,q. Substitute it into the formula The water quality evaluation coefficient χ of the water samples at each detection point in the reservoir at each time point is obtained im, where PH', ο', ξ' represent the preset reference values ​​of pH, dissolved oxygen concentration, and turbidity, respectively, 1 ,φ 2 ,φ 3 They represent the preset weighting factors of pH, dissolved oxygen concentration, and turbidity respectively; they can intuitively compare the water quality conditions at different times and test points, help to quickly understand the quality of water and provide a basis for subsequent analysis and decision-making.

[0031] It should be noted that, in a specific embodiment, φ 1 Can be set to 0.4, φ 2 Can be set to 0.4, φ 3 It can be set to 0.2. Dissolved oxygen is extremely critical to the survival of aquatic organisms and the stability of the ecosystem. The respiration of aquatic organisms depends on sufficient dissolved oxygen. Insufficient dissolved oxygen will seriously affect biodiversity and ecological balance, and it can intuitively reflect the self-purification ability and health of the water body; the appropriate pH range has an important influence on the growth, development, reproduction of aquatic organisms, and the existence form and reaction of chemical substances in the water body. An overly acidic or alkaline environment will cause harm to aquatic organisms and affect the chemical properties of the water body; turbidity mainly affects the transparency and sensory perception of the water body. Although it is not as directly related to the survival and chemical properties of organisms as dissolved oxygen and pH, excessive turbidity will affect the photosynthesis of aquatic plants, the vision and foraging of organisms such as fish, and may also indicate abnormal input of substances such as sediment. Therefore, the weights corresponding to pH and dissolved oxygen concentration are relatively high.

[0032] In the second step, adjacent time points are grouped as adjacent time point groups. The water quality evaluation coefficient difference of water samples at each detection point in each adjacent time point group of the reservoir is obtained by subtracting the water quality evaluation coefficient of water samples at each detection point in the adjacent time point group of the reservoir. The water quality evaluation coefficient difference of water samples at each detection point in each adjacent time point group of the reservoir is compared with the preset water quality evaluation coefficient difference threshold. If the water quality evaluation coefficient difference of water samples at a certain detection point in a certain adjacent time point group of the reservoir is greater than or equal to the preset water quality evaluation coefficient difference threshold, the time point group is recorded as the pollution time period, and the pollution time periods of each detection point in the reservoir are counted and feedback is given. Sudden changes in water quality can be discovered in a timely manner, and time periods and areas where pollution may occur can be accurately located, so as to take targeted measures for governance and intervention, improve the monitoring and response efficiency of reservoir water quality changes, and ensure the safety and stability of reservoir water quality.

[0033] S3. Reservoir water level prediction: obtain the water level of each detection point in the reservoir at each historical time point, the local rainfall and temperature of the reservoir at each historical time point, and analyze to obtain the predicted maximum water level of the reservoir at each future time point.

[0034] The specific analysis method for reservoir water level prediction is as follows: the first step is to read the historical water conditions data of the reservoir from the management database, select time points for the historical water conditions data according to a preset time length, record them as historical time points, extract the water levels of each detection point in the reservoir at each historical time point from the historical water conditions data, and connect to the meteorological department at the same time to obtain the local rainfall and temperature of the reservoir at each historical time point; this can fully understand the past water level changes of the reservoir and the relationship with meteorological factors, provide a data basis for establishing an accurate prediction model, and at the same time obtain local rainfall and temperature information to better analyze the influencing factors of water level changes.

[0035] In the second step, a linear regression model was used with water level as the dependent variable Y and rainfall and temperature as the independent variables X. 1 , X 2 , the linear regression equation Y = aX is calculated 1 +bX 2 +c, obtain the predicted rainfall and temperature values ​​at each future time point from the meteorological department, substitute them into the linear regression equation, calculate the predicted water level values ​​of each detection point at each future time point of the reservoir, and select the maximum value from the predicted water level values ​​of each detection point at each future time point of the reservoir, which is recorded as the maximum predicted water level value of the reservoir at each future time point, recorded as The future water level of the reservoir can be estimated in advance, which has important guiding significance for water resources management, flood control and drought relief decision-making, water conservancy project planning, etc. It helps to prepare response measures in advance and reduce potential risks and losses.

[0036] It should be noted that, in a specific embodiment, there is a set of data: rainfall (mm): 10, 15, 25, 25. Temperature (℃): 20, 25, 30, 35. Water level (m): 1.5, 1.8, 2.2, 2.5. Suppose the linear regression equation is Y=aX 1 +bX 2 +c, first calculate the mean of each variable: mean rainfall Average temperature Water level mean Calculate the product of the deviations of each item and get 12.5, 7.5, 125, 125, and then calculate the regression coefficient: c=2-0.117.5-0.0627.5=0.2, and the linear regression equation is calculated as: Y=0.1*X 1 +0.06*X 2 +0.2.

[0037] S4. Water use scheduling analysis: Obtain the water consumption in each area in each time period, and combine the real-time deliverable water volume of the reservoir to analyze the water delivery volume in each area in each time period, and then deliver water to each area in each time period.

[0038] See also Figure 3 As shown, the specific analysis method of the water use scheduling analysis is: the first step is to divide the water supply range of the reservoir into several areas according to the set area, recorded as each area, and divide each area into industries, recorded as each industry area, and divide the time period according to a fixed time length, and read the water consumption of each area and each time period from the management database respectively, and calculate the average of the water consumption of each area and each time period according to the divided industry, and obtain the average water consumption of each industry area in each time period, and then analyze and obtain the proportion of water consumption of each industry area; the specific distribution and characteristics of water use within the entire water supply range can be clearly understood, which can provide basic data for subsequent analysis and decision-making, and help to carry out targeted water resources management and planning.

[0039] It should be noted that the industry areas can be divided into agricultural areas, industrial areas, commercial areas, residential areas, tourism and leisure areas, etc.

[0040] It should be noted that the specific analysis method for the water consumption ratio of each industry region is as follows: read the average water consumption of each industry region in each time period, recorded as Q jp , j represents the number of the jth industry area, j = 1, 2, ..., g, p represents the number of the pth time period, p = 1, 2, ..., l, through the formula Get the water consumption ratio of each industry region ε j .

[0041] In the second step, the water consumption proportion of each industry area is compared with the preset water consumption proportion threshold. The industries corresponding to the industry areas whose water consumption proportion is greater than or equal to the preset water consumption proportion threshold are recorded as high water-consuming industries, and the industries corresponding to the industry areas whose water consumption proportion is less than or equal to the preset water consumption proportion threshold are recorded as general water-using industries. The objects of key attention and management can be clearly identified, and more stringent water-saving measures or reasonable regulation can be adopted for high water-consuming industries to achieve optimal allocation of water resources.

[0042] The third step is to read the water consumption in each area and time period, compare it with the preset water consumption threshold, record the time period when the water consumption in each area is greater than or equal to the preset water consumption threshold as the peak water consumption period, and screen out the peak water consumption periods in each area; this is conducive to the reasonable arrangement of water supply, making adequate preparations during peak water consumption periods, and ensuring the stability and reliability of water supply.

[0043] The fourth step is to allocate water use weights according to the high water consumption industries, general water use industries, and peak water use periods. The water use weights of each region and each time period are obtained by accumulating them, and the real-time transportable water volume of the reservoir is obtained. The transported water volume of each region and each time period is obtained by multiplying the real-time transportable water volume of the reservoir by the water use weights of each region and each time period, thereby transporting water to each region in each time period. This can allocate water resources more scientifically and reasonably, provide differentiated water supply according to the characteristics of different regions, industries and time periods, improve the efficiency of water resource utilization, and better meet water needs in all aspects.

[0044] It should be noted that in a specific embodiment, the preset water consumption threshold is 30%. In a certain city, the water consumption of the industrial sector accounts for 40%, and the industrial sector is marked as a high water consumption industry. The water consumption of the service sector accounts for 20%, and the service sector is marked as a general water consumption industry. The preset water consumption threshold is 100 cubic meters per hour. The water consumption of area A from 9 am to 11 am is 120 cubic meters per hour, so this time period is marked as a peak water consumption period. The water use weight is set to 0.5 for high water consumption industries, 0.3 for general water consumption industries, and 0.4 for peak water consumption periods. Area A is a high water consumption industry and is in a peak water consumption period, so the water use weight of area A from 9 am to 11 am is 0.5+0.4=0.9.

[0045] S5. Dam safety monitoring: Test the safety parameters of each dam test point and analyze to obtain the safety evaluation factor of the reservoir dam. The safety parameters include displacement and stress compliance.

[0046] The specific analysis method of the safety parameters of the dam detection points is as follows: the first step is to select a number of detection points on the reservoir dam according to the set intervals, which are recorded as the dam detection points, and select a number of time points with equal time intervals, which are recorded as monitoring time points. The coordinates of each monitoring time point of the dam detection points are obtained by the total station, which are recorded as (x fs ,y fs ,z fs ), f represents the number of the f-th dam detection point, f=1,2,...,k, s represents the number of the s-th monitoring time point, s=1,2,...,z, and substitute it into the formula Get the displacement d of each dam detection point f ,x f (s-1), y f (s-1), z f ( s-1 ) represents the horizontal, vertical and vertical coordinates of the f-th dam detection point at the s-1th monitoring time point; it can monitor the deformation of the dam in real time and discover possible displacement problems of the dam in time, which is very important for ensuring the safety and stability of the dam.

[0047] In the second step, the stress value of each dam detection point at each monitoring time point is detected by stress sensors, which is recorded as ρ fs , substituting it into the formula Get the stress qualification level of each dam test pointζ f , where ρ 0 represents the preset standard stress, They represent the preset stress value and the weight factor of stress stability respectively, and z represents the number of monitoring time points. It can be used to understand whether the stress conditions of the dam at different locations meet the standards, thereby evaluating the structural safety of the dam, which is helpful to take measures in advance to ensure the stability and safe operation of the dam.

[0048] It should be noted that, in a specific embodiment, It can be set to 0.6. It can be set to 0.4. The stress value directly reflects the actual stress that the dam is subjected to at a specific location. If the stress value exceeds the design allowable range or safety threshold, it may directly lead to serious problems such as damage, cracks, or even collapse of the dam structure. This is a key indicator of dam safety. Although the stress stability is not as directly dangerous as the stress value, it can reflect the changing trend of the dam's stress state. If the stress stability is not good, continues to fluctuate, or changes abnormally, it may indicate that potential risks are gradually accumulating or that other influencing factors are at work. It can be used as an auxiliary judgment and early warning indicator, so the stress value has a higher weight.

[0049] The specific analysis method of the safety evaluation coefficient of the reservoir dam is: read the displacement d of each dam detection point respectively f , Stress qualification levelζ f , substituting it into the formula The safety evaluation coefficient ω of the reservoir dam is obtained, where d max Indicates the preset maximum allowable displacement, η 1 , η 2 They represent the preset displacement and weight factors of the stress qualification level, k represents the number of dam inspection points, and e represents the natural constant. It provides an intuitive numerical value to measure the safety level of the dam, reflects the actual safety status of the dam in a more comprehensive and scientific manner, and provides a basis for subsequent maintenance and management decisions.

[0050] It should be noted that, in a specific embodiment, n 1 Can be set to 0.4, η 2It can be set to 0.6. The degree of stress compliance is directly related to the stability and safety of the dam structure. Unreasonable stress distribution or excessive stress may lead to serious consequences such as cracks, deformation and even collapse of the dam. It is one of the core elements of dam safety. The displacement can reflect the deformation of the dam to a certain extent, but sometimes the change of displacement within a certain range may not necessarily endanger the safety of the dam immediately. It is more of an indicator for monitoring and auxiliary judgment. However, if the displacement changes abnormally and significantly, it is also a very dangerous signal. Therefore, the weight corresponding to the degree of stress compliance is higher.

[0051] S6. Equipment status monitoring: Detect the status parameters of each device in the reservoir and analyze them to obtain the status evaluation coefficient of the reservoir equipment. The status parameters include temperature, vibration frequency, and maintenance time interval at each monitoring time point.

[0052] The specific analysis method of the status parameters of each device in the reservoir is as follows: the first step is to number each facility and equipment of the reservoir in sequence according to the set order, record them as each device of the reservoir, select a detection point in each device, and use a temperature sensor to detect the temperature of each detection point at each monitoring time point during the working process of each device of the reservoir, and obtain the temperature of each monitoring time point of each device of the reservoir. c represents the number of the cth device, c = 1, 2, ..., r; it can clearly identify each device, facilitate targeted management and maintenance, and real-time temperature monitoring can promptly detect whether the device has abnormal heating and other conditions, which helps prevent failures in advance.

[0053] In the second step, the working process of each device in the reservoir is divided into several time periods of equal length, recorded as each working time period, and the vibration number of each device in the reservoir in each working time period is obtained by the vibration sensor, and the vibration frequency of each device in the reservoir in each working time period is obtained by dividing the vibration number of each device in the reservoir in each working time period by the length of the working time period. Then, the vibration frequency of each device in the reservoir is obtained by mean calculation, recorded as α c The number and frequency of vibrations can reflect the operating status and mechanical properties of the equipment, and help determine whether the equipment is running smoothly and whether there are potential faults.

[0054] The third step is to read the maintenance time of each device in the reservoir from the management database. The maintenance time interval of each device in the reservoir is obtained by subtracting the time of two adjacent maintenance of each device in the reservoir and taking the average value, which is recorded as T. c ; Understand the rules and frequency of equipment maintenance in order to arrange maintenance plans reasonably and ensure long-term stable operation of the equipment.

[0055] The specific analysis method of the state evaluation coefficient of the reservoir equipment is: reading the temperature of each device at each monitoring time point in the reservoir Vibration frequency of each equipment in the reservoir α c and maintenance interval T c , substituting it into the formula Obtain the status evaluation coefficient of reservoir equipment in T 0 Respectively represent the preset temperature, reference value of maintenance time interval, κ 1 , κ 2 , κ 3 They represent the weight factors of the preset temperature, vibration frequency, and maintenance time interval respectively, r represents the number of devices, and z represents the number of monitoring time points; it comprehensively considers key aspects such as temperature, vibration frequency, and maintenance time interval, and can more comprehensively and accurately reflect the actual status of the equipment.

[0056] It should be noted that, in a specific embodiment, κ 1 can be set to 0.4, κ 2 can be set to 0.4, κ 3 It can be set to 0.2. Temperature has an important impact on the performance and life of the equipment. Too high or too low temperature may cause abnormal operation of the equipment, accelerated material aging, increased failure rate, etc. It can directly reflect the operating environment and status of the equipment. Abnormal vibration frequency may indicate that the equipment has mechanical failure, imbalance and other problems, which has a great impact on the normal operation and reliability of the equipment. It can timely reflect the mechanical condition of the equipment. Reasonable maintenance time intervals can ensure that the equipment is in good working condition, but it is relatively an indirect reflection. Unlike temperature and vibration frequency, it can not reflect the current status of the equipment more directly and quickly. Therefore, the weights corresponding to temperature and vibration frequency are higher.

[0057] S7. Reservoir safety analysis: predict the maximum value of the reservoir water level at each future time point Safety evaluation coefficient ω of reservoir dam and status evaluation coefficient of reservoir equipment The comprehensive safety evaluation index of the reservoir is obtained through analysis, where u represents the number of the u-th future time point, u=1,2,...,v, and feedback is given.

[0058] The specific analysis method of the reservoir safety analysis is as follows: the first step is to read the maximum value of the predicted water level of the reservoir at each future time point. Safety evaluation coefficient ω of reservoir dam and status evaluation coefficient of reservoir equipment Substituting this into the formula The comprehensive safety evaluation index of the reservoir is obtained, where H' represents the safety water level threshold, v represents the number of future time points, and w 1 、w 2 、w 3They respectively represent the weight factors of the preset maximum water level forecast of the reservoir, the safety evaluation coefficient of the reservoir dam, and the status evaluation coefficient of the reservoir equipment; they can more comprehensively reflect all aspects of reservoir safety, avoid the limitations of single-factor evaluation, provide a scientific basis for reservoir management and decision-making, and help to rationally arrange resources and take targeted measures.

[0059] It should be noted that, in a specific embodiment, w 1 Can be set to 0.2, w 2 Can be set to 0.5, w 3 It can be set to 0.3. The safety of the reservoir dam is the core key to reservoir safety. Once a problem occurs in the dam, it may lead to extremely serious consequences, such as dam collapse, which is directly related to the safety of life and property of the people downstream and public safety. Therefore, its importance is extremely high and its weight is relatively large. The status of the reservoir equipment will also have an important impact on the operation and safety of the reservoir. The good operation of the equipment is the guarantee for the normal operation of the reservoir, but its importance is slightly lower than that of the dam. Although the maximum water level forecast is also important, it mainly affects the scheduling of the reservoir, etc., but in comparison, its urgency to direct safety threats is not as critical as the dam safety and equipment status, so its weight is relatively small.

[0060] The second step is to compare the comprehensive safety evaluation index of the reservoir with the preset comprehensive safety evaluation index threshold. If the comprehensive safety evaluation index of the reservoir is greater than or equal to the preset comprehensive safety evaluation index threshold, it means that the comprehensive safety evaluation index of the reservoir is qualified. Otherwise, it means that the comprehensive safety evaluation index of the reservoir is unqualified, and feedback will be given; quickly determine whether the safety of the reservoir meets the standards so that relevant personnel can understand the situation in a timely manner, and then take corresponding measures based on the results, such as strengthening maintenance, adjusting operation strategies, etc., to ensure the safety of the reservoir.

[0061] The present invention screens the pollution time periods of each detection point of the reservoir by analyzing the water quality parameters of water samples from each detection point at each time point in the reservoir, analyzes the water delivery volume of each area in each time period by obtaining the water consumption of each area in each time period, and then delivers water to each area in each time period. According to the predicted maximum value of the water level of the reservoir at each future time point, the safety evaluation coefficient of the reservoir dam, and the status evaluation coefficient of the reservoir equipment, the comprehensive safety evaluation index of the reservoir is analyzed and fed back, which is helpful to comprehensively evaluate the safety status of the reservoir and promptly discover potential risks and problems.

[0062] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A smart reservoir management method based on big data, characterized in that: The steps include: S1. Reservoir water quality monitoring: water quality parameters of water samples at each testing point at each time point in the reservoir are tested, including pH, dissolved oxygen concentration, and turbidity; S2. Reservoir water quality analysis: water quality evaluation coefficients of water samples at each detection point at each time point in the reservoir are obtained based on water quality parameter analysis, and then each pollution time period of each detection point in the reservoir is analyzed and feedback is provided; S3. Reservoir water level prediction: obtain the water level of each detection point of the reservoir at each historical time point, the local rainfall and temperature of the reservoir at each historical time point, and analyze to obtain the maximum predicted water level of the reservoir at each future time point; S4. Water use scheduling analysis: Obtain the water consumption of each region in each time period, and analyze the water delivery volume of each region in each time period in combination with the real-time deliverable water volume of the reservoir, and then deliver water to each region in each time period; S5. Dam safety monitoring: Test the safety parameters of each dam test point and analyze to obtain the safety evaluation coefficient of the reservoir dam. The safety parameters include displacement and stress qualification level. S6. Equipment status monitoring: Detect the status parameters of each device in the reservoir and analyze to obtain the status evaluation coefficient of the reservoir equipment. The status parameters include temperature, vibration frequency, and maintenance time interval at each monitoring time point; S7. Reservoir safety analysis: predict the maximum value of the reservoir water level at each future time point Safety evaluation coefficient ω of reservoir dam and status evaluation coefficient of reservoir equipment The comprehensive safety evaluation index of the reservoir is obtained through analysis, where u represents the number of the u-th future time point, u=1,2,...,v, and feedback is given.

2. According to the big data-based smart reservoir management method of claim 1, it is characterized by: The specific analysis method for the reservoir water quality monitoring is: In the first step, several detection points are selected in the reservoir according to the set interval, and several time points are selected according to the set interval duration, which are recorded as each time point, and a set amount of water samples are collected from each detection point of the reservoir at each time point, which are recorded as water samples of each detection point at each time point, and the pH of the water samples of each detection point at each time point is detected by a PH sensor to obtain the pH of the water samples of each detection point at each time point in the reservoir; The second step is to connect the dissolved oxygen meter, insert the dissolved oxygen electrodes into the water samples at each detection point at each time point, and read the dissolved oxygen concentration of the water samples at each detection point at each time point in the reservoir through the dissolved oxygen meter; The third step is to detect the turbidity of the water samples at each detection point at each time point through the turbidity sensor to obtain the turbidity of the water samples at each detection point at each time point in the reservoir.

3. According to the big data-based smart reservoir management method of claim 2, it is characterized by: The specific analysis method of the reservoir water quality analysis is: The first step is to read the pH, dissolved oxygen concentration, and turbidity of water samples at each test point in the reservoir at each time point, recorded as pH im , im , im , where i represents the number of the i-th time point, i=1,2,...,n, and m represents the number of the m-th detection point, m=1,2,...,q. Substitute it into the formula The water quality evaluation coefficient χ of the water samples at each detection point in the reservoir at each time point is obtained im , where PH', ο', ξ' represent the preset reference values ​​of pH, dissolved oxygen concentration, and turbidity, respectively, and φ1, φ2, φ3 represent the preset weighting factors of pH, dissolved oxygen concentration, and turbidity, respectively; In the second step, adjacent time points are grouped as adjacent time point groups, and the water quality evaluation coefficient difference of water samples at each detection point in each adjacent time point group of the reservoir is obtained by subtracting the water quality evaluation coefficient of water samples at each detection point in each adjacent time point group of the reservoir, and the water quality evaluation coefficient difference of water samples at each detection point in each adjacent time point group of the reservoir is compared with the preset water quality evaluation coefficient difference threshold. If the water quality evaluation coefficient difference of water samples at a certain detection point in a certain adjacent time point group of the reservoir is greater than or equal to the preset water quality evaluation coefficient difference threshold, then the time point group is recorded as a polluted time period, and the pollution time periods of each detection point in the reservoir are counted and feedback is given.

4. The smart reservoir management method based on big data according to claim 3 is characterized by: The specific analysis method for reservoir water level prediction is as follows: The first step is to read the historical water data of the reservoir from the management database, select the time points of the historical water data according to the preset time length, record them as historical time points, extract the water level of each detection point of the reservoir at each historical time point from the historical water data, and connect with the meteorological department to obtain the local rainfall and temperature of the reservoir at each historical time point; In the second step, the linear regression model is used, with water level as the dependent variable Y, rainfall and temperature as the independent variables X1 and X2, to calculate the linear regression equation Y=aX1+bX2+c. The predicted rainfall and temperature at each future time point are obtained from the meteorological department, and they are substituted into the linear regression equation to calculate the predicted water level values ​​of each detection point at each future time point of the reservoir. The maximum value is selected from the predicted water level values ​​of each detection point at each future time point of the reservoir, and recorded as the predicted maximum water level value of the reservoir at each future time point, recorded as 5. The smart reservoir management method based on big data according to claim 1 is characterized by: The specific analysis method of the water use scheduling analysis is: The first step is to divide the water supply range of the reservoir into several areas according to the set area, record them as areas, divide the areas into industries, record them as industry areas, and divide the time periods according to fixed time lengths. Read the water consumption of each area and time period from the management database, and calculate the average of the water consumption of each area and time period according to the divided industries to obtain the average water consumption of each industry area and time period, and then analyze and obtain the water consumption proportion of each industry area; The second step is to compare the water consumption ratio of each industry area with the preset water consumption ratio threshold, and record the industry corresponding to the industry area whose water consumption ratio is greater than or equal to the preset water consumption ratio threshold as a high water consumption industry, and record the industry corresponding to the industry area whose water consumption ratio is less than or equal to the preset water consumption ratio threshold as a general water consumption industry; The third step is to read the water consumption of each area in each time period, compare it with the preset water consumption threshold, record the time period when the water consumption of each area is greater than or equal to the preset water consumption threshold as the peak water consumption period, and screen out the peak water consumption periods of each area; The fourth step is to allocate water use weights according to the high water consumption industries, general water use industries, and peak water use periods. The water use weights of each region and each time period are obtained by accumulating them. At the same time, the real-time transportable water volume of the reservoir is obtained. The transportable water volume of each region and each time period is obtained by multiplying the real-time transportable water volume of the reservoir by the water use weight of each region and each time period, thereby transporting water to each region in each time period.

6. The smart reservoir management method based on big data according to claim 1 is characterized by: The specific analysis method of the safety parameters of the above-mentioned dam detection points is as follows: In the first step, several detection points are selected on the reservoir dam according to the set intervals, which are recorded as the dam detection points, and several time points with equal time intervals are selected, which are recorded as monitoring time points. The coordinates of each monitoring time point of each dam detection point are obtained by the total station, which are recorded as (x fs ,y fs ,z fs ), f represents the number of the f-th dam detection point, f=1,2,...,k, s represents the number of the s-th monitoring time point, s=1,2,...,z, and substitute it into the formula Get the displacement d of each dam detection point f ,x f(s-1) ,y f(s-1) 、z f(s-1) Indicates the horizontal coordinate, vertical coordinate and vertical coordinate of the s-1th monitoring time point of the fth dam detection point; In the second step, the stress value of each dam detection point at each monitoring time point is detected by stress sensors, which is recorded as ρ fs , substituting it into the formula Get the stress qualification level of each dam test point Where ρ0 represents the preset standard stress, They represent the preset stress value and the weight factor of stress stability respectively, and z represents the number of monitoring time points.

7. The smart reservoir management method based on big data according to claim 6 is characterized by: The specific analysis method of the safety evaluation coefficient of the reservoir dam is: Read the displacement d of each dam detection point respectively f , Stress qualification level Substituting this into the formula The safety evaluation factor ω of the reservoir dam is obtained, where d max It represents the preset maximum allowable displacement, η1 and η2 represent the weight factors of preset displacement and stress qualification degree respectively, k represents the number of dam inspection points, and e represents the natural constant.

8. The smart reservoir management method based on big data according to claim 7 is characterized by: The specific analysis method of the state parameters of each device in the reservoir is as follows: The first step is to number the facilities and equipment of the reservoir in the set order, record them as the equipment of the reservoir, select the detection point of each equipment, and use the temperature sensor to detect the temperature of each monitoring time point of the detection point during the working process of each equipment of the reservoir, and obtain the temperature of each monitoring time point of each equipment of the reservoir. c represents the number of the c-th device, c = 1, 2, ..., r; In the second step, the working process of each device in the reservoir is divided into several time periods of equal length, recorded as each working time period, and the vibration number of each device in the reservoir in each working time period is obtained by the vibration sensor, and the vibration frequency of each device in the reservoir in each working time period is obtained by dividing the vibration number of each device in the reservoir in each working time period by the length of the working time period. Then, the vibration frequency of each device in the reservoir is obtained by mean calculation, recorded as α c ; The third step is to read the maintenance time of each device in the reservoir from the management database. The maintenance time interval of each device in the reservoir is obtained by subtracting the time of two adjacent maintenance of each device in the reservoir and taking the average value, which is recorded as T. c .

9. The smart reservoir management method based on big data according to claim 8 is characterized by: The specific analysis method of the state evaluation coefficient of the reservoir equipment is: Read the temperature of each device at each monitoring time point in the reservoir Vibration frequency of each equipment in the reservoir α c and maintenance interval T c , substituting it into the formula Obtain the status evaluation coefficient of reservoir equipment in T0 represents the reference value of the preset temperature and maintenance time interval, κ1, κ2, and κ3 represent the weight factors of the preset temperature, vibration frequency, and maintenance time interval, r represents the number of equipment, and z represents the number of monitoring time points.

10. The smart reservoir management method based on big data according to claim 1 is characterized by: The specific analysis method of the reservoir safety analysis is as follows: The first step is to read the maximum predicted water level of the reservoir at each future time point Safety evaluation coefficient ω of reservoir dam and status evaluation coefficient of reservoir equipment Substituting this into the formula The comprehensive safety evaluation index ψ of the reservoir is obtained, H' represents the safe water level threshold, v represents the number of future time points, w1, w2, and w3 represent the weight factors of the preset maximum water level prediction of the reservoir, the safety evaluation coefficient of the reservoir dam, and the status evaluation coefficient of the reservoir equipment, respectively. The second step is to compare the comprehensive safety evaluation index of the reservoir with the preset comprehensive safety evaluation index threshold. If the comprehensive safety evaluation index of the reservoir is greater than or equal to the preset comprehensive safety evaluation index threshold, it means that the comprehensive safety evaluation index of the reservoir is qualified. Otherwise, it means that the comprehensive safety evaluation index of the reservoir is unqualified and feedback will be given.

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