Water conservancy system monitoring method based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to a water conservancy system monitoring method based on the Internet of Things, and the method comprises the steps: obtaining the power generation data of a plurality of moments in a water conservancy system and the water level data corresponding to each power generation data based on the Internet of Things, and determining the change stability of the power generation at each moment according to the power generation data; the power generation rule characteristic value at each moment is determined according to the power generation amount data, the water level data and the change stability, the potential abnormal type is determined according to the power generation rule characteristic and the change stability, the potential abnormal type is automatically identified as sediment accumulation or equipment fault, the monitoring efficiency is improved, prevention and troubleshooting are carried out in time, and the monitoring efficiency is improved. Normal operation of a water conservancy system and stability of generating capacity are maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a water conservancy system monitoring method based on the Internet of Things. Background Art

[0002] In the power stations of the hydropower system, power generation monitoring is crucial to ensure the stability of power supply. By real-time monitoring of power generation, abnormal power generation problems can be discovered in time, power supply interruptions caused by insufficient power generation can be prevented, equipment damage and safety accidents can be prevented, thereby ensuring the continuity of industrial production, public services and daily life.

[0003] At present, the abnormal power generation of power stations in the hydropower system may be caused by a variety of factors, such as insufficient water, sediment accumulation and equipment failure. Insufficient water will directly affect the ability of water flow to drive the generator set and reduce power generation. The accumulation of sediment may cause blockage of turbines and water inlets, reduce water flow efficiency, and thus affect power generation efficiency. Equipment failures such as generator failure or control system failure will also lead to a decrease in power generation capacity or shutdown. However, current monitoring methods can only inform of the abnormality after determining that the power generation is abnormal, but cannot directly inform the type of abnormality. It is necessary to manually further investigate the specific fault problem, which is inefficient and not conducive to quickly handling the abnormality, and is not conducive to maintaining the normal operation of the hydropower system and the stability of power generation. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a water conservancy system monitoring method based on the Internet of Things, and the technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a method for monitoring a water conservancy system based on the Internet of Things, including: Based on the Internet of Things, the power generation data at multiple times in the water conservancy system and the water level data corresponding to each power generation data are obtained; Determine the stability of the change of power generation at each moment according to the power generation data; Determine the characteristic value of power generation law at each moment according to the power generation data, the water level data and the change stability; According to the power generation regularity characteristics and the change smoothness, potential abnormality types are determined; the abnormality types include sediment accumulation or equipment failure.

[0005] In one embodiment, determining the stability of the change of power generation at each moment according to the power generation data includes: Determining the degree of power generation abnormality at each moment according to the power generation data; The stability of the change in power generation at each moment is determined based on the power generation data and the power generation abnormality degree.

[0006] In one embodiment, determining the abnormality of power generation at each moment according to the power generation data includes: A specified number of moments are used as a window size, and a target window corresponding to each moment is determined according to the window size, wherein each moment is the last moment in the target window corresponding to the moment; According to the target window and the power generation data, respectively, in each of the target windows, determine the first power generation of each last moment, the second power generation of each moment except the last moment, the total power generation of all moments in the target window, the average of the total power generation of all moments in the target window except the last moment, and the first absolute value of the difference between the first power generation and each of the second power generation; The sum of power generation differences in each target window is calculated respectively according to the first absolute value, and the power generation abnormality degree at each moment is determined respectively according to the total power generation, the power generation mean and the sum of power generation differences.

[0007] In one embodiment, determining the abnormality of power generation at each moment according to the total power generation, the average power generation and the sum of the power generation differences respectively includes: respectively calculating the second absolute value of the difference between the total power generation and the average power generation; The power generation abnormality degree at each moment is obtained according to the second absolute value and the first product of the total power generation difference.

[0008] In one embodiment, determining the stability of the change of power generation at each moment according to the power generation data and the power generation abnormality degree includes: generating a power generation curve graph according to the power generation data; According to the power generation data and the power generation abnormality, in each of the target windows, respectively determine the absolute values ​​of a first difference between the power generation abnormality corresponding to the moment before the last moment and the power generation abnormality corresponding to the last moment, a second difference between the power generation at the first moment and the power generation at the last moment, and a third difference between the power generation at the initial moment and the power generation at the last moment; According to the power generation curve, determine a first slope of a line connecting the power generation at a moment before the last moment and the power generation at the last moment, a second slope of a line connecting the power generation at two moments before the last moment and the power generation at a moment before the last moment, and determine a first ratio of the first slope to the second slope; Determine a fourth difference between a preset value and the first ratio, and determine a second product of the first difference and the second difference and a second ratio of the absolute value of the second product and the third difference, and based on the third product of the second ratio and the fourth difference and a natural exponential function, obtain the smoothness of the change in power generation at each moment.

[0009] In one embodiment, determining the characteristic value of the power generation law at each moment according to the power generation data, the water level data and the change stability includes: Determine the Pearson correlation coefficient corresponding to each target window according to the water level data and the stability of the change; Taking the last moment in any target window as the first moment, determining each second moment except the first moment, and determining the first power generation and the first water level corresponding to the first moment, and the third power generation and the second water level corresponding to each second moment according to the water level data and the power generation data; Determine the water volume variation effect at the first moment according to the Pearson correlation coefficient, the first power generation, the first water level, each of the third power generation and the second water level, and return to the step of taking the last moment in any target window as the first moment until the water volume variation effect at each moment is obtained; According to the water volume variation effect and the power generation data, the characteristic value of the power generation law at each moment is determined.

[0010] In one embodiment, determining the water volume change effect at the first moment according to the Pearson correlation coefficient, the first power generation, the first water level, each of the third power generation and the second water level includes: Determining a third ratio of the preset value to the Pearson correlation coefficient; Determining a target ratio of the first water level to the first power generation, fourth ratios of each of the second water levels to each of the third power generation, and respectively determining third absolute values ​​of differences between the target ratio and each of the fourth ratios; The absolute value sum of each of the third absolute values ​​is determined, and the water volume change effect at the first moment is obtained according to a fourth product of the absolute value sum and the third ratio and a natural exponential function.

[0011] In one embodiment, determining the power generation law characteristic value at each moment according to the water volume variation effect and the power generation data includes: Determine the total power generation of the target month at each moment according to the power generation data; Acquire historical power generation data, and determine the average total power generation corresponding to the target month from the historical power generation data; The absolute value of the fifth difference between the total power generation and the total power generation mean and the fifth ratio of the water volume variation effect to the absolute value of the fifth difference are determined respectively, and the characteristic value of the power generation law at each moment is obtained based on the sum of the water volume variation effect and the fifth ratio.

[0012] In one embodiment, determining the potential abnormality type according to the power generation law characteristics and the change stability includes: According to the sixth ratio of the power generation abnormality degree at each moment to the power generation law characteristic value at each moment, a corrected power generation abnormality degree corresponding to each moment is obtained; According to the corrected power generation abnormality level corresponding to each moment and the prediction model, a predicted value of the power generation abnormality level corresponding to the next moment of the current moment is obtained; A potential abnormality type is determined based on the predicted value and the change smoothness.

[0013] In one embodiment, determining the potential abnormality type according to the predicted value and the change stability includes: When the predicted value is greater than the first threshold, comparing the change stability at the current moment with the second threshold; If the change stability at the current moment is greater than the second threshold, determining that the potential abnormality type is sediment accumulation; If the change stability at the current moment is less than or equal to the second threshold, it is determined that the potential abnormality type is a device failure.

[0014] The present invention has the following beneficial effects: Based on the Internet of Things, the power generation data of the water conservancy system at multiple times and the water level data corresponding to each power generation data are obtained. According to the power generation data, the stability of the power generation change at each moment is determined. According to the power generation data, the water level data and the stability of the change, the characteristic value of the power generation law at each moment is determined. According to the power generation law characteristics and the stability of the change, the potential abnormal type is determined, and the potential abnormal type is automatically identified as sediment accumulation or equipment failure, which is conducive to improving monitoring efficiency, timely prevention and investigation, and maintaining the normal operation of the water conservancy system and the stability of power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of the steps of a water conservancy system monitoring method based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the water conservancy system monitoring method based on the Internet of Things proposed by the present invention, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] It should be noted that in order to ensure that the calculation results are meaningful, when performing fractional operations in the embodiments of the present invention, when the denominator is 0, it is necessary to add a parameter adjustment factor greater than 0 to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to actual conditions, and this application does not impose any special restrictions.

[0020] It should be noted that the term “exemplary” in the embodiments of the present application refers to examples listed for the convenience of explanation, and other embodiments are not limited to the examples listed.

[0021] The specific scheme of the water conservancy system monitoring method based on the Internet of Things provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flow chart of a water conservancy system monitoring method based on the Internet of Things provided by an embodiment of the present invention. The water conservancy system monitoring method based on the Internet of Things may at least include steps S100-S400: S100, obtaining power generation data at multiple times in a water conservancy system and water level data corresponding to each power generation data based on the Internet of Things.

[0023] S200: Determine the stability of the change in power generation at each moment based on the power generation data.

[0024] S300, determining the characteristic value of power generation law at each moment according to power generation data, water level data and change stability.

[0025] S400: Determine potential abnormality types based on power generation law characteristics and change stability; abnormality types include but are not limited to sediment accumulation or equipment failure.

[0026] The technical solution of the embodiment of the present application obtains power generation data at multiple moments in a water conservancy system and water level data corresponding to each power generation data based on the Internet of Things, determines the stability of power generation changes at each moment based on the power generation data, determines the characteristic value of the power generation law at each moment based on the power generation data, water level data and the stability of changes, determines the potential abnormal type based on the power generation law characteristics and the stability of changes, and automatically identifies the potential abnormal type as sediment accumulation or equipment failure, which is conducive to improving monitoring efficiency, timely prevention and investigation, and maintaining the normal operation of the water conservancy system and the stability of power generation.

[0027] In one embodiment, in step S100, since the change of power generation is affected by multiple factors in the monitoring of power generation, the monitoring system is connected to the Internet of Things device through the Internet of Things, and the data collected by the Internet of Things device is obtained through the Internet of Things, and the Internet of Things device can obtain the power generation data at multiple times in the water conservancy system and the water level data corresponding to the power generation data. It should be noted that the time and frequency of collection can be set based on needs, and the data is collected once every hour, that is, the data is collected once every hour, such as 20:00, 21:00, etc., and a total of 1 month of data is collected. It should be noted that since the power generation data may be affected by the water flow, it may also be affected by the sediments in the water. The sediments may cause the water flow to be blocked and reduce the power generation capacity. When the equipment in the water conservancy system fails, it will also cause abnormal power generation data. Therefore, it is necessary to distinguish the causes of the abnormality, that is, the type of abnormality.

[0028] It should be noted that the embodiment of the present application determines the degree of power generation abnormality of the power generation at each moment. If the power generation at each moment changes more greatly than that at other moments, it means that the power generation may be abnormal. The influence mechanism of each factor on the power generation is different, so the manifestation of abnormal power generation changes will also be different. The stability of the power generation change is calculated by the change of power generation, and then the water volume change effect at each moment is obtained by the specific influence of water volume on power generation. Because the influence of water volume on power generation is normal, the power generation abnormality at each moment is corrected according to the water volume change effect at each moment; and the power generation abnormality at the next moment is predicted according to the corrected power generation abnormality.

[0029] Among them, the abnormal phenomenon of power generation may be caused by a variety of factors, which mainly include the accumulation of sediments, changes in water flow, and equipment failures. Different factors have different effects on power generation, resulting in different characteristics and degrees of abnormal changes in power generation. Therefore, when analyzing the abnormal situation of power generation, it is first necessary to evaluate the power generation at each moment and judge its degree of abnormality. If the power generation at a certain moment changes greatly compared with other moments, it means that the power generation at that moment may be abnormal, and this abnormality may be caused by a variety of factors. At the same time, since the power generation data may be affected by short-term fluctuations and noise, directly analyzing the data at each moment may lead to misjudgment. At the same time, it is necessary to observe the trend of power generation changes over a period of time to more accurately describe the trend of power generation changes.

[0030] In one implementation, step S200 includes steps S201-S202: S201. Determine the abnormality of power generation at each moment according to power generation data.

[0031] First, a specified number of moments is used as the window size. For example, if the specified number is 10, 10 moments are used as the window size, and the target window corresponding to each moment is determined according to the window size. In the target window corresponding to each moment, the moment is the last moment. For example, there are 11 moments in total, namely, 00:00, 01:00, 02:00, 03:00, 04:00, 05:00, 06:00, 07:00, 08:00, 09:00, and 10:00. At this time (00:00, 01:00, 02:00, 03:00, 04:00, 05:00, 06:00, 07:00, 08:00, 09:00, and 10:00). :00, 09:00) as a target window, and the last time in the target window is 09:00, this target window is the target window corresponding to the time of 09:00. Similarly, the target window corresponding to the time of 10:00 is (01:00, 02:00, 03:00, 04:00, 05:00, 06:00, 07:00, 08:00, 09:00, 10:00), so as to determine the target window corresponding to each time, and get the The target window corresponding to the time.

[0032] Secondly, according to the target window and power generation data, determine the last moment (i.e. The first power generation at the time , the second power generation at each moment except the last moment (No. The first time in the target window corresponding to the The power generation at the target time), the total power generation at all times in the target window (i.e. The total power generation of the target window corresponding to the moment is the sum of the power generation of 10 moments), the average of the total power generation of the target window corresponding to all moments in the target window except the last moment (i.e., the sum of the total target window power generation corresponding to the other 9 moments except the last moment divided by 9) and the first absolute value of the difference between the first power generation and each second power generation .

[0033] Furthermore, according to the first absolute value , calculate the total power generation difference of each target window , and respectively according to the total power generation , average power generation And the total difference in power generation , determine the abnormal degree of power generation at each moment. Specifically: calculate the total power generation The average power generation The second absolute value of the difference , respectively, according to the second absolute value And the total difference in power generation The first product of The abnormality of power generation at the time , the formula is: In the formula, Indicates The abnormality of power generation at the moment, The larger the The more the power generation at the moment differs from the previous power generation, the The more abnormal the power generation is at a certain moment, The larger the The greater the abnormality of power generation at that moment.

[0034] It should be noted that the accumulation of sediments will gradually hinder the flow of water, reduce the speed of water flow, and affect the rotation speed and power generation efficiency of the turbine. Under normal circumstances, abnormal changes in sediment accumulation will cause the power generation to gradually decrease, and this change is usually slow; changes in water flow are also an important factor in abnormal power generation. Water flow is affected by natural conditions such as rainfall and changes in water sources in the basin. When precipitation increases or decreases, the water volume will also change, thereby affecting the input water volume and power generation of the turbine. The power generation will gradually change with the increase or decrease in water volume; equipment failure is also an important reason for abnormal power generation. Failure or performance degradation of power generation equipment such as turbines and generator sets will directly affect the power generation efficiency, resulting in a sudden decrease in power generation. Therefore, the cause of abnormal power generation is first analyzed through the specific degree of change in power generation. Specifically, if the difference in the degree of abnormality at adjacent moments is small, it indicates that the abnormal change is relatively gentle, which may be caused by sediment accumulation or water volume changes. The change amplitude of power generation at adjacent moments is small, indicating that the change of power generation is stable. If the abnormal degree changes at adjacent moments are similar, it means that the trend of abnormal change is more consistent and the degree of change is relatively gentle, indicating that the abnormal changes in the system are relatively stable, which may be caused by continuous sediment or water volume factors.

[0035] S202: Determine the stability of the change in power generation at each moment based on the power generation data and the degree of power generation abnormality.

[0036] First, a power generation curve graph is generated based on the power generation data, in which the horizontal axis represents the time and the vertical axis represents the power generation corresponding to the time.

[0037] Secondly, according to the power generation data and the degree of power generation abnormality, the last moment (the The moment before the moment The degree of power generation abnormality corresponding to the time The degree of power generation abnormality corresponding to the last moment The first difference , power generation at the first moment The power generation at the last moment The second difference , initial moment The power generation at the first moment (for example, the power generation at the first moment in the power generation data collected for one month or the first moment in the target window) and the power generation at the last moment The third difference The absolute value of .

[0038] Furthermore, according to the power generation curve, determine the first slope of the line connecting the power generation at the moment before the last moment and the power generation at the last moment , the second slope of the line connecting the power generation of the two moments before the last moment and the power generation of the moment before the last moment , and determine a first ratio of the first slope to the second slope .

[0039] Then, illustratively, taking the preset value 1 as an example, the ratio of the preset value 1 to the first The fourth difference , and determine the first difference The second difference The second product of and the second product The third difference The absolute value of The second ratio , and according to the third product of the second ratio and the fourth difference and the natural exponential function , we can get the stability of power generation at each moment. The specific formula is: In the formula, Indicates The stability of the changes from moment to moment; Indicates the change in the degree of adjacent power generation anomalies. The closer the adjacent anomalies are, the more gradual the change is, and the more likely it is that the change is caused by sediment and water volume; It indicates the difference of power generation at adjacent moments. The smaller the difference, the smaller the change of power generation. The larger the value, the greater the difference between the initial moment and the The greater the difference in power generation at each moment, the more likely it is that anomalies will occur. Power generation is subject to abnormal changes due to sediment or water volume. This means that the closer the slopes at adjacent moments are, the more similar the changing trends are and the more gradual the degree of change is.

[0040] It should be noted that in the change of power generation caused by the change of water volume, the impact of water volume on power generation is direct. The increase or decrease of water flow directly changes the input of the turbine, which in turn directly affects the power generation output. The water volume will be rapidly affected by factors such as weather, rainfall, seasonal changes, etc., so the power generation varies with the water volume. In a hydroelectric power station, sediments in the reservoir will accumulate at the bottom, mainly including silt and other sediments, which may affect the water storage capacity of the reservoir, or during the water diversion process, sediments may be deposited at the bottom of the diversion channel or waterway, which may cause blockage and reduced flow. Sediments may also be deposited around the turbine, affecting power generation efficiency and power generation.

[0041] Among them, when the change of water level is closer to the change of power generation, it means that the abnormal power generation may be caused by the change of water volume. Due to the difference in precipitation, the stability of the change at each moment also changes with the amount of water. When the precipitation is large, the stability of the change will become smaller. At the same time, the overall change of the stability caused by the water volume is relatively small. If the ratio of water level to power generation at each moment remains relatively stable, it means that the change of power generation maintains a stable proportional relationship with the change of water level; because the power generation is affected by the change of water level, the ratio of water level to power generation is not much different and is basically constant, which means that the system may be in normal operation, and the change of power generation caused by water level change remains consistent.

[0042] In one implementation, step S300 includes steps S301-S304: S301. Determine the Pearson correlation coefficient corresponding to each target window according to the water level data and the stability of the change.

[0043] It should be noted that the Pearson correlation coefficient between the two data can be determined based on existing means. Therefore, the Pearson correlation coefficient corresponding to each target window can be determined based on the water level data and the stability of the change using existing means. (i.e. The Pearson correlation coefficient of the target window corresponding to the moment) is not repeated here.

[0044] S302, taking the last moment in any target window as the first moment, determining each second moment except the first moment, and determining the first power generation and the first water level corresponding to the first moment, and the third power generation and the second water level corresponding to each second moment according to the water level data and the power generation data.

[0045] Optionally, the last moment in any target window is taken as the first moment, recorded as At the moment, determine each second moment except the first moment, recorded as the At the moment, and according to the water level data and the power generation data, determine the first power generation corresponding to the first moment and first water level , the third power generation corresponding to each second moment and the second water level .

[0046] S303, determining the water volume change effect at the first moment according to the Pearson correlation coefficient, the first power generation, the first water level, each third power generation and the second water level, and returning to the step of taking the last moment in any target window as the first moment until the water volume change effect at each moment is obtained.

[0047] Specifically, the water volume variation effect at the first moment is determined according to the Pearson correlation coefficient, the first power generation, the first water level, each third power generation and the second water level: First, take the preset value 1 as an example to determine the correlation coefficient between the preset value 1 and the Pearson The third ratio It should be noted that the preset value cannot be 0.

[0048] Secondly, determine the target ratio of the first water level to the first power generation , a fourth ratio of each second water level to each third power generation , and respectively determine the third absolute value of the difference between the target ratio and each fourth ratio .

[0049] Furthermore, the absolute value sum of each third absolute value is determined ,in is the total quantity at the moment, and according to the fourth product of the sum of absolute values ​​and the third ratio and the natural exponential function, the water volume change effect at the first moment is obtained, and the formula is: In the formula, Indicates The water volume change effect at the moment (currently the first moment), the sum of the absolute values Indicates the difference between the ratio of the water level to its power generation at each first moment and the ratio of the water level to its power generation at other second moments, the sum of the absolute values The smaller the The more the impact at any moment is related to the water volume, the more likely it is that the abnormal power generation is caused by the change in water volume. The greater the effect of water volume fluctuation, The larger the value, the The greater the correlation between the stability of changes at each moment in the target window of time and the water volume, the more relevant it is to the change in water volume and the greater the impact of water volume.

[0050] In addition, return to the step of taking the last moment in any target window as the first moment, that is, re-determine the new first moment in the new target window, and re-determine the water volume change effect of the new first moment until the water volume change effect of each moment is obtained. , When different values ​​are taken, it represents the water volume change effect at different times .

[0051] It should be noted that when analyzing the abnormal power generation, the abnormal degree of power generation at each moment needs to be corrected. For the abnormal power generation moments affected by sediment and equipment failure, the abnormal degree needs to be increased to highlight the impact of these abnormal factors on power generation; for the abnormal power generation caused by water volume changes, the abnormal degree should be reduced to avoid misjudging the normal fluctuation of water volume as abnormal. Among them, hydropower stations usually adjust the power generation according to the real-time changes in power demand to ensure the stability and efficiency of the system. Even if the water volume of the reservoir is surplus, the power generation of the power station will still be restricted by the load demand, especially during the trough of demand. When the power demand is low, the power supply in the power grid is in excess, and the power station may choose to reduce the power generation output to avoid the waste of excess power or adjust the balance of the power grid. The precipitation and power demand in different seasons are different. Therefore, the power consumption at the current moment is compared with the power consumption in the historical data. If the power demand at the current moment is low, the impact of water volume on power generation may be smaller.

[0052] S304: Determine the characteristic value of power generation law at each moment according to the water volume variation effect and power generation data.

[0053] First, based on the power generation data, determine the total power generation of the target month at each moment It should be noted that, assuming that the current power generation data is collected in September 2024, the total power generation of the target month (September) at each moment is determined by adding up the power generation at all moments in September.

[0054] Secondly, obtain historical power generation data, and determine the average total power generation corresponding to the target month from the historical power generation data. It should be noted that in addition to the current data collected in September 2024, historical power generation is also collected in historical time, so the total power generation in September in the historical year can be determined. For example, assuming that three years of historical power generation data are used, namely September 2021, September 2022, and September 2023, the total power generation of the three Septembers is determined, and then the total power generation is divided by 3 to obtain the average total power generation. .

[0055] Furthermore, the total power generation is determined The average of total power generation The fifth difference The absolute value of and water volume fluctuation effects Difference with the fifth The absolute value of The fifth ratio , and according to the water volume change effect The sum of the fifth ratio gives the characteristic value of power generation law at each moment: in, No. The characteristic value of power generation law at the moment, It means that when the change of power generation in the same month is closer to the historical data, The power generation at each moment changes according to normal rules, and the larger the characteristic value of the power generation rule.

[0056] In one implementation, step S400 includes steps S401-S403: S401. Obtain a corrected power generation abnormality level corresponding to each moment according to a sixth ratio of the power generation abnormality level at each moment to the power generation law characteristic value at each moment.

[0057] Optionally, according to the abnormality of power generation at each moment and the characteristic value of power generation law at each moment The sixth ratio of the power generation abnormality corresponding to each moment is obtained. : ,When the water volume change effect is larger, it means that the abnormal power generation caused by the water volume is more affected, and the actual abnormal power generation degree is smaller, and the abnormal power generation degree needs to be corrected to a smaller value.

[0058] S402: Obtain a predicted value of the power generation abnormality level corresponding to the next moment of the current moment according to the corrected power generation abnormality level corresponding to each moment and the prediction model.

[0059] Optionally, the prediction model uses the EWMA (exponentially weighted moving average) model to calculate the corrected power generation abnormality level at each moment. The sequence is input into the EWMA model. The EWMA model will calculate the calculated value of the power generation abnormality at the current moment according to the weighted average value of the input sequence, and predict the power generation abnormality corresponding to the future moment (that is, the next moment of the current moment).

[0060] S403: Determine potential abnormality types based on the predicted values ​​and the stability of changes.

[0061] First, the first threshold is 0.8, and the second threshold is adjusted based on actual needs. When the predicted value is greater than the first threshold 0.8, the stability of the change at the current moment is The second threshold is compared.

[0062] Secondly, if the current moment changes steadily If the current change is stable, the potential abnormality type is determined to be sediment accumulation, and the user is prompted to clean it up in time to prevent further accumulation of sediment. If the value is less than or equal to the second threshold, the potential abnormality type is determined to be equipment failure, and personnel are prompted to conduct detailed inspection and maintenance of the equipment in advance to avoid the continuous occurrence of abnormal power generation.

[0063] It can be understood that if the predicted value is less than the first threshold value of 0.8, it means that no abnormal situation has occurred for the time being. Continuing monitoring and returning to the step of obtaining power generation data at multiple times in the water conservancy system and the water level data corresponding to each power generation data based on the Internet of Things is conducive to timely discovery of potential problems and potential abnormal types.

[0064] The method of the embodiment of the present application monitors the power generation data and analyzes the stability of changes, characteristic values ​​of power generation rules, etc., so as to determine whether the potential abnormal type is sediment accumulation or equipment failure. It can timely discover abnormal power generation problems and take preventive measures, which is beneficial to prevent equipment damage and ensure the normal operation of the system and the stability of power generation.

[0065] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A water conservancy system monitoring method based on the Internet of Things, characterized in that: The method comprises: Based on the Internet of Things, the power generation data at multiple times in the water conservancy system and the water level data corresponding to each power generation data are obtained; Determine the stability of the change of power generation at each moment according to the power generation data; Determine the characteristic value of power generation law at each moment according to the power generation data, the water level data and the change stability; Determining potential abnormality types according to the power generation regularity characteristics and the stability of the changes; the abnormality types include sediment accumulation or equipment failure; Among them, according to the power generation data, the abnormal degree of power generation at each moment is determined, and according to the numerical changes of power generation at different moments and the abnormal degree of power generation, the stability of the change is determined; Methods for determining the characteristic values ​​of power generation laws include: Determine the Pearson correlation coefficient based on the water level data and the smoothness of the change; take the last moment in any target window as the first moment, and the other moments as the second moment, and determine the first power generation and the first water level at the first moment, and the third power generation and the second water level at each second moment based on the water level data and the power generation data; determine the water volume change effect at the first moment based on the Pearson correlation coefficient, the first power generation, the first water level, each third power generation and the second water level, and take the last moment in any target window as the first moment until the water volume change effect at each moment is obtained; determine the characteristic value of the power generation law based on the water volume change effect and the power generation data; Methods for determining potential anomaly types include: According to the sixth ratio of the power generation abnormality degree at each moment to the power generation law characteristic value, the corrected power generation abnormality degree corresponding to each moment is obtained; according to the corrected power generation abnormality degree and the prediction model, the predicted value of the power generation abnormality degree at the next moment of the current moment is obtained; according to the predicted value and the stability of the change, the potential abnormality type is determined.

2. The water conservancy system monitoring method based on the Internet of Things according to claim 1 is characterized in that: Determining the abnormality of power generation at each moment according to the power generation data includes: A specified number of moments are used as a window size, and a target window corresponding to each moment is determined according to the window size, wherein each moment is the last moment in the target window corresponding to the moment; According to the target window and the power generation data, respectively, in each of the target windows, determine the first power generation of each last moment, the second power generation of each moment except the last moment, the total power generation of all moments in the target window, the average of the total power generation of all moments in the target window except the last moment, and the first absolute value of the difference between the first power generation and each of the second power generation; The sum of power generation differences in each target window is calculated respectively according to the first absolute value, and the power generation abnormality degree at each moment is determined respectively according to the total power generation, the power generation mean and the sum of power generation differences.

3. The water conservancy system monitoring method based on the Internet of Things according to claim 2 is characterized in that: Determining the abnormality of power generation at each moment according to the total power generation, the average power generation and the sum of the power generation differences respectively includes: respectively calculating the second absolute value of the difference between the total power generation and the average power generation; The power generation abnormality degree at each moment is obtained according to the second absolute value and the first product of the total power generation difference.

4. The water conservancy system monitoring method based on the Internet of Things according to claim 2 is characterized in that: Determining the stability of the change according to the numerical change of the power generation at different times and the degree of power generation abnormality includes: generating a power generation curve graph according to the power generation data; According to the power generation data and the power generation abnormality, in each of the target windows, respectively determine the absolute values ​​of a first difference between the power generation abnormality corresponding to the moment before the last moment and the power generation abnormality corresponding to the last moment, a second difference between the power generation at the first moment and the power generation at the last moment, and a third difference between the power generation at the initial moment and the power generation at the last moment; According to the power generation curve, determine a first slope of a line connecting the power generation at a moment before the last moment and the power generation at the last moment, a second slope of a line connecting the power generation at two moments before the last moment and the power generation at a moment before the last moment, and determine a first ratio of the first slope to the second slope; Determine a fourth difference between a preset value and the first ratio, and determine a second product of the first difference and the second difference and a second ratio of the absolute value of the second product and the third difference, and based on the third product of the second ratio and the fourth difference and a natural exponential function, obtain the smoothness of the change in power generation at each moment.

5. The water conservancy system monitoring method based on the Internet of Things according to claim 1 is characterized in that: Determining the water volume change effect at the first moment according to the Pearson correlation coefficient, the first power generation, the first water level, each third power generation and the second water level comprises: Determining a third ratio of the preset value to the Pearson correlation coefficient; Determining a target ratio of the first water level to the first power generation, fourth ratios of each of the second water levels to each of the third power generation, and respectively determining third absolute values ​​of differences between the target ratio and each of the fourth ratios; The absolute value sum of each of the third absolute values ​​is determined, and the water volume change effect at the first moment is obtained according to a fourth product of the absolute value sum and the third ratio and a natural exponential function.

6. The water conservancy system monitoring method based on the Internet of Things according to claim 1 is characterized in that: Determining the characteristic value of power generation law according to the water volume variation effect and power generation data includes: Determine the total power generation of the target month at each moment according to the power generation data; Acquire historical power generation data, and determine the average total power generation corresponding to the target month from the historical power generation data; The absolute value of the fifth difference between the total power generation and the total power generation mean and the fifth ratio of the water volume variation effect to the absolute value of the fifth difference are determined respectively, and the characteristic value of the power generation law at each moment is obtained based on the sum of the water volume variation effect and the fifth ratio.

7. The water conservancy system monitoring method based on the Internet of Things according to claim 1 is characterized in that: The potential abnormality types determined based on the predicted value and the stability of the change include: When the predicted value is greater than the first threshold, comparing the change stability at the current moment with the second threshold; If the change stability at the current moment is greater than the second threshold, determining that the potential abnormality type is sediment accumulation; If the change stability at the current moment is less than or equal to the second threshold, it is determined that the potential abnormality type is a device failure.

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