A fuzzy recognition reservoir flood control safety chain discriminant system and method

Abstract

The application relates to the technical field of data processing systems or methods specially applicable to supervision or prediction purposes, in particular to a fuzzy recognition reservoir flood control safety chain type discrimination system and method, which comprises a storage module, an upstream water quantity estimation module, a downstream water quantity difference value module and a reservoir redundancy analysis module, the upstream water quantity estimation module is configured with an estimation model, the downstream water quantity difference value module is configured with a calculation model, and the reservoir redundancy analysis module is configured with an analysis model. Through the upstream water quantity estimation module, the downstream water quantity difference value module and the reservoir redundancy analysis module, reservoir flood control safety early warning can be realized based on the change of upstream water quantity, the existing technology needs to be combined with multiple data information for estimation, meanwhile, the application can realize simple reservoir flood control early warning in chain type by relying on upstream water quantity data.

Description

Technical Field

[0001] This application relates to the field of data processing systems or methods specifically applicable to monitoring or forecasting purposes, and more particularly to a fuzzy identification system for a reservoir flood control safety chain, and also to a method thereof. Background Technology

[0002] Flood control scheduling establishes a flood control safety evaluation index system for reservoir groups based on real-time water conditions, reservoir and river water levels, and the hydraulic connections between reservoirs. It dynamically assesses the flood control safety level of the reservoir group system based on the development of water and rainfall conditions. This system can be used to guide reservoirs with less severe flood control situations to share the flood control pressure of reservoirs with severe flood control situations, make reasonable use of idle flood control capacity, and redistribute floodwater in time and space, which has important practical value.

[0003] In the prior art, the publication date is October 13, 2023, the publication number is CN116882696A, and the title is "A Two-Layer Discrimination Method for Flood Control Safety of Reservoir Groups Coupled with Fuzzy Recognition and Probabilistic Reasoning". It discloses the following steps: constructing flood control safety evaluation indicators for reservoirs and cross-sections within a reservoir group system; selecting typical historical floods to construct a dynamic flood scenario set; constructing a fuzzy recognition model to evaluate the flood control safety level of each reservoir and cross-section in time periods, completing the upper-layer discrimination of flood control safety; inferring the probability density distribution of forecast errors based on flood forecast standards, selecting time periods with low flood control safety levels, and generating random flood samples for those time periods; generating a reservoir scheduling scheme set through joint scheduling of the reservoir group, and constructing a Bayesian network training sample set; constructing a Bayesian network model, and calculating the flood risk rate of reservoirs and cross-sections through probabilistic reasoning, completing the lower-layer discrimination of flood control safety.

[0004] The aforementioned technical solutions enable accurate judgment of flood control safety in real-time flood control scheduling and improve the efficiency of flood control safety judgment. However, they cannot achieve systematic reservoir flood control early warning based on a limited number of data factors. Summary of the Invention

[0005] The inventors discovered through research that current domestic and international research on flood control safety assessment primarily focuses on establishing flood control safety assessment index systems to comprehensively evaluate the flood control safety levels of water conservancy projects, river basins, and cities. However, it fails to consider the impact of dynamic changes in torrential rain and floodwaters during real-time flood control scheduling on the safety levels of flood control projects, and the assessment of flood control safety remains at a macro-level. In flood risk assessment research, current domestic and international studies typically rely on Monte Carlo stochastic simulation methods, Markov chains, and Bayesian network models for flood risk assessment.

[0006] The purpose of this application is to provide a fuzzy recognition-based reservoir flood control safety chain discrimination system and method, which solves the technical problem that existing technologies cannot achieve rapid early warning based on changes in upstream water volume through an upstream water volume prediction module, a downstream water volume difference module, and a reservoir redundancy analysis module.

[0007] According to one aspect of this application, a fuzzy recognition-based reservoir flood control safety chain discrimination system is provided, including a storage module that stores at least the reservoir's past flood periods. The stored data in the storage module is uploaded to a cloud database. The system also includes an upstream water volume estimation module, a downstream water volume difference module connected to the upstream water volume estimation module, and a reservoir redundancy analysis module connected to the downstream water volume difference module. The storage module synchronously stores data from the upstream water volume estimation module, the downstream water volume difference module, and the reservoir redundancy analysis module. The upstream water volume estimation module contains a prediction model, the downstream water volume difference module contains a calculation model, and the reservoir redundancy analysis module contains an analysis model.

[0008] In some embodiments, the prediction model receives upstream water volume data and performs water volume prediction; the model is as follows:

[0009] Ι 上游水量 =∑(Q 上游一段 +Q 上游二段 +…+Q 上游n段 )

[0010] Q 上游n段 =S n v n

[0011] Among them, I 上游水量 Q represents the total water flow at the upstream location of the reservoir during the target time period; 上游n段 This represents the water flow rate of the upstream target section.

[0012] In some embodiments, the calculation model receives upstream total flow data and performs calculation of the water volume difference between upstream and downstream within a target time period. The calculation model is as follows:

[0013]

[0014] Among them, I 标量 This represents the standard difference in water volume between the upstream and downstream sections of the reservoir.

[0015] In some embodiments, the analysis model receives water volume difference data between upstream and downstream targets within a specific time period and performs reservoir redundancy determination, wherein when I 差值 When I is less than or equal to 70%, the analysis model does not perform redundant judgments; when I 差值 When the percentage is greater than 70%, the analysis model performs a redundancy check.

[0016] In some embodiments, the analysis model is:

[0017]

[0018] Among them, Q (v) The percentage of remaining water in the reservoir; Standard value for the difference between reservoir capacity and upstream water volume; The standard value of the difference between the reservoir's remaining capacity and the downstream water volume.

[0019] In some embodiments, the upstream water volume estimation module further includes a time period early warning analysis unit and a temporary storage unit for storing early warning information.

[0020] According to another aspect of this application, a fuzzy recognition method for determining a reservoir flood control safety chain is provided. This method is executed by a processor and includes:

[0021] Obtain the total water flow at the upstream location of the reservoir within a target time period to obtain the upstream total water volume dataset;

[0022] Obtain the total water flow at the downstream location of the reservoir within a target time period to obtain a dataset of total downstream water volume.

[0023] Perform water volume difference calculation on the upstream total water volume dataset and the downstream total water volume dataset to obtain the difference dataset;

[0024] Based on the difference dataset, reservoir redundancy judgment is performed to obtain the proportion of reservoir surplus.

[0025] In some embodiments, the reservoir redundancy determination is performed as follows:

[0026] When I 差值 When I is less than or equal to 70%, redundant judgment is not performed; when I 差值 If the percentage is greater than 70%, a redundancy check will be performed.

[0027] Compared with the prior art, this application has the following advantages and beneficial effects: This application realizes reservoir flood control safety early warning based on changes in upstream water volume through upstream water volume prediction module, downstream water volume difference module and reservoir redundancy analysis module. Unlike the prior art, which requires the combination of multiple data information for prediction, this application can realize chain-like reservoir flood control early warning simply by relying on upstream water volume data. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the discrimination system of this application;

[0030] Figure 2 This is a flowchart of the discrimination method in this application. Detailed Implementation

[0031] The following will refer to the appendices in the embodiments of this application. Figure 1-2 The technical solutions in the embodiments of this application will be clearly and completely described together. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0032] Application Overview

[0033] Current research on flood control safety assessment, both domestically and internationally, primarily focuses on establishing flood control safety assessment index systems to comprehensively evaluate the flood control safety levels of water conservancy projects, river basins, and cities. However, it fails to consider the impact of real-time flood control scheduling and the dynamic changes in torrential rain and floodwaters on the safety levels of flood control projects, and its assessment of flood control safety remains at a macro-level. It is not difficult to observe that upstream water levels of reservoirs often exhibit abnormal changes long before floods occur, but these abnormal changes are often overlooked. This is because current flood warning systems require specific anomalies in data from multiple levels before issuing an alert, which poses a severe challenge to the flood control capabilities of reservoirs.

[0034] Based on the aforementioned problems, this application aims to maximize the flood control early warning capability of reservoirs by relying on fewer factors. It designs a discrimination system consisting of an upstream water volume prediction module, a downstream water volume difference module, and a reservoir redundancy analysis module. By predicting the changes in upstream water volume at different times, combining the downstream difference with the upstream water volume, and taking into account the reservoir's surplus capacity, it can achieve rapid chain-like flood control early warning for reservoirs based on upstream water volume changes.

[0035] The discrimination system of this embodiment will be described in detail below:

[0036] A fuzzy recognition-based reservoir flood control safety chain discrimination system includes a storage module that stores at least the reservoir's past flood periods. The stored data is uploaded to a cloud database. The memory can be used to store computer programs and / or modules. The processor performs various functions by running or executing the computer programs and / or modules stored in the memory and by accessing the data stored in the memory. The memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a given function, etc.; the data storage area can store data created based on device usage, etc. Furthermore, the memory can include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards, secure digital cards, flash memory cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices. The past flood periods of the reservoir refer to the periods during which past floods caused by floods occurred in the target reservoir, and this data can serve as effective reference and comparison data.

[0037] The system also includes an upstream water volume estimation module, a downstream water volume difference module connected to the upstream water volume estimation module, and a reservoir redundancy analysis module connected to the downstream water volume difference module. A storage module synchronously stores data from these modules. Specifically, the upstream water volume estimation module acquires segmented water flow data from the upstream water level, forming a dataset of total water flow within a target time period at the upstream location of the reservoir. The downstream water volume difference module then calculates the difference between the upstream and downstream water volumes within the target time period. Finally, the reservoir redundancy analysis module analyzes the upstream and downstream water volume difference data, performs reservoir redundancy judgment, and issues flood warnings based on any abnormal judgment data. It is understood that one or more modules are stored in memory and executed by a processor to complete this application. One or more modules can be a series of computer program instruction segments capable of performing specific functions, describing the execution process of the computer program. For example, the computer program can be divided into an upstream water volume estimation module, a downstream water volume difference module, and a reservoir redundancy analysis module, with the functions of each module as described above.

[0038] To ensure the aforementioned modules can better fulfill their respective functions, this embodiment configures a prediction model within the upstream water volume prediction module, a calculation model within the downstream water volume difference module, and an analysis model within the reservoir redundancy analysis module. In detail,

[0039] The prediction model receives upstream water volume data and performs water volume prediction. The model is as follows:

[0040] Ι 上游水量 =∑(Q 上游一段 +Q 上游二段 +…+Q上游n段 )

[0041] Q 上游n段 =S n v n

[0042] Among them, I 上游水量 Q represents the total water flow at the upstream location of the reservoir during the target time period; 上游n段 This refers to the water flow rate of the upstream target section. For example, when the upstream of the reservoir is divided into 3 monitoring areas, when Q... 上游一段 =650m 3 Q 上游二段 =620m 3 Q 上游三段 =1020m 3 At that time, I 上游水量 =650+620+1020=2290m 3 .

[0043] Next, the calculation model receives the upstream total water flow data and performs the calculation of the water volume difference between the upstream and downstream targets within the same time period. The calculation model is as follows:

[0044]

[0045] Among them, I 标量 This represents the standard difference in water volume between the upstream and downstream sections of the reservoir.

[0046] For example, when I 上游水量 =650+620+1020=2290m 3 I 下游水量 =1120m 3 I 标量 =1200, then I 差值 =97.5%.

[0047] Furthermore, the analysis model receives water volume difference data between upstream and downstream targets within a specific time period and performs reservoir redundancy assessment. Specifically, when I... 差值 When I is less than or equal to 70%, the analysis model does not perform redundant judgments; when I 差值 When the value is greater than 70%, the analysis model performs a redundancy check. For example, when I... 标量 = 97.5% represents I 差值 When the percentage is greater than 70%, the analysis model performs a redundancy check, as follows:

[0048] Redundancy checks are performed using an analytical model, which is as follows:

[0049]

[0050] Among them, Q (v) The percentage of remaining water in the reservoir; Standard value for the difference between reservoir capacity and upstream water volume; The standard value of the difference between the reservoir's remaining capacity and the downstream water volume. For example, based on the aforementioned example, substituting relevant exemplary data, where... I 水库余量 =1800m 3 Then Q (v) =108.8%, at this point the proportion Q (v) A reading exceeding 100% indicates that the reservoir will exceed its redundancy capacity, necessitating a reservoir flood warning. It should be noted that this embodiment does not aim to provide a final flood warning for reservoirs; rather, its purpose is to issue a warning as early as possible based on upstream water volume data, reminding relevant departments to prepare accordingly.

[0051] In other possible implementations, the technical features are basically the same as those described in the foregoing embodiments. The same technical features and solutions will not be repeated here; only the differences will be described. The upstream water volume estimation module also includes a time-period early warning analysis unit and a temporary storage unit for storing early warning information. The time-period early warning analysis unit includes a primary early warning subunit and a secondary early warning subunit. It should be noted that, to further improve the accuracy of early warnings, an early warning analysis unit and a temporary storage unit can also be configured within the upstream water volume estimation module to perform early warning analysis on other environmental conditions, including but not limited to wind speed and rainfall. Simultaneously, to improve the early warning efficiency of the early warning analysis unit and prevent redundancy of early warning data, the early warning analysis unit also connects the primary and secondary early warning subunits.

[0052] Exemplary methods

[0053] A fuzzy recognition-based method for determining the safety chain of reservoir flood control, executed by a processor, includes:

[0054] Obtain the total water flow at the upstream location of the reservoir within a target time period to obtain the upstream total water volume dataset;

[0055] Obtain the total water flow at the downstream location of the reservoir within a target time period to obtain a dataset of total downstream water volume.

[0056] Perform water volume difference calculation on the upstream total water volume dataset and the downstream total water volume dataset to obtain the difference dataset;

[0057] Based on the difference dataset, reservoir redundancy judgment is performed to obtain the proportion of reservoir surplus.

[0058] Among them, the reservoir redundancy judgment is performed as follows: when I 差值 When I is less than or equal to 70%, redundant judgment is not performed; when I 差值 If the percentage is greater than 70%, a redundancy check will be performed.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of this application. It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that it can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of this application is defined by the appended claims rather than the foregoing description, and thus all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0060] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A fuzzy recognition reservoir flood control safety chain discriminant system, comprising a storage module, which at least stores past water level rising time periods of the reservoir, and the storage module stores data uploaded to a cloud database, characterized in that, It also includes an upstream water volume prediction module, a downstream water volume difference module connected to the upstream water volume prediction module, and a reservoir redundancy analysis module connected to the downstream water volume difference module. The storage module synchronously stores data from the upstream water volume prediction module, the downstream water volume difference module, and the reservoir redundancy analysis module. The upstream water volume prediction module is configured with a prediction model, the downstream water volume difference module is configured with a calculation model, and the reservoir redundancy analysis module is configured with an analysis model. The analysis model receives water volume difference data within a target time period from the upstream and downstream sides obtained from the calculation model. Perform reservoir redundancy judgment, where when When the percentage is less than or equal to 70%, the analysis model does not perform redundant checks; when... When the percentage is greater than 70%, the analysis model performs a redundancy check; the analysis model is as follows: ; wherein, is a reservoir surplus ratio; is a standard value of the difference between the reservoir surplus and the upstream water volume; is a standard value of the difference between the reservoir surplus and the downstream water volume, when the reservoir surplus ratio exceeds 100%, it means that the reservoir will exceed its own redundancy, at which time the reservoir flood control warning needs to be carried out.

2. The system of claim 1, wherein, The estimation model receives upstream water quantity data to perform water quantity estimation, and the model is as follows: ; ; wherein, is the total amount of water flow in the target time period at the location upstream of the reservoir; is the water flow of the target section upstream.

3. The system of claim 2, wherein, The calculation model receives upstream water flow total quantity data to perform water quantity difference calculation in the target time period of the upstream and downstream, and the calculation model is as follows: ; wherein, is the standard deviation of the water volume between the upstream and downstream of the reservoir.

4. The system of claim 1, wherein, The upstream water quantity estimation module further includes a time period early warning analysis unit and a temporary storage unit for performing data storage on early warning information.

5. The system of claim 4, wherein, The time period early warning analysis unit includes a first early warning subunit and a second early warning subunit.

6. A fuzzy-identified reservoir flood control safety chain discriminant method, the method being executed by a processor, characterized in that, It includes: Obtain the total quantity of water flow in the target time period at the upstream position of the reservoir to obtain the upstream water quantity total quantity data set; Obtain the total quantity of water flow in the target time period at the downstream position of the reservoir to obtain the downstream water quantity total quantity data set; Perform water quantity difference calculation on the upstream water quantity total quantity data set and the downstream water quantity total quantity data set to obtain a difference data set; Based on the difference data set, perform reservoir redundancy judgment to obtain a reservoir quantity proportion, and the reservoir redundancy judgment is as follows: When When the redundancy determination is not performed when the percentage is less than or equal to 70%. When When the percentage is greater than 70%, a redundancy determination is performed; The calculation formula of the reservoir quantity proportion is as follows: ; wherein, is a reservoir remaining amount ratio; is a standard value of a difference between the reservoir remaining amount and the upstream water amount; is a standard value of a difference between the reservoir remaining amount and the downstream water amount; when the ratio exceeds 100%, it represents that the reservoir will exceed its own redundancy amount, at which time reservoir flood control warning is required. is a standard value of a difference between the reservoir remaining amount and the upstream water amount;

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