Reservoir ecological regulation decision-making method based on risk classification matching of antibiotic-resistant bacteria

Abstract

This invention discloses a reservoir ecological scheduling decision-making method based on antibiotic-resistant bacteria risk classification and matching, belonging to the field of smart water conservancy and ecohydraulics. The method first acquires ARB biological information and three types of hydrodynamic information of the water body: hydraulic retention, vertical structure, and sediment interface. Then, based on preset rules, the current ARB risk is diagnosed according to its dominant hydrodynamic cause type—hydraulic retention, vertical isolation, or sediment release. Next, based on the diagnosis result, a uniquely matching basic scheduling rule is called from a pre-set scheduling rule library. Finally, this rule is fused with the reservoir's multi-objective constraints to generate executable scheduling instructions, and closed-loop evaluation and parameter correction are performed based on effect feedback. This invention structures the complex ARB biological risk into a general decision-making process of "cause diagnosis—paradigm matching," providing a systematic and scalable top-level technical solution for proactive prevention and control of reservoir ARB risks.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of smart water conservancy and ecohydraulics, specifically involving a reservoir ecological scheduling decision-making method based on antibiotic resistance bacteria risk classification matching. Background Technology

[0002] As critical drinking water sources, reservoirs are facing a severe threat to water quality safety from emerging microbial pollutants such as antibiotic-resistant bacteria (ARBs) and resistance genes. These pollutants, after entering reservoirs through various pathways, can persist, proliferate, and even be released secondaryly under specific hydrodynamic conditions, including long-term water retention, stable thermal stratification, and active sediment-water interfaces, posing a potential biological risk to drinking water safety.

[0003] Currently, the design and operation of reservoir scheduling systems primarily focus on traditional objectives such as flood control, water supply, power generation, and eutrophication control. For emerging biofouling risks like ARBs, existing technologies mainly focus on two directions: firstly, upgrading water treatment processes at the point of water supply for removal; and secondly, long-term pollution source control at the watershed scale. However, within the core water storage and purification unit of reservoirs, effective scheduling technologies for proactive, rapid, and precise intervention against ARB risks have not yet been developed.

[0004] Existing water quality-oriented scheduling methods typically operate by setting fixed thresholds for physical or chemical indicators such as water temperature, dissolved oxygen, and nutrient concentration. However, the formation of ARB risk is the result of a complex coupling between biological processes and the dynamic environment, with its dominant factors dynamically evolving with hydrological conditions, seasonal variations, and reservoir operating status. Directly using ARB concentration as a scheduling target would be difficult to translate into feasible scheduling instructions due to its complex biological characteristics, inherent monitoring lag, and potential conflicts with other reservoir scheduling objectives. Therefore, current technologies lack a decision-making logic and technical method that can dynamically analyze the biological risk of ARB into actionable hydrodynamic interventions and is compatible with the multi-objective scheduling system of reservoirs.

[0005] This invention aims to overcome the shortcomings of the existing technologies and provide a systematic method for reservoir ecological scheduling decision-making. This method constructs a standardized decision-making process of "risk monitoring - cause diagnosis - strategy matching," structuring the complex ARB biological risk problem into a classification problem that can correspond to different hydrodynamic regulation modes. This provides a novel technical solution for the proactive and forward-looking prevention and control of ARB risks within reservoir systems. Summary of the Invention

[0006] To address the shortcomings of existing reservoir scheduling technologies in effectively converting complex ARB biological risks into executable hydrodynamic control commands and lacking targeted and systematic proactive prevention and control solutions, this invention provides a logically clear and closed-loop operational reservoir ecological scheduling decision-making method and related devices based on ARB risk cause classification and matching, achieving targeted and efficient prevention and control of ARB risks and synergistic optimization of multi-objective reservoir operation.

[0007] A reservoir ecological scheduling decision-making method based on antibiotic resistance bacteria risk classification and matching includes the following steps: S1. Information acquisition step: Acquire ARB biological information reflecting the risk of antibiotic resistance in the reservoir, and hydrodynamic information reflecting the hydraulic retention state, vertical structure state, and sediment interface state of the water body; S2. Risk cause diagnosis step: Based on the reservoir ARB biological information and hydrodynamic information acquired in step S1, diagnose the current ARB risk as its dominant hydrodynamic cause type through preset classification rules, wherein the dominant hydrodynamic cause type is at least one of hydraulic retention type, vertical isolation type, and sediment release type; S3. Scheduling rule matching step: Based on the dominant hydrodynamic cause type diagnosed in step S2, call the basic scheduling rule uniquely corresponding to the hydrodynamic cause type from the preset basic scheduling rule library; the scheduling rules pre-stored in the scheduling rule library are associated with the dominant hydrodynamic cause type, and different scheduling rules have differentiated core scheduling objectives; S4. S3. Dispatch instruction generation step: Combine the basic dispatch rules called in step S3 with the multi-objective operation constraints of the reservoir to generate an executable dispatch instruction; S5. Feedback adjustment step: Based on the effect feedback information obtained after the dispatch instruction generated in step S4 is executed, evaluate the control effect, and make adaptive corrections to subsequent decisions or parameters based on the evaluation results.

[0008] Furthermore, the conditions for determining the dominant hydrodynamic cause type as hydraulic retention type in step S2 include: the ARB risk level exceeds a preset threshold, and the hydrodynamic information indicates that the actual hydraulic residence time of the water body is longer than a preset benchmark residence time.

[0009] Furthermore, the conditions for determining the dominant hydrodynamic cause type as vertically isolated in step S2 include: the ARB risk significantly accumulates in a certain vertical water layer, specifically manifested as the ARB concentration or abundance value of that water layer exceeding twice the preset multiple threshold of the average value across the entire water depth; and the hydrodynamic information indicates that the vertical stratification stability parameter of the water body is higher than the preset stability threshold.

[0010] Furthermore, the conditions for determining the dominant hydrodynamic cause type as sediment release type in step S2 include: the increase in ARB risk and the increase in sediment disturbance risk inferred from the hydrodynamic information are positively correlated in time and space.

[0011] Furthermore, the core scheduling objective of the basic scheduling rules associated with the aforementioned hydraulic retention causes is to reduce the average hydraulic residence time of the water body.

[0012] Furthermore, the core scheduling rules associated with the aforementioned vertical isolation cause have the objective of disrupting or weakening the stable vertical thermal / dissolved oxygen stratification structure of the water body.

[0013] Furthermore, the core scheduling objective of the basic scheduling rules associated with the aforementioned sediment release-type causes is to maintain or create hydrodynamic conditions that inhibit sediment resuspension.

[0014] A reservoir ecological scheduling decision-making system based on antibiotic resistance risk classification and matching includes: a computer-readable storage medium and a processor; the computer-readable storage medium is used to store executable instructions; the processor is used to read the executable instructions stored in the computer-readable storage medium and execute the reservoir ecological scheduling decision-making method based on antibiotic resistance risk classification and matching as described above.

[0015] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir ecological scheduling decision-making method based on antibiotic resistance bacteria risk classification matching as described above.

[0016] Compared with the prior art, the present invention has the following significant advantages:

[0017] (1) This invention establishes a systematic decision-making architecture from ARB risk identification to corresponding hydrodynamic cause diagnosis and scheduling rule matching, transforming the complex ARB biological risk problem into a clear, programmable and executable scheduling process, breaking through the limitations of traditional single indicator regulation, and realizing targeted and precise intervention of ARB risk.

[0018] (2) The present invention substantially protects the cognitive method of hydrodynamic attribution of ARB risk and the decision-making logic of matching targeted intervention rules for different causes. It does not rely on specific monitoring equipment, calculation models or engineering facilities, and provides top-level design protection for various technical implementation schemes. It has a wide range of applications.

[0019] (3) The decision-making framework has good compatibility and scalability. It can be embedded as an independent functional module into the existing reservoir integrated scheduling system. Through the fusion of multi-objective constraints, it can naturally achieve the synergistic optimization of ARB risk prevention and control and traditional scheduling objectives without the need for large-scale transformation of the existing system.

[0020] (4) This invention achieves adaptive correction of decision-making logic and scheduling parameters through a closed-loop evaluation mechanism, which improves the dynamic adaptability and long-term effectiveness of the scheduling scheme and provides a new technical path for proactive and forward-looking prevention and control of reservoir ARB risk. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the overall framework and process of the reservoir ecological scheduling decision-making method based on risk cause classification and matching proposed in this invention.

[0022] Figure 2 This is a logical diagram of the ARB risk cause diagnosis decision proposed in this invention, showing the decision tree from multi-source information input to the determination of the three major cause types.

[0023] Figure 3 This is a schematic diagram illustrating the core mapping relationship between the ARB risk causes and the basic scheduling rules proposed in this invention. It intuitively shows the one-to-one correspondence between the three hydrodynamic cause types and the three basic scheduling rules. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0025] Please see Figure 1-3 The first aspect of this invention provides a reservoir ecological scheduling decision-making method based on antibiotic resistance bacteria risk classification and matching. Its core technical solution involves constructing a standardized decision-making process of "risk monitoring - cause diagnosis - strategy matching - instruction generation - closed-loop evaluation." This method structures the ARB biological risk into a classification problem driven by three key hydrodynamic states, and pre-sets a set of basic scheduling intervention logic for each state. Through cause-targeted matching of scheduling rules, combined with multi-objective constraints, executable instructions are generated. Specifically, the method includes the following steps:

[0026] S1. Information Acquisition Steps: These steps involve continuously collecting two types of core information reflecting the reservoir's ARB risk level and its potential hydrodynamic driving factors. Specifically, this includes:

[0027] (1) Reservoir ARB bioinformation: quantitative data characterizing the presence and dynamic changes of ARBs, including the absolute abundance of ARBs or their abundance change rate.

[0028] (2) Hydrodynamic information: Quantitative data characterizing the three types of key hydrodynamic conditions that may drive ARB risk, specifically:

[0029] ① Hydraulic retention state data: an indicator reflecting the overall replacement rate of water, preferably hydraulic residence time.

[0030] ② Vertical structural state data: an index reflecting the stability of thermal / dissolved oxygen stratification and vertical mixing capacity of the water body, preferably the stratification strength index.

[0031] ③ Sediment interface state data: Indicators reflecting sediment disturbance and resuspension risk, preferably at least one of near-bottom turbidity, water level variability or bottom shear stress.

[0032] S2. Risk Cause Diagnosis Steps: Based on the information obtained above, and according to the preset classification logic, the dominant hydrodynamic cause type of the current ARB risk is automatically diagnosed. The dominant hydrodynamic cause type is specifically divided into:

[0033] (1) Hydraulic retention type: When the risk of ARB increases (the absolute abundance of ARB exceeds the preset concentration threshold or its rate of change is positive and exceeds the preset rate of change threshold), and the hydraulic retention time of the water body exceeds the preset retention threshold, it is diagnosed as this type; this type represents the risk of ARB accumulation due to insufficient water replacement.

[0034] (2) Vertical isolation type: When the risk of ARB increases significantly in a specific vertical water layer (such as the bottom water body) and the water stratification intensity index exceeds the preset stratification threshold, it is diagnosed as this type; this type represents the risk of ARB vertical isolation and enrichment in a specific water layer due to the stable stratification of water body thermal / dissolved oxygen.

[0035] (3) Sediment release type: When the risk of ARB increases and sediment interface disturbance indicators (such as a sudden increase in near bottom turbidity, water level variability exceeding the limit or bottom shear stress exceeding the standard) are significantly positively correlated in the spatiotemporal dimensions, this type is diagnosed; this type represents the risk of ARB resuspension and release from sediments due to hydrodynamic disturbance.

[0036] S3. Scheduling rule matching step: A basic scheduling rule library corresponding one-to-one with the above three types of hydrodynamic genesis is pre-set, and the matching basic scheduling rule is automatically called according to the gene diagnosis results. The basic scheduling rule library specifically includes:

[0037] (1) Accelerated replacement rule: Corresponding to the hydraulic retention type, its core logic is to shorten the hydraulic retention time and accelerate the water body renewal of the reservoir; the scheduling operation tends to increase the outflow and optimize the combination of discharge orifices to improve the water body replacement efficiency.

[0038] (2) Forced mixing rule: Corresponding to the vertical isolation type, its core logic is to destroy the vertical stratification of the reservoir water and enhance the material exchange between the layers; the scheduling operation tends to adjust the combination of multiple elevation orifice discharges, or use pulse discharge and other methods to induce vertical mixing of the water.

[0039] (3) Stability inhibition rule: Corresponding to the sediment release type of cause, its core logic is to reduce the shear force at the sediment-water interface of the reservoir and inhibit sediment resuspension; the scheduling operation tends to control the water level variability, smooth the outflow process, and cautiously activate the bottom spillway facilities.

[0040] S4. Instruction generation steps: The basic scheduling rules invoked are integrated and optimized with the multi-objective operational constraints of the reservoir, such as flood control, water supply, and power generation, to finally generate an executable scheduling instruction containing the specific outflow process, the operation sequence of the spillway facilities, and the operating parameters; the multi-objective operational constraints include at least the reservoir flood limit water level constraint, the minimum ecological outflow constraint, the water supply guarantee rate constraint, and the power generation scheduling boundary constraint.

[0041] S5. Closed-loop evaluation steps: After the scheduling instructions are executed, new risk and hydrodynamic information is continuously collected through the information acquisition steps to evaluate the effectiveness of ARB risk control.

[0042] (1) If the ARB risk is mitigated as expected and no other scheduling target conflict is triggered, the current decision logic and scheduling rule parameters shall be maintained.

[0043] (2) If the ARB risk is not mitigated as expected, or new risk characteristics appear, or scheduling target conflicts are triggered, the decision logic is restarted (returning to the cause diagnosis step), or the preset thresholds and operating parameters in the scheduling rules are adaptively modified to form a closed-loop adaptive optimization.

[0044] The following is a specific embodiment, combined with Figures 1-3 Taking a typical deep-water reservoir experiencing an increased risk of ARB (Automatic Reservoir Boredom) during the summer thermal stratification stabilization period as a scenario, this paper elaborates on the specific implementation process of the decision-making method described in this invention.

[0045] First, the information acquisition step is performed. The system receives multi-source data from reservoir online monitoring, laboratory analysis, and hydrodynamic model calculations, and integrates them into two core types of information to provide data support for subsequent diagnosis.

[0046] 1. Reservoir ARB Bioinformatics: The copy number concentration of the sulfonamide antibiotic resistance gene (sul) in reservoir water samples was obtained using conventional quantitative qPCR methods in this field. This indicator serves as a quantitative proxy for ARB risk, and its dynamic changes reflect the net accumulation or reduction trend of ARB in the water body.

[0047] 2. Hydrodynamic Information: Basic monitoring data obtained through monitoring equipment such as water temperature profilers, flow meters, water level gauges, and near-bottom turbidity meters are used to calculate three key hydrodynamic state characterization indicators. Among them, the hydraulic retention state is characterized by hydraulic residence time (HRT), the vertical structure state is characterized by the vertical temperature gradient (ΔT / Δz) to represent the water stratification intensity, and the sediment interface state is characterized by near-bottom turbidity and water level change rate (|dH / dt|).

[0048] Both types of information are transmitted to the subsequent risk cause diagnosis stage in real-time or near real-time.

[0049] Next, the risk cause diagnosis step is executed. The system inputs the acquired reservoir ARB biological information and hydrodynamic information into the diagnosis module, and performs the diagnosis according to the preset classification logic rules (see [link to specific judgment logic]). Figure 2 The diagram shown illustrates the ARB risk cause diagnosis decision logic for automatic diagnosis.

[0050] In this embodiment, the diagnostic information input is: ① The concentration of the sul gene copy number in the area near the water intake has been increasing for two consecutive days, and its rate of change exceeds the preset ARB risk change rate threshold G. c (Set to 0.3 log) 10 ① The water volume is within the preset baseline threshold range, indicating normal water replacement. ② The ΔT / Δz value is significantly higher than the preset stratification threshold (0.5℃ / m), indicating the presence of a strong thermocline in the reservoir water and a stable vertical stratification structure. ③ The near-bottom turbidity shows no significant fluctuations, the water level change rate is within a stable range, and there is no obvious disturbance at the sediment interface.

[0051] Based on the above information, the system sequentially determines the causes according to preset logic: excluding hydraulic retention, all criteria for vertical isolation are met, and the criteria for sediment release are not met. Ultimately, the system comprehensively diagnoses that the dominant hydrodynamic cause of the current ARB risk is vertical isolation. This method avoids misjudgments caused by relying solely on ARB concentration-driven scheduling—such as misjudging upstream input as reservoir enrichment and blindly increasing discharge, which could actually transport high-concentration water downstream. This invention achieves accurate attribution of ARB risk sources through three independent criteria: hydraulic retention, vertical structure, and sediment interface, ensuring targeted scheduling interventions.

[0052] Subsequently, hydrodynamic information is processed. Based on the aforementioned vertical isolation diagnostic results, the system automatically retrieves the uniquely matching basic scheduling rule from the pre-set basic scheduling rule base (see the mapping relationship between cause and rule for details). Figure 3The diagram shown illustrates the mapping relationship between ARB risk causes and basic scheduling rules, namely, the forced mixing rule. The core scheduling objective of this rule is to disrupt the stable vertical thermal stratification structure of the reservoir water and enhance the interlayer material exchange. Based on this core objective, the rule outputs a preliminary scheduling guidance intention: to induce vertical mixing of the water by using a combination of operations at multiple elevation spillways.

[0053] Then, the scheduling instruction generation step is executed. The system inputs the scheduling guidance intention of the above-mentioned mandatory mixed rules, along with the current multi-objective operational constraints of the reservoir, into the hydrodynamic scheduling optimization algorithm. The multi-objective operational constraints include at least the reservoir flood control limit water level constraint, minimum ecological discharge flow constraint, downstream water supply guarantee rate constraint, and power generation scheduling boundary constraint. The optimization algorithm solves the problem under the premise of satisfying all hard constraints, and finally generates specific and executable scheduling instructions. In this embodiment, the generated scheduling instruction is: maintain a constant total outflow from the reservoir for the next 12 hours, and adopt an operation mode in which the surface spillway and the middle spillway are intermittently opened in a 7:3 ratio, with the opening status of the spillway switching every 2 hours.

[0054] After the aforementioned scheduling instructions are issued and executed, the system immediately initiates the closed-loop evaluation step and enters the adaptive optimization phase. During and after the execution of the scheduling instructions, the system continuously collects updated ARB biological and hydrodynamic information through the information acquisition step, and evaluates the effectiveness of ARB risk regulation based on this feedback information.

[0055] In this embodiment, feedback information collected after the execution of the scheduling command shows that the vertical temperature gradient (ΔT / Δz) of the reservoir water body is slowly decreasing, and the vertical stratification structure is beginning to weaken. However, the decrease in the sul gene copy number concentration in the area near the water intake has not reached the preset risk mitigation target. After evaluation, the system determines that the current forced mixing rule is not strong enough and has not achieved the expected ARB risk control effect. It then triggers an adaptive response for parameter correction: instructing the scheduling command generation module to enhance the vertical mixing intensity of the water body in the next decision cycle, shortening the alternation cycle between the surface and middle layer spillways from 2 hours to 1 hour, generating a new scheduling command and executing it.

[0056] If feedback indicates that the ARB risk has not been mitigated or new risk characteristics have emerged, the system will trigger an adaptive response for re-diagnosis, re-inputting the updated full information into the risk cause diagnosis module and initiating a new round of complete decision-making. This closed-loop assessment and adaptive correction process continues until the reservoir's ARB risk drops to a preset acceptable level and there are no other scheduling conflicts.

[0057] This embodiment is merely a specific application example provided to clearly illustrate the technical solution of the present invention, and is not intended to limit the scope of the invention. The core of the present invention lies in constructing and executing the aforementioned information acquisition, conducting ARB risk cause diagnosis and scheduling rule matching, generating and running scheduling instructions, and finally completing a standardized decision-making logic chain for closed-loop assessment of ARB risk. Specific technical details involved in the embodiment can be replaced or adjusted according to the actual hydrological and water quality conditions, monitoring capabilities, and operational requirements of the reservoir.

[0058] The indicators characterizing the ARB risk in reservoirs can be replaced by conventional ARB quantitative indicators in this field, such as the copy number concentration of other drug-resistant genes and the number of viable ARB bacteria. The detection methods can be correspondingly high-throughput sequencing, plate culture counting, etc.

[0059] The indicators characterizing the three types of hydrodynamic states can be replaced with other commonly used indicators in this field. For example, the hydraulic retention state can be characterized by water age and water replacement rate, the vertical structure state can be characterized by dissolved oxygen vertical gradient to represent stratification intensity, and the sediment interface state can be characterized by bottom shear stress to represent disturbance risk.

[0060] The specific operation of the basic scheduling rules can be adjusted according to the engineering facilities conditions of the reservoir. For example, the forced mixing rule can be implemented by means of pulse discharge and mixing of bottom and surface water, while the accelerated replacement rule can be implemented by means of optimizing the combination of discharge orifices and increasing the outflow.

[0061] The optimization algorithm for generating scheduling instructions can be a conventional multi-objective optimization algorithm in this field, such as genetic algorithm, particle swarm optimization, etc.

[0062] Any reservoir ARB risk control decision-making scheme that, under the concept of this invention, identifies the dominant hydrodynamic causes of ARB risk by identifying three key hydrodynamic states—hydraulic retention, vertical structure, and sediment interface—and accordingly matches the corresponding basic scheduling rules, and generates scheduling instructions by combining reservoir multi-objective operation constraints and achieves closed-loop optimization through feedback information, falls within the protection scope defined by the claims of this invention, regardless of how the specific monitoring indicators, calculation methods, or operation methods vary.

[0063] Compared with the prior art, the present invention has the following features and effects:

[0064] Structured decision-making logic enables targeted intervention for ARB risk: This invention transforms the complex biological risk of antibiotic-resistant bacteria (ARB) into three quantifiable hydrodynamic causes—hydraulic retention, vertical isolation, and sediment release—through a standardized process of "information acquisition—risk cause diagnosis—scheduling rule matching—scheduling instruction generation—feedback adjustment." It also uniquely matches each cause with a corresponding basic scheduling rule (such as accelerated replacement, forced mixing, and stable inhibition), breaking through the limitations of traditional single-indicator control and achieving precise and proactive prevention and control of ARB risk.

[0065] Closed-loop adaptive optimization enhances the dynamic adaptability of scheduling: The feedback adjustment step adaptively modifies subsequent decisions or parameters based on the effect information after scheduling execution, forming a closed-loop evaluation and continuous optimization mechanism. This mechanism can cope with dynamic changes in hydrology, water quality, and ARB risks, ensuring the long-term effectiveness and robustness of the scheduling scheme.

[0066] Compatible with multi-objective constraints and coordinated with traditional scheduling objectives: This invention naturally integrates traditional multi-objective constraints such as reservoir flood control, water supply, and power generation in the scheduling instruction generation step. It can embed ARB risk prevention and control into daily operation without large-scale modification of the existing scheduling system, thereby achieving synergistic optimization of ecological security and engineering benefits.

[0067] Another aspect of the present invention provides a reservoir ecological scheduling decision system based on antibiotic resistance risk classification matching, comprising: a computer-readable storage medium and a processor; the computer-readable storage medium is used to store executable instructions; the processor is used to read the executable instructions stored in the computer-readable storage medium and execute the reservoir ecological scheduling decision method based on antibiotic resistance risk classification matching described in the first aspect.

[0068] In another aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir ecological scheduling decision-making method based on antibiotic resistance bacteria risk classification matching as described in the first aspect.

[0069] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A reservoir ecological scheduling decision-making method based on antibiotic-resistant bacteria risk classification and matching, characterized in that, Includes the following steps: S1. Information Acquisition Steps: Acquire ARB biological information reflecting the risk of antibiotic resistance in the reservoir, as well as hydrodynamic information reflecting the hydraulic retention state, vertical structure state, and sediment interface state of the water body; S2. Risk Cause Diagnosis Step: Based on the reservoir ARB biological information and hydrodynamic information obtained in step S1, the current ARB risk is diagnosed as its dominant hydrodynamic cause type through preset classification rules. The dominant hydrodynamic cause type is at least one of hydraulic retention type, vertical isolation type, and sediment release type. S3. Scheduling rule matching step: Based on the dominant hydrodynamic cause type diagnosed in step S2, call the basic scheduling rule that uniquely corresponds to the hydrodynamic cause type from the pre-set basic scheduling rule library; the scheduling rules pre-stored in the scheduling rule library are associated with the dominant hydrodynamic cause type, and different scheduling rules have different core scheduling objectives; S4. Scheduling instruction generation step: Combine the basic scheduling rules invoked in step S3 with the multi-objective operation constraints of the reservoir to generate executable scheduling instructions; S5. Feedback Adjustment Step: Based on the effect feedback information obtained after the execution of the scheduling instruction generated in step S4, evaluate the adjustment effect, and make adaptive corrections to subsequent decisions or parameters based on the evaluation results.

2. The method according to claim 1, characterized in that, The conditions for determining the dominant hydrodynamic cause type as hydraulic retention type in step S2 include: the ARB risk level exceeds a preset threshold, and the hydrodynamic information indicates that the actual hydraulic residence time of the water body is longer than the preset benchmark residence time.

3. The method according to claim 1, characterized in that, The conditions for determining the dominant hydrodynamic origin type as vertically isolated in step S2 include: the ARB risk significantly accumulates in a certain vertical water layer, specifically manifested as the ARB concentration or abundance value of that water layer exceeding twice the preset multiple threshold of the average value across the entire water depth; and the hydrodynamic information indicating the vertical stratification stability parameter of the water body is higher than the preset stability threshold.

4. The method according to claim 1, characterized in that, The conditions for determining the dominant hydrodynamic cause type as sediment release type in step S2 include: the increase in ARB risk and the increase in sediment disturbance risk inferred from the hydrodynamic information are positively correlated in time and space.

5. The method according to claim 1, characterized in that, The core scheduling objective of the basic scheduling rules associated with the aforementioned hydraulic retention causes is to reduce the average hydraulic residence time of the water body.

6. The method according to claim 1, characterized in that, The core scheduling rules associated with the aforementioned vertical isolation type formation have the core scheduling objective of disrupting or weakening the stable vertical thermal / dissolved oxygen stratification structure of the water body.

7. The method according to claim 1, characterized in that, The core scheduling objective of the basic scheduling rules associated with the aforementioned sediment release-type genesis is to maintain or create hydrodynamic conditions that inhibit sediment resuspension.

8. A reservoir ecological scheduling decision-making system based on antibiotic-resistant bacteria risk classification and matching, comprising: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the reservoir ecological scheduling decision-making method based on antibiotic resistance risk classification matching as described in any one of claims 1-7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir ecological scheduling decision-making method based on antibiotic resistance risk classification matching as described in any one of claims 1-7.

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