A water conservancy calculation-algorithm-computing power event-driven dynamic financing scheduling method, device and medium

Abstract

The application discloses a water conservancy calculation-algorithm-computing power event-driven dynamic financing scheduling method, equipment and medium, relates to the cross technical field of water conservancy engineering informatization, artificial intelligence and distributed computing, and the method comprises the following steps: obtaining current water conservancy event information and respectively generating a water conservancy event characteristic vector and a semantic enhancement vector; inputting the two into an event-three calculation demand mapping network to obtain three types of demand increments of calculation, algorithm and computing power; obtaining current available three calculation resources and screening candidate three calculation combinations with the demand increments as constraints; calculating the coupling adaptation degree of each candidate combination by using a coupling adaptation degree function and selecting an optimal three calculation combination; and generating a water conservancy event scheduling scheme containing resource arrangement strategies and storage-computing collaboration strategies according to the event severity level and the optimal three calculation combination. The application can avoid the collaborative scheduling failure problem caused by the space-time mismatch of three calculation resources, event-driven load mutation and safety isolation constraints in the water conservancy scene.

Description

Technical Field

[0001] This application relates to the interdisciplinary fields of water conservancy engineering informatization, artificial intelligence and distributed computing, and in particular to a water conservancy data-algorithm-computing power event-driven dynamic integrated scheduling method, equipment and medium. Background Technology

[0002] In the process of integrated watershed management and digital twin water conservancy construction, water conservancy business systems typically rely on multi-source heterogeneous data, various professional models and artificial intelligence algorithms, as well as distributed computing, storage, and network resources. However, in current water conservancy informatization practices, data, algorithms, and computing power are usually managed as independent resources. This leads to problems such as data not being ready, algorithms waiting for input, idle computing nodes, or inability to execute after resource scheduling, which seriously restricts the efficiency and reliability of business response.

[0003] The root cause of the aforementioned problems lies in the inherent limitations of digital twin water conservancy systems. Constrained by insufficient intelligent computing power and the lack of general artificial intelligence capabilities in the water conservancy field, existing digital twin systems are still inadequate in acquiring richer data, constructing higher-precision models, and providing more intelligent analysis, computation, and decision support. They fail to fully leverage the supporting role of computing power and large-scale models for "twin" and "intelligence." Specifically, this manifests in three key areas: First, massive amounts of multi-source sensing data are difficult to convert into computationally usable data at low cost, requiring extensive governance and enhancement algorithms. Second, implicit knowledge such as scheduling procedures, plans, and case studies is difficult to digitally reuse, necessitating the use of large-scale models in the water conservancy field for procedure-level understanding and generation. Third, the training and inference of large-scale models, in turn, place higher demands on intelligent computing power. Data production depends on algorithms, algorithm intelligence depends on large-scale models, and the application of large-scale models depends on computing power; these three elements form a mutually driving closed loop. This is the core driving force that distinguishes water conservancy's "three-computation integration" from general computing power scheduling, and it is also the fundamental problem that this application aims to solve.

[0004] Meanwhile, water-related events such as torrential rains, floods, droughts, water pollution, and geological disasters are characterized by their suddenness and strong time-varying nature, causing drastic changes in data access volume, algorithm call volume, and computing power load within a short period of time. Existing scheduling methods mostly respond passively after load changes occur, making it difficult to complete data prefetching, model warm-up, and computing power reservation before the event is triggered, thus affecting the system's ability to respond quickly and operate stably in emergency scenarios.

[0005] In addition, water conservancy production data is usually restricted by security policies and cannot be directly transmitted to public clouds or remote computing centers; however, large-scale hydrological simulation, deep learning inference, and training of large language models in the water conservancy field require strong computing power support. Moreover, the currently common storage and computing separation technology is mainly aimed at ordinary cloud computing scenarios and lacks a dedicated mechanism for water conservancy data security isolation, long-distance transmission, and edge-remote collaborative computing, making it difficult to effectively balance security and computing efficiency.

[0006] Furthermore, general-purpose large language models lack systematic embedding of water conservancy professional knowledge systems and industry rules. Directly using them for water conservancy forecasting and scheduling decision support may produce illusory results or results that do not conform to professional standards. At the same time, the training data of large language models in the water conservancy field is subject to security constraints, and the inference tasks have strong real-time requirements. Existing technologies have not yet formed a secure deployment and interpretation mechanism that is closely integrated with the water conservancy three-calculation scheduling framework, which makes the reliable application of intelligent models in actual business systems still face significant obstacles. Summary of the Invention

[0007] The purpose of this application is to provide a dynamic integrated scheduling method, device and medium for water conservancy data-algorithm-computing power event-driven scheduling, which can avoid the problem of collaborative scheduling failure caused by spatiotemporal mismatch of the three computing resources, event-driven load mutation and security isolation constraints in water conservancy scenarios, and realize dynamic integrated scheduling.

[0008] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a dynamic integrated scheduling method driven by water conservancy data, algorithms, and computing power events, including: Obtain current water conservancy event information, encode the current water conservancy event information to generate water conservancy event feature vectors; perform semantic parsing on the current water conservancy event information to generate semantically enhanced vectors; The water conservancy event feature vector and semantic enhancement vector are input into the event-three-calculation demand mapping network to obtain the data demand increment, algorithm demand increment and computing power demand increment. Obtain the currently available computing resources and, with the constraints of the incremental data demand, algorithm demand, and computing power demand, obtain candidate combinations of the three computing resources; The coupling fitness function is used to calculate the fitness of the candidate three-computation combination. With the goal of maximizing the overall coupling fitness of data, algorithm and computing power, joint optimization is carried out under the security access condition to obtain the optimal three-computation combination. Based on the severity level of the event and the optimal combination of three calculations, a water conservancy event scheduling scheme is generated, which includes resource orchestration strategy and storage-computation coordination strategy.

[0009] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method.

[0010] Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method.

[0011] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a dynamic integrated scheduling method, device, and medium for water conservancy data-algorithm-computing power event-driven scheduling. By encoding and semantically parsing water conservancy event information, unstructured event descriptions are transformed into computable structured features, providing an accurate data foundation for subsequent resource prediction. Furthermore, by utilizing a demand mapping network from events to the three computing resources, the scale of data, algorithm, and computing power shortages is estimated in advance at the beginning of an event, transforming passive response into proactive prediction. Based on this, incremental demand is used as a hard constraint to filter currently available resources, quickly narrowing down the range of feasible configurations. Then, a coupling fit function is used to perform an overall matching quality assessment on the selected combinations, ensuring that the final selected three computing combinations can operate collaboratively at the execution level, avoiding situations where the algorithm is not ready after data arrival or computing power cannot be effectively invoked after allocation. Finally, considering the urgency of the event, an execution scheme is output, including differentiated resource scheduling order and storage-computing collaboration rules under security constraints. This ensures that the scheduling strategy is both appropriate to the event level and considers the security isolation requirements of water conservancy data and the operability of cross-node computation. This application can avoid the problems of collaborative scheduling failure caused by spatiotemporal mismatch of computing resources, event-driven load mutation and security isolation constraints in water conservancy scenarios, and realize dynamic integrated scheduling. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments 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.

[0013] Figure 1 A flowchart illustrating a water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method provided in an embodiment of this application; Figure 2 A schematic diagram of the logical architecture of a water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of a spatiotemporal coupling descriptor for hydraulic three-calculation provided in an embodiment of this application; Figure 4 A schematic diagram of a large language model architecture in the field of water conservancy provided in an embodiment of this application; Figure 5 A schematic diagram illustrating the execution timing of a storage-compute separation collaborative protocol provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating multi-level computing power selection under a security classification system, provided in one embodiment of this application. Figure 7 A schematic diagram of the three-stage orchestration execution workflow provided for an embodiment of this application; Figure 8 An application environment diagram of a water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] In the process of comprehensive watershed management and digital twin water conservancy construction, water conservancy business systems typically rely simultaneously on multi-source heterogeneous data, various professional models and artificial intelligence algorithms, as well as distributed computing, storage, and network resources. For ease of explanation, the "three calculations" in this invention refer to data, algorithms, and computing power. Specifically, data refers to data resources such as hydrological, meteorological, remote sensing, engineering monitoring, and historical scheduling records; algorithms refer to models or programs such as hydrological forecasting, hydrodynamic simulation, risk assessment, scheduling optimization, and intelligent question answering; and computing power refers to computing infrastructure such as CPUs (Central Processing Units), GPUs (Graphics Processing Units), memory, storage, network bandwidth, and edge / central computing nodes.

[0017] In this application, the spatial attributes of all data, algorithms, and computing power are uniformly expressed using the same spatial reference, and the temporal attributes are uniformly expressed using the same temporal reference. Spatiotemporal registration and geometric correction of multi-source data are performed under this unified spatiotemporal reference to ensure that each component of the spatiotemporal coupling descriptor of the three computing systems is comparable and computable.

[0018] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, a dynamic integrated scheduling method driven by water conservancy data, algorithms, and computing power events is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the dynamic integrated scheduling method includes: Obtain current water conservancy event information, encode the current water conservancy event information to generate a water conservancy event feature vector E; perform semantic parsing on the current water conservancy event information to generate a semantically enhanced vector. .

[0019] By inputting the water conservancy event feature vector and semantic enhancement vector into the event-three-calculation demand mapping network, the incremental data demand is obtained. Algorithm requirement increment and the increase in computing power demand .

[0020] Obtain the currently available computing resources (data, algorithm, and computing power) and, constrained by the incremental requirements of the data, algorithm, and computing power, obtain candidate combinations of the three computing resources. .

[0021] The fitness function is used to calculate the fitness of candidate three-computation combinations. With the goal of maximizing the overall coupling fitness of data, algorithm, and computing power, and under the condition of safe access, joint optimization is performed to obtain the optimal three-computation combination. The specific solution method for the joint optimization is as follows: First, by pre-screening candidates based on data, algorithms, and computing power, the search space is reduced from the full Cartesian product to a set of feasible candidate triples; then, using the coupling fitness degree ρ as the fitness, the feasible candidate triples are solved. When the candidate size is less than a preset size threshold, an exhaustive exact solution is used; when the candidate size is greater than the preset size threshold, heuristic methods such as genetic algorithms or particle swarm optimization are used for approximate solutions. The solution process combines pre-configuration, instantiation, and execution in a three-stage orchestration, thereby achieving a balance between optimization accuracy and real-time performance.

[0022] Based on the event severity level and the optimal combination of three computational methods, a water conservancy event scheduling scheme is generated, which includes resource orchestration strategies and storage-computing coordination strategies. The event severity level is determined by calculating a comprehensive severity index from the current water conservancy event information and matching it with a classification threshold.

[0023] For ease of understanding, such as Figure 3 As shown, a spatiotemporal coupling descriptor for the three computational systems in water conservancy is defined to address the spatiotemporal dependencies among data, algorithms, and computing power in water conservancy operations. .

[0024] ; in, The data is a spatiotemporal description vector. For the algorithm's spatiotemporal requirements vector, For the feature vector of computing power nodes, It is a three-dimensional spatiotemporal coupling function.

[0025] ; in, A polygonal topological description of the spatial coverage area. For time resolution and time window, For data quality metrics, This refers to the data access security level.

[0026] ; in, To define the applicable spatial range of the algorithm, To constrain the algorithm's execution time, The spatiotemporal characteristics of the input data for the algorithm are required. This refers to the characteristics of the computational resources required for the algorithm to run.

[0027] ; in, The node's geographical coordinates This describes the computing capabilities of a node. For network topology connectivity characteristics, This refers to the node's security level.

[0028] ; in, The coupling fit scalar value represents the coupling fit under given data. ,algorithm and computing power The degree of spatiotemporal coordination among the three elements under given conditions. The coupling fit is calculated using the following formula: ; The coupling fitness formula can also be simplified as follows: .

[0029] in, For space adaptation functions, For time adaptation functions, For quality adaptation function, For safety adaptation functions; , , , The corresponding weights must satisfy the non-negativity and normalization constraints: ≥0, ≥0, ≥0, ≥0, + + + =1.

[0030] The spatial adaptation function is represented by both the spatial intersection-union ratio and the geographic distance attenuation.

[0031] Spatial adaptation function formula: ; In the formula, Indicates the coverage area of ​​the data space Applicable spatial range of the algorithm The intersection and union ratio, Indicates the location of computing nodes To the intersection of data and algorithm space Geographical distance from the geometric center This is the spatial attenuation coefficient.

[0032] The time adaptation function is represented by both resolution satisfaction and time window coverage. If The sampling interval is indicated by a smaller sampling interval, which represents a higher time resolution.

[0033] Time adaptation function formula: ; To avoid misjudging low-resolution data as meeting the requirements, the resolution satisfaction score is calculated as follows: ; ; In the formula, Indicates resolution satisfaction. Indicates the coverage of the time window; This indicates the algorithm's requirements for data temporal resolution. Indicates the actual time sampling interval of the data. Indicates the data time window, This represents the time window required by the algorithm. If the system will... If the sampling frequency is defined as the sampling frequency rather than the sampling interval, then a ratio in the opposite direction should be used and explicitly stated in the system configuration.

[0034] The quality fit function is used to measure whether the data quality meets the algorithm input requirements.

[0035] Quality Fit Function Formula: ; In the formula, This represents the minimum quality threshold required for the algorithm input.

[0036] The security adaptation function is used to determine whether the security level of the computing node meets the data access requirements. Data quality metrics. The water resources data quality comprehensive evaluation index (QI) is used. The QI is calculated by weighted scores of five quality dimensions: accuracy, completeness, consistency, timeliness, and usability. That is, the QI is the weighted sum of the scores of each dimension and their weights. The weights of each dimension are determined by a combination of the analytic hierarchy process and the entropy weight method to reduce subjective weighting bias. When the QI is lower than the set threshold, data enhancement processes such as data cleaning, missing data completion, super-resolution reconstruction, and time series enhancement are automatically triggered.

[0037] Formula for the security adaptation function: ; ; ; when hour, ;when hour, ;when hour, Assume the security level is a positive integer; the larger the value, the higher the level. The security gap... for: = .

[0038] Data security level Security level of computing power nodes A four-level positive integer coding system is uniformly adopted. The mapping relationship between security level values ​​and level names is as follows: Level 1 corresponds to Public, Level 2 to Internal, Level 3 to Secret, and Level 4 to Confidential; Enhanced level corresponds to Level 3 and above; the higher the security level value, the more stringent the security protection requirements.

[0039] To prevent security-incompatible scenarios from entering scheduling solely due to high scores in other compatibility criteria, this invention further sets security admission conditions. Only when... Furthermore, this three-component combination only enters the candidate set after the security policy review is approved; when In this case, the combination is directly determined to be an infeasible combination and is not included. Sort.

[0040] The initial weights can be set to , , , This initial value is used for system cold starts and can be updated online based on historical execution feedback according to different flow domains and business scenarios.

[0041] The aforementioned unified spatiotemporal coupling description model for the three elements of data, algorithms, and computing power in water conservancy scenarios enables scheduling decisions to simultaneously consider spatial coverage, time windows, data quality, resource capacity, and security levels. This avoids the problems that arise in existing technologies where data, algorithms, and computing power are managed as independent resources, which can easily lead to issues such as data not being ready, algorithms waiting for input, computing nodes being idle, or inability to execute after resource scheduling.

[0042] As an optional implementation method, current water conservancy event information is obtained, and the current water conservancy event information is encoded to generate a water conservancy event feature vector, specifically including: Acquire hydrological and meteorological forecast data, real-time monitoring data, remote sensing identification data, and historical records of similar events as information on current water conservancy events; The current water conservancy event information is encoded to obtain the water conservancy event feature vector E. .in, Indicates the type of water conservancy event. Indicates the severity level of a water conservancy incident. Indicates the location code of the water conservancy event. Indicates the time when the water conservancy incident occurred. Indicates the duration of the water conservancy event. This indicates the scope of impact of a water conservancy event. Its function is to transform information such as hydrological and meteorological forecasts, real-time monitoring, remote sensing identification, and historical similar events into incremental demands that can be used for resource screening and pre-configuration.

[0043] The definitions, calculation methods, and value ranges of each component of the eigenvector E are as follows.

[0044] (1) Event type coding . It uses one-hot encoding to represent four types of events: rainstorms and floods, droughts, water pollution, and geological disasters.

[0045] ∈{(1,0,0,0),(0,1,0,0),(0,0,1,0),(0,0,0,1)}; As an optional implementation, (1,0,0,0) can be defined to represent a rainstorm and flood event, (0,1,0,0) to represent a drought event, (0,0,1,0) to represent a water pollution event, and (0,0,0,1) to represent a geological disaster event.

[0046] (2) Severity level of the incident . Based on the comprehensive severity index The calculations determined that the values ​​1, 2, and 3 represent the warning level, alert level, and emergency level, respectively.

[0047] ; ; }; in, The threshold for grading is 1, which is the boundary between the warning level and the alert level; This is the grading threshold 2, which marks the boundary between the alert level and the emergency level. , The determination methods include: collecting historical disaster event samples and classifying them into three categories—early warning, alert, and emergency—based on actual losses, emergency response levels, or expert annotations; calculating the losses for each event. The threshold values ​​are determined using K-means clustering, quantiles, or the maximum inter-class variance method to establish the grading boundaries. Finally, adjustments are made based on the basin's flood control standards, early warning issuance rules, and expert review. Different thresholds can be set for different basins, but the source and calibration records of the thresholds should be retained.

[0048] Overall Severity Index Calculated separately based on event type. Because the comprehensive severity index calculation formula and dimensional meaning differ for different event types, the grading threshold... , The thresholds should be set separately according to the event type, that is, rainstorms and floods, droughts, water pollution, and geological disasters should each be assigned independent grading thresholds. The same set of thresholds should not be reused across different event types.

[0049] For events such as torrential rain and floods: ; in, To forecast cumulative rainfall (mm). This is a reference value (mm) for the maximum historical rainfall during the same period in the basin. To predict peak flow (m³) 3 / s), Design flood control flow rate (m) for river channels 3 / s), The current water level (m) Warning water level (m). Weighting for rainfall For traffic weight, As water level weight, .

[0050] For drought-related events: ; in, This represents the normalized term of the standardized precipitation index. This represents the normalized term for precipitation deficit; To standardize the precipitation index weights, Weighting for precipitation deficit.

[0051] Regarding water pollution incidents: ; in, For pollutant concentration, For pollutant standard limits, For the area of ​​the affected waters, Total water area; As the weight of the concentration term, Area weight.

[0052] For geological disaster events: ; in, The surface deformation rate, The deformation threshold, This represents the amount of rainfall in the preceding period. This is the historical rainfall for the same period; For the weight of the transformation term, The weight of the rainfall item.

[0053] Various weights can be determined comprehensively through historical disaster sample regression, analytic hierarchy process (AHP), expert scoring, and sensitivity analysis. Taking rainstorm and flood events as an example, typical weights... , , This can be used as a cold start reference value; in actual deployment, historical flood events from the past 20 years in the basin should be collected to calculate... The correlation between the actual disaster level or economic loss is determined, and the weights and thresholds are calibrated using the minimum error criterion.

[0054] (3) Event location coding . The spatial location used to represent the impact of an event can be represented using any one or a combination of the following methods, based on the system data: latitude and longitude coordinate pairs, watershed-sub-watershed two-level coding, administrative division coding, or polygons representing the scope of impact. Preferably, a combination of watershed-sub-watershed coding and WGS-84 coordinates is used. ; in, For first-level watershed coding, Encoding for secondary sub-basins, The longitude of the event center point (WGS-84 coordinate system). The latitude of the event center point (WGS-84 coordinate system).

[0055] With data space coverage The relationship is as follows: by and Determine the scope of the predicted impact and correlate it with the data resources. Perform spatial intersection or coverage calculations.

[0056] (4) Time of the event . This indicates the time when the predicted event occurs or reaches a threshold of business concern. For rainfall forecast products, the predicted start time of rainfall can be used; for flood forecast products, the peak arrival time minus the forecast period can be used; for pollution or geological disaster events, the predicted time when the monitoring indicator first exceeds the warning threshold can be used. To facilitate model input, the absolute timestamp of the predicted event can be used. and relative lead time They jointly stated: ; ; in, For the current moment, Used to determine the time window for data prefetching, computing power reservation, and model warm-up.

[0057] (5) Duration of the event . This indicates the predicted duration of the event, preferably in hours. For forecast events, the duration can be determined jointly by the duration given in the forecast product and statistical values ​​of similar historical events. ; in, To predict the duration given by the product, The historical average duration of events of the same type and level. Used to determine the time window range for prefetching data and the duration of computing power reservation.

[0058] (6) Event spread rate or diffusion range . This is used to express the speed or proportion at which the impact area of ​​an event changes over time. For rainstorm and flood events, the flood wave propagation speed (in km / h) can be used; for drought events, the rate of expansion of the drought-affected area or the standardized affected area can be used; for water pollution events, the pollutant diffusion speed can be used; and for geological disaster events, the rate of expansion of the deformation impact area can be used. The unified expression is: ; in, This indicates the extent of the event's influence at time t. This represents the event propagation rate at time t. Indicates the central location of the event. This represents a space buffer function that expands outward from the center point.

[0059] Or, when modeling by area ratio, it can be written as: ; in, The area affected is... This represents the total area of ​​the watershed. The value range can be normalized to [0,1], or physical units can be retained according to the event type.

[0060] Furthermore, to enhance predictive capabilities in scenarios involving rare events and complex event chains, semantic parsing is performed on current water conservancy event information to generate semantically enhanced vectors. Specifically, this includes: inputting the original hydrological and meteorological forecast text, monitoring briefings, and historical dispatch record summaries from the current water conservancy event information into a large language model for the water conservancy field to obtain semantically enhanced feature information; and then encoding the semantically enhanced feature information to generate a semantically enhanced vector. The semantically enhanced feature information includes the event chain evolution trend, secondary disaster risk level, historical similar case features, and affected engineering information. It serves as an auxiliary input to the mapping network and does not directly replace numerical features. .

[0061] The large language model for the water resources field can be adaptively fine-tuned using corpora such as hydrological forecast texts, flood control scheduling plans, water conservancy project operation reports, industry standards, and historical emergency response records, based on a general pre-trained large language model. To meet data security requirements, fine-tuning data should be processed primarily within the water resources production network or a controlled private network, and model parameter updates or the external transmission of intermediate representations should comply with the storage-computing collaborative security strategy.

[0062] like Figure 4 As shown, the large language model for the water conservancy field adopts a three-layer architecture: a basic large model layer L0, a water conservancy industry large model layer L1, and a business scenario large model layer L2. The business scenario large model layer generates an interpretable reasoning process for scheduling decisions through retrieval enhancement, constrained by the water conservancy knowledge base, and generates an interpretable explanation of the scheduling results, including the basis for combination selection, the basis for pre-configuration, and the reasons for degradation.

[0063] Furthermore, the feature vectors and semantic enhancement vectors of water conservancy events are input into the event-three-calculation demand mapping network to obtain the incremental data demand. Algorithm requirement increment and the increase in computing power demand .

[0064] The event-three-computation demand mapping network is as follows: ; ; ; ; ; ; in, For incremental data requirements (additional data resources needed due to event triggering, such as additional spatial range, longer time window, additional data types, etc.) For incremental algorithm requirements (additional algorithm instances needed due to event triggering, such as additional forecast models, simulation models, etc.) This refers to the incremental computing power demand (the additional computing resources required due to event triggers, such as additional CPU / GPU, bandwidth, etc.). Mapping functions for data requirements Mapping functions for algorithm requirements This is a mapping function for computing power demand. This is the feature vector of water conservancy events. For semantic enhancement vectors; Map network parameters to the data. Map network parameters for the algorithm, The parameters are used to map computing power to the network parameters. These parameters are obtained through training on historical event records and actual computing usage, with the training objective being to minimize the error between predicted and actual usage. For different... The mapping network can activate branches with different parameters to achieve event type conditional prediction.

[0065] Water Conservancy Event Feature Vector With semantic enhancement vectors The concatenation of the inputs is used as the input, and features are extracted through N fully connected hidden layers (N is a positive integer greater than or equal to 1). The output layer then outputs the features respectively. , , ; Targeting event types Set up independent parameter branches, and the network will be based on... Activate the weight parameters of the corresponding branches to achieve demand prediction based on event type conditions; network parameters , , It is obtained by training by minimizing the mean squared error between the predicted demand from historical events and the actual configuration demand.

[0066] The output structure of the three calculations of demand increment is respectively related to , , Alignment: ; indicates the spatial coverage, time window, minimum quality threshold, and upper limit of security level of the required data.

[0067] ; indicates the applicable space, execution time limit, input requirements, and computational resource requirements of the required algorithm.

[0068] This indicates the required computing power, network bandwidth, maximum acceptable latency, and minimum security level. The incremental computing power requirement is... Algorithm requirement increment Incremental computing power demand Both refer to the resource demand vector that needs to be configured by the scheduling system due to the current water conservancy event. Its value is the net increment of the event-driven predicted total demand minus the baseline resources already reserved by the system. When the system does not set a baseline reservation, the increment degenerates into the event-driven predicted total demand.

[0069] Furthermore, the algorithm resources adopt a decoupled organization method of "general operators + object parameters". General computational operators unrelated to the hydraulic object (such as the one-dimensional hydrodynamic Saint-Venant solver, the Xin'anjiang confluence operator, and the Muskingen confluence operator) are separated and encapsulated from specific engineering object parameters (river cross-section, watershed parameters, and scheduling rules), allowing the same general operator to adapt to different watersheds and engineering objects. Preferably, a Physical Information Neural Network (PINN) is introduced into the strongly nonlinear forecasting stage, embedding the physical equation residuals as training constraints into the neural network to improve the extrapolation capability for extreme scenarios and the generalization ability in data-scarce regions. This generalization mechanism significantly reduces the migration and reuse costs on the algorithm side, thereby expanding the selectable scale and adaptation speed of the three-calculation combination in the "algorithm" dimension.

[0070] Obtain currently available computing resources (data resources, algorithm instances, and computing power nodes) and, constrained by the incremental requirements of data demand, algorithm demand, and computing power demand, obtain candidate combinations of the three computing resources. Specifically, this includes: upon receiving a business request, representing available data resources, algorithm instances, and computing power nodes as sets. , , .according to , , Pre-screening will be performed, with the following specific constraints: Data candidate subset ≠ Only retain computing resources whose spatial range intersects with the event prediction requirement space, whose data quality is not lower than the minimum requirement threshold, and whose data security level is not higher than the upper limit of the requirement.

[0071] Algorithm candidate subset M Only retain algorithm resources that fully cover the event prediction requirements and whose computational power requirements are met.

[0072] Candidate subset of computing power Only computing nodes with computing capacity, network bandwidth, and node security level that are not lower than the lower limit of event prediction requirements will be retained.

[0073] The above three candidate subsets constitute the candidate three-calculation combination set. }.in, Candidate data resources that satisfy data constraints, Candidate algorithm instances that satisfy the algorithm constraints, Candidate computing nodes that meet the computing power constraints. Any candidate triplet. This represents a feasible three-calculation combination scheme. For simplicity, Figure 2 The set of candidate combinations is represented as Ω, and any candidate triple is represented as... The two have the same meaning.

[0074] Then, for candidate triples Calculate coupling fitness The optimal combination is determined under the constraints of security access conditions, total computing power, and task deadline.

[0075] Among them, the optimal three-calculation combination That is, among all the qualified data, algorithms, and computing power that are freely combined to form triples, find the resource combination with the largest coupling fit ρ, and use it as the scheduling scheme for this water conservancy business task.

[0076] The security access conditions are: This signifies the complete shielding of incompatible resource combinations to prevent the leakage of classified water conservancy production data across security domains, representing a mandatory security threshold for the water conservancy industry; the total computing power constraint is: This means that the sum of all computing resources used in this task cannot exceed the total remaining computing power of the cluster, to prevent resource over-provisioning and crashes due to multi-task contention; the task deadline constraint is: This indicates that the total computation time of the selected combination cannot exceed the business time limit, ensuring the real-time nature of flood control and emergency dispatch; combinations that exceed the time limit will be directly excluded.

[0077] Based on this, a water conservancy event scheduling scheme is generated, which includes resource orchestration strategy and storage-computation coordination strategy, according to the severity level of the event and the optimal combination of the three calculations.

[0078] The resource orchestration strategy of the water conservancy event dispatch scheme is as follows: based on the severity level of the event. and lead time This triggers a differentiated pre-configuration strategy.

[0079] When the severity level of the event is at the warning level ( The system triggers data prefetching, filters relevant data from the available data set based on the incremental data demand, and prefetches necessary data from the collection storage area to the high-speed cache area.

[0080] When the severity level of the incident is at the alert level ( The system triggers computing power reservation and algorithm preheating based on data prefetching, filters computing power nodes that meet the conditions according to the incremental computing power demand, and loads relevant algorithm instances according to the incremental algorithm demand.

[0081] When the severity level of the incident is emergency ( Under the premise of data pre-fetching, computing power reservation and algorithm warm-up, the system triggers preemptive scheduling, suspends or downgrades low-priority non-event tasks, and allocates the released resources to event-related tasks first.

[0082] Furthermore, for scenarios with multiple concurrent events, the system calculates a comprehensive priority based on the event severity, scope of impact, business deadline, and security level, and uses a priority queue for resource allocation. When a resource becomes abnormal, it is removed from the available set, and the coupling adaptability is recalculated among the remaining resources to select the suboptimal combination for continued execution.

[0083] For missing data or outliers, the system adopts the following processing rules: If a key input is missing, it will be supplemented using the most recent valid value, interpolation from nearby stations, remote sensing substitute data, or statistical values ​​of similar historical events, and the data quality score will be reduced. If the monitored value significantly exceeds the physical reasonable range, an anomaly detection will be triggered and manual verification will be required.

[0084] The storage-computing collaboration strategy of the water conservancy event scheduling scheme is as follows: deploy edge computing agent nodes within the water conservancy production network, and use the edge computing agent nodes to perform local preprocessing, feature extraction and sensitive field desensitization on the original water conservancy data to generate compressed intermediate results; The compressed intermediate results are transmitted to a remote computing node for large-scale computation, and the intermediate computation results returned by the remote computing node are received. Edge computing agent nodes integrate the intermediate calculation results with local private data to generate the final calculation result.

[0085] Specifically, such as Figure 5 As shown, to address the contradiction that water conservancy production data must not leave the closed network while large-scale computing requires remote centralized computing power support, an edge-remote collaborative storage-computing separation protocol is designed.

[0086] Edge computing proxy computing (T1-T3). Edge computing proxy nodes are deployed within the water conservancy production network. The edge nodes perform cleaning, spatiotemporal alignment, feature extraction, and sensitive field desensitization on the raw water conservancy data locally, and only transmit the compressed intermediate computing results to the remote computing nodes. The raw water conservancy data does not leave the production network.

[0087] ; ; ; in, For the currently transmitted data, For the previous data transmission, For differential data, The sparsity threshold, To compress data packets, This is an approximation of the value recovered from the remote end. This indicates that the difference results are sparsified.

[0088] Computational tasks are deployed separately (T4-T7). Computational tasks are divided into edge and remote stages based on data dependencies and security levels. Computations involving raw production data, private project runtime data, and sensitive scheduling records are completed at the edge; numerical simulations, deep learning inference, or model training requiring large-scale computing power are completed remotely. After the remote computation results are returned to the edge, the edge nodes combine them with local private data to complete the final result integration.

[0089] Dynamic latency compensation. Edge nodes monitor round-trip latency between themselves and remote computing nodes in real time. and according to Adjust the synchronization frequency and transmission batch.

[0090] when When decreasing, that is The system employs high-frequency, small-batch synchronization.

[0091] when When it rises, that is The system reduces the synchronization frequency and increases the batch size.

[0092] when Continuously exceeding the threshold At that time, that is The system automatically switches to edge-independent computing mode, using pre-deployed lightweight models or simplified hydrological models to complete tasks, sacrificing some accuracy in exchange for business continuity and real-time performance. and The preset threshold can be configured according to the network environment and the sensitivity of the service to latency.

[0093] Furthermore, the remote computing nodes are preferably selected from schedulable computing nodes in the national integrated computing power network, including the "East Data West Computing" national hub node and socialized computing power accessed through computing power grid connection. During scheduling, the system selects the best option from its own edge computing power, industry-specific network computing power, and national computing power network public computing power based on the security level and latency requirements of the task. Figure 6 As shown: Tasks with an internal security level or higher are limited to completion on a dedicated network or edge nodes. Training and simulation tasks with lower security levels and high computing power requirements can be offloaded to national computing power network nodes. When national computing power network nodes are unreachable or latency exceeds limits, the system reverts to an independent edge computing mode. Therefore, this application does not require water resources departments to build large-scale intelligent computing clusters themselves, but rather dynamically integrates and calls upon external computing power based on the principle of "security classification + latency constraints," which aligns with the development trend of a unified national computing power network.

[0094] After obtaining the water conservancy event scheduling plan, the process also includes: executing the water conservancy event scheduling plan and obtaining actual execution indicators; the actual execution indicators include normalized time efficiency, resource utilization rate and result accuracy.

[0095] The actual scheduling quality score is calculated based on the actual execution indicators. With the optimization objective of minimizing the difference between the actual scheduling quality score and the coupling fitness, the parameters of the event-three-calculation demand mapping network and the weight coefficients in the coupling fitness function are updated using the gradient descent algorithm.

[0096] The actual scheduling quality score ; .in, To normalize time efficiency, For resource utilization, Score the accuracy of the results; This is a weighting coefficient for time efficiency. This is a weighting coefficient for resource utilization. This is the weighting coefficient for the accuracy of the result.

[0097] Among them, normalized time efficiency Determined based on the timestamp in the task execution log. This is the normalized completion time. If the task is completed within the deadline, then: If the task times out, i.e. ,but .

[0098] To minimize the actual scheduling quality score Coupling fit The differences between them are used as the optimization objective to update the parameters of the event-three-calculation requirement mapping network. , , and the weight coefficients in the coupling fit function , , , .

[0099] The loss function is defined as: .

[0100] Online weight updates employ mini-batch gradient descent: ← ; .

[0101] in, For the i-th weight coefficient ( , , or ), For learning rate, Let be the partial derivative of the loss function with respect to the i-th weight, and ← indicates the assignment update operation. The predicted coupling fit of the k-th sample. The actual scheduling quality score for the k-th sample. The value of the i-th adaptation function ( , , or (After the update) Apply nonnegativity constraints and renormalize to make .

[0102] The system collects the completion time, resource utilization, result accuracy, number of anomalies, number of degradations, and manual review feedback for each orchestration session, and calculates the actual scheduling quality score. and will Store the data in the feedback sample set. Periodically update the weight coefficients using the feedback sample set. , , , and mapping network parameters , , This enables scheduling strategies to gradually adapt to different river basins, seasons, and business objectives.

[0103] When a resource fails during execution, the system removes the faulty resource from the available set and recalculates the resource from the remaining resources. It selects the suboptimal combination for execution, forming fault tolerance and dynamic degradation capabilities. At the same time, the large language model in the water conservancy field can generate interpretable explanations for the scheduling results, including the reasons for selecting the three-calculation combination, the basis for pre-configuration, the reasons for degradation, and suggestions for manual review.

[0104] In addition, the scheduling scheme of this application also includes three-stage orchestration execution, such as Figure 7 As shown, the pre-configuration phase completes data pre-fetching, computing power reservation, and algorithm preheating before the event occurs; the instantiation phase establishes data channels, loads algorithm instances, and allocates computing resources when business requests arrive; the execution phase constructs a Directed Acyclic Graph (DAG) according to task dependencies and executes the task flow.

[0105] This application also provides an application scenario in which the above-mentioned water conservancy data-algorithm-computing power event-driven dynamic integrated scheduling method is applied.

[0106] Taking the flood control scenario in the Jushui-Zhanghe River basin of Yichang City as an example, the spatiotemporal description vector of the rainfall forecast data is as follows: ; in, This represents the polygonal boundary of the Juzhang River basin. For a 1-hour time resolution and a 72-hour time window, It is 0.92. This is an internal-level security designation. The spatiotemporal requirements vector for the distributed hydrological model algorithm is: ; in, The area encompasses the Juzhang River basin. The results must be generated within 30 minutes. The input rainfall data must have a time resolution of at least 1 hour. At least an 8-core CPU and 32GB of RAM are required. The characteristic vector of a computing power node in a certain water conservancy data center is: ; in, It features 8 GPUs, a 64-core CPU, and 512GB of RAM. With 100Gbps bandwidth and 8ms latency, Enhanced security level.

[0107] like , , ,but: ; ; ; If actually implemented , , ,and , , ,but: ; ; ; Therefore, we can know the current situation. If the predicted value is too high, the system will reduce the weight of the corresponding dimension or adjust the prediction model parameters in subsequent online learning.

[0108] Taking heavy rain and flood warnings as an example, the estimated cumulative rainfall in the Jushui-Zhanghe River basin over the next 6 hours is... Forecast peak flow 3 / s, current water level ; Known , 3 / s, ,Pick , , ,but: .

[0109] set up , ,but The severity level of the event is alert. The complete event feature vector can be written as: ; in, It is a type of rainstorm flood. Alert level, Indicates the watershed and its center coordinates. For the next 2 hours, It lasts for 12 hours. The speed is 25 km / h. Mapping network output. , , Afterwards, the system completed data pre-fetching, computing power reservation, and model preheating in advance.

[0110] In another exemplary embodiment of this application, the hydrological monitoring data of a certain water conservancy project over the past five years is stored within the production network, with a security level of internal. Local edge nodes have limited computing power, while remote computing centers possess large-scale GPU resources. The system performs data cleaning, feature extraction, and differential compression at the edge, only storing the compressed intermediate results δ. t The data is transmitted to the remote end. After completing large-scale computations at the remote end, the intermediate computation results are returned to the edge, where they are then combined with local private engineering data to generate the final result. Throughout the entire process, the raw data does not leave the production network.

[0111] In another exemplary embodiment of this application, such as Figure 7 As shown, in the flood control "four-prevention" service, the pre-configuration stage pre-fetches digital terrain data, historical rainstorm cases, and real-time rainfall and water level data into the high-speed cache based on weather forecasts, and reserves GPU and CPU resources; the instantiation stage selects after the actual rainstorm is triggered. The highest three calculations are combined and a data channel is established; during the execution phase, tasks are carried out according to the DAG (Directed Acyclic Graph) process, which includes data preparation, hydrological forecasting, flood evolution, dispatching scheme generation, and risk assessment.

[0112] If the local CPU node fails during execution, the system will remove the failed node from the local CPU node. Remove from the list and recalculate candidate combinations from the remaining nodes. If the value is not specified, the suboptimal solution is selected and the process continues, and the reasons for the downgrade, the time taken for reorganization, and the changes in the accuracy of the results are recorded.

[0113] In one exemplary embodiment, such as Figure 8 As shown, the water conservancy data-algorithm-computing power event-driven dynamic integrated scheduling method provided in this application can be applied to, for example... Figure 8 The system architecture shown is as follows. This architecture includes a water resources sensing layer, a water resources production network, edge computing agent nodes, water resources business systems, and a cloud computing power scheduling and management platform. The water resources sensing layer collects raw data through hydrological and meteorological stations, radar, remote sensing satellites, and other equipment, and transmits it to edge computing agent nodes via the water resources production network for local preprocessing and compression. The cloud computing power scheduling and management platform includes core modules such as computing power resource management, algorithm management, event management, and scheduling strategy management. It is used to execute the dynamic fusion scheduling method described in this application and connects externally to the National Computing Power Network, the East-West Data Hub node, and socialized computing power resources to achieve multi-level optimal scheduling of computing power under security classification constraints.

[0114] To verify the engineering effectiveness of the hierarchical pre-scheduling, spatiotemporal coupling integration, and three-computation collaborative mechanism of this application in the intelligent scheduling scenario of flood control in the basin, this application conducts multi-dimensional backtracking verification by combining typical flood events in the Yangtze River Basin, control ablation experiments, and digital twin flood control large-scale model engineering examples.

[0115] A retrospective analysis was conducted on several flood events in the Yangtze River basin in 2020. The maximum inflow peak flow into the Three Gorges Reservoir was 75,000 m³ / h. 3 / s (Design flood flow 83700 m³ / s) 3The measured water level at Yichang Station was 53.08m (warning level 52.0m), and the average rainfall in the area was approximately 280mm (historical average for the same period is approximately 150mm). After setting the weights based on river flooding as the primary factor, the calculated SI (Severity Index) was approximately 0.83, exceeding the emergency threshold of 0.7. This correctly triggered the emergency pre-configuration (data pre-fetching, computing power reservation, algorithm warm-up, and preemptive scheduling), consistent with the actual emergency response. Multiple backtests showed that when SI > 0.5, approximately 85% of events corresponded to actual Level IV or higher emergency responses, and when SI > 0.7, approximately 90% corresponded to Level III or higher, verifying the effectiveness of the event severity index-based pre-configuration triggering.

[0116] Using the 2020 flood recap as a baseline, the baseline was traditional static allocation (fixed data source, fixed model, and reserved computing power based on peak values), while the experimental group used the spatiotemporal coupling dynamic adaptation proposed in this application. Based on backtesting of engineering operation data, the scheme generation latency decreased from 50 minutes to 5 minutes (↓90%), computing power utilization increased from 32% to 78%, computing power cost (normalized) decreased from 1.00 to 0.58, the critical task latency compliance rate increased from 68% to 95%, and the model's first-time deployment rate increased from 48% to 92%. Without changing any individual technologies, simply replacing "static allocation" with "spatiotemporal coupling joint optimization" yielded significant synergistic increments, demonstrating that the integration mechanism of this application has an independent and quantifiable technical contribution.

[0117] This paper uses a large-scale flood control model (digital twin water conservancy + large-scale water conservancy model) of a certain watershed as a carrier to illustrate the path of realizing the integration of computing, computation, and analysis. On the data side, the large model needs to call data from multiple sources such as rainfall and water conditions, engineering operation, and scheduling procedures to drive the intelligent creation of multi-source data (remote sensing interpretation, spatiotemporal completion, and quality evaluation) to form high-quality data. On the algorithm side, multiple candidate scheduling schemes (millisecond level) are first generated using a knowledge graph response library, and then a one-dimensional hydrodynamic model is called to refine the verification of key schemes, reflecting the dual drive of "mechanism + large model". On the computing power side, the computing power requirements of large model inference and hydrodynamic verification are significantly different. This application dynamically schedules the data between edge inference nodes and remote (national computing power network) training / deduction nodes according to the event level. This embodiment shows that the large model amplifies the demand for data and computing power, the intelligent creation of multi-source data reflects the value of the algorithm, and the three are dynamically integrated through the joint optimization of coupling and adaptation degree in this application.

[0118] This application has at least the following beneficial effects: (1) A unified spatiotemporal coupling description model of the three elements of data, algorithm and computing power in water conservancy scenario was established, so that scheduling decisions can simultaneously consider spatial coverage, time window, data quality, resource capacity and security level.

[0119] (2) It realizes the prediction of water conservancy event-driven three-calculation demand and hierarchical pre-configuration, which can complete data pre-fetching, model warm-up and computing power reservation before the event occurs, reducing the response delay of critical business.

[0120] (3) An edge-remote storage and computing collaboration mechanism for water conservancy data security isolation is proposed, which utilizes remote computing power to complete complex calculations without leaving the production network with the original data.

[0121] (4) A closed-loop mechanism of execution, feedback, optimization and re-execution has been formed, enabling the scheduling weight and demand forecasting model to be continuously adjusted based on historical execution results.

[0122] (5) Use the large language model in the water conservancy field to enhance the semantics of events and interpret scheduling decisions, so that the identification and scheduling process of complex event chains is easier for water conservancy business personnel to understand, review and trace.

[0123] (6) By combining joint optimization with security access hard constraints with the goal of maximizing the overall coupling and adaptability of the three computing systems, the limitations of the existing technology of independent scheduling of data, algorithms and computing power in different dimensions and only performing weighted soft sorting are overcome. Under the premise of ensuring the security isolation of water conservancy data, the global collaborative optimization of the three computing resources is achieved.

[0124] (7) Connect remote computing nodes to the national integrated computing network so that water conservancy business can dynamically access and call national public computing power according to security level, avoiding the high cost of water conservancy departments building large-scale intelligent computing clusters, and conforming to the development trend of the national computing power network.

[0125] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data, algorithms, computing resource descriptors, historical samples of water conservancy events, scheduling weight parameters, watershed engineering benchmark parameters, task execution feedback samples, and copies of parameters from large-scale language models in the water conservancy field. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements all steps of the water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method.

[0126] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0128] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0129] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0131] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0132] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A dynamic integrated scheduling method driven by water conservancy data, algorithm, and computing power events, characterized in that, The dynamic fusion scheduling method includes: Obtain current water conservancy event information, encode the current water conservancy event information to generate water conservancy event feature vectors; perform semantic parsing on the current water conservancy event information to generate semantically enhanced vectors; The water conservancy event feature vector and semantic enhancement vector are input into the event-three-calculation demand mapping network to obtain the data demand increment, algorithm demand increment and computing power demand increment. Obtain the currently available computing resources and, with the constraints of the incremental data demand, algorithm demand, and computing power demand, obtain candidate combinations of the three computing resources; The coupling fitness function is used to calculate the fitness of the candidate three-computation combination. With the goal of maximizing the overall coupling fitness of data, algorithm and computing power, the optimal three-computation combination is obtained by joint optimization under the condition of safe access. Based on the severity level of the event and the optimal combination of three calculations, a water conservancy event scheduling scheme is generated, which includes resource orchestration strategy and storage-computation coordination strategy.

2. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, Obtain current water conservancy event information, encode the current water conservancy event information to generate water conservancy event feature vectors, specifically including: Acquire hydrological and meteorological forecast data, real-time monitoring data, remote sensing identification data, and historical records of similar events as information on current water conservancy events; The current water conservancy event information is encoded to obtain the water conservancy event feature vector E. ;in, Indicates the type of water conservancy event. Indicates the severity level of a water conservancy incident. Indicates the location code of the water conservancy event. Indicates the time when the water conservancy incident occurred. Indicates the duration of the water conservancy event. This indicates the scope of impact of a water conservancy incident.

3. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, Semantic parsing is performed on current water conservancy event information to generate semantically enhanced vectors, specifically including: The hydrological and meteorological forecast text, monitoring briefings, and historical dispatch record summaries from the current water conservancy event information are input into a large language model in the water conservancy field to obtain semantically enhanced feature information. The semantically enhanced feature information includes the event chain evolution trend, secondary disaster risk level, historical similar case features, and affected project information. The semantic enhancement feature information is encoded to generate a semantic enhancement vector.

4. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, The event-three-computation demand mapping network is as follows: ; ; ; in, To increase the demand for data, For incremental algorithm requirements, This represents an increase in computing power demand; Mapping functions for data requirements Mapping functions for algorithm requirements This is a mapping function for computing power demand. This is the feature vector of water conservancy events. For semantic enhancement vectors; , , These are the parameters for the corresponding mapping network.

5. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, The coupling fitness function is used to calculate the coupling fitness ρ of the candidate three-component combination. The formula for calculating the coupling fitness ρ is as follows: ； In the formula, For space adaptation functions, For time adaptation functions, For quality adaptation function, For safety adaptation functions; , , , For the corresponding weights, and satisfying ≥0, ≥0, ≥0, ≥0, + + + =1; For the data space coverage area, To define the applicable spatial range of the algorithm, The geographic coordinates of the computing node; For time resolution and time window, The spatiotemporal characteristics of the input data for the algorithm are required. Constraints on algorithm execution time; This is a data quality metric. Security level of computing nodes Data access security level; Spatial adaptation function formula: ; In the formula, Indicates the coverage area of ​​the data space Applicable spatial range of the algorithm The intersection and union ratio, Indicates the location of computing nodes To the intersection of data and algorithm space Geographical distance from the geometric center This is the spatial attenuation coefficient; Time adaptation function formula: ; ； ； In the formula, Indicates resolution satisfaction. Indicates the coverage of the time window; This indicates the algorithm's requirements for data temporal resolution. Indicates the actual time sampling interval of the data. Indicates the data time window, Indicates the time window required by the algorithm; Quality Fit Function Formula: ; In the formula, This represents the minimum quality threshold required for the algorithm input; Formula for the security adaptation function: ; ; ; = ； In the formula, This indicates a security gap.

6. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, The resource orchestration strategy of the water conservancy event scheduling scheme is as follows: When the severity level of an event is warning level, the system triggers data prefetching, filters relevant data from the available data set according to the incremental data demand, and prefetches necessary data from the collection storage area to the cache area. When the severity level of an event is alert, the system triggers computing power reservation and algorithm preheating based on data prefetching, filters computing power nodes that meet the conditions according to the incremental computing power demand, and loads relevant algorithm instances according to the incremental algorithm demand. When the severity level of an event is emergency, the system, under the premise of data prefetching, computing power reservation, and algorithm warm-up, triggers preemptive scheduling, suspends or downgrades low-priority non-event tasks, and allocates the released resources to event-related tasks first.

7. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, The storage-computing collaboration strategy of the water conservancy event scheduling scheme is to deploy edge computing agent nodes within the water conservancy production network, and use the edge computing agent nodes to perform local preprocessing, feature extraction and sensitive field desensitization on the original water conservancy data to generate compressed intermediate results; The compressed intermediate results are transmitted to a remote computing node for large-scale computation, and the intermediate computation results returned by the remote computing node are received. Edge computing agent nodes integrate the intermediate calculation results with local private data to generate the final calculation result.

8. The water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method according to claim 1, characterized in that, After obtaining the water conservancy event dispatch plan, it also includes: The water conservancy event scheduling plan is executed to obtain actual performance indicators; the actual performance indicators include normalized time efficiency, resource utilization rate, and result accuracy. The actual scheduling quality score is calculated based on the actual execution indicators. With the optimization objective of minimizing the difference between the actual scheduling quality score and the coupling fitness, the parameters of the event-three-calculation demand mapping network and the weight coefficients in the coupling fitness function are updated using the gradient descent algorithm.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the water conservancy data-algorithm-computing power event-driven dynamic fusion scheduling method as described in any one of claims 1-8.

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