Remote centralized control data scheduling method and system

Abstract

The invention relates to hydropower dispatching operation management, in particular to a remote centralized control data dispatching method and system, and the method comprises the steps: obtaining multi-source heterogeneous data of source network integration dispatching; the multi-source heterogeneous data is preprocessed; integrating the preprocessed multi-source heterogeneous data, and constructing a digital data model so as to perform synchronous mapping on states between source networks; constructing a source network integration scheduling process model based on the digital data model; determining an optimal execution path of a scheduling process through the source network fusion scheduling process model; verifying the user permission; and when the user has the calling authority of the scheduling process, carrying out source network integration scheduling according to the optimal execution path. Through automatic scheduling, the scheduling response time is remarkably shortened, and the scheduling efficiency and the scheduling reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of hydropower dispatching and operation management technology, and in particular to a remote centralized control data dispatching method and system. Background Technology

[0002] Remote centralized control and data scheduling is a technical approach that uses a centralized scheduling system to achieve real-time monitoring and scheduling control of distributed or remote equipment. It typically utilizes advanced information and communication technologies, combined with data acquisition, processing, and transmission technologies, allowing dispatchers to obtain real-time operational data from various devices through a remote system and make scheduling decisions based on this data. Its core lies in the centralized management and control of multi-point data through a unified system, ensuring the efficient and safe operation of complex systems such as power, transportation, and industry.

[0003] Remote centralized control and dispatching methods are widely used in power grids, power equipment, energy management and other fields. Through real-time data transmission and analysis, they realize dispatching functions such as equipment status monitoring, load adjustment and fault diagnosis, thereby improving the reliability, response speed and intelligence level of the system.

[0004] However, existing scheduling methods face the challenge of effectively integrating heterogeneous data from different sources during data collection. Furthermore, the source-side and grid-side models have long been separated, maintained separately by the power plant control system and the scheduling system, respectively, lacking a unified logical view. This makes it difficult for scheduling processes to coordinate across source and grid systems. Existing scheduling workflows largely rely on dispatchers' experience and manual operations for cross-system collaboration, increasing the human burden and making processes prone to deviations or security risks due to data synchronization delays or omissions. Additionally, the lack of centralized authentication and unified authorization mechanisms during identity verification poses risks of unauthorized access or data leakage. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a remote centralized control data scheduling method and system: A first aspect of the present invention provides a remote centralized control data scheduling method, comprising: S1: Obtain multi-source heterogeneous data for source-network converged scheduling; S2: Preprocess the multi-source heterogeneous data; S3: Integrate preprocessed multi-source heterogeneous data to construct a digital data model for synchronous mapping of the state between source networks; S4: Based on the aforementioned digital data model, construct a source-network convergence scheduling process model; S5: Determine the optimal execution path of the scheduling process through the source-network convergence scheduling process model; S6: Verify user permissions; S7: When the user has the authority to access the scheduling process, source-network fusion scheduling is performed according to the optimal execution path.

[0006] A second aspect of the present invention provides a remote centralized control data scheduling system, comprising: a processor and a memory; The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the remote centralized control data scheduling method as described in the first aspect.

[0007] The beneficial effects of this invention are reflected in the following aspects: By preprocessing multi-source heterogeneous data, the integrity and continuity of the data are ensured, reducing scheduling decision errors caused by inconsistent data quality and improving data availability and accuracy. By integrating the preprocessed multi-source heterogeneous data, the source-network linkage is reflected, making the state synchronization mapping between the source and network clearer and more operable. By constructing a source-network fusion scheduling process model, scheduling tasks can be standardized and modularized, realizing bidirectional linkage between the device layer and the scheduling process layer, improving the automation and intelligence of the scheduling system, reducing human intervention, and improving task processing efficiency. By automatically selecting the optimal path to execute scheduling tasks, the time consumption and resource waste of the execution path are reduced, thereby improving scheduling efficiency and response speed. By dynamically verifying user permissions, it is ensured that each user can only access and operate resources within their authorized scope, effectively preventing unauthorized access and potential security risks. Every operation is performed within the authorized scope, ensuring system security, preventing unauthorized operations and data leakage, and improving system compliance, reliability, and auditability. By scheduling the source and network according to the optimal path, the system can automatically and accurately execute scheduling tasks, avoiding human intervention. Attached Figure Description

[0008] Figure 1 A flowchart illustrating a remote centralized control data scheduling method provided by the present invention; Figure 2 A schematic diagram illustrating the process of constructing a digital data model provided by the present invention; Figure 3 This is a schematic diagram of a heterogeneous graph construction process provided by the present invention; Figure 4 A schematic diagram of a dynamic state mapping process provided by the present invention; Figure 5 This is a schematic diagram of the structure of a remote centralized control data scheduling system provided by the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] Example 1: Reference manual attached Figure 1 The diagram shows a flowchart of a remote centralized control data scheduling method provided by the present invention.

[0011] This invention provides a remote centralized control data scheduling method, comprising: S1: Obtain multi-source heterogeneous data for source-network convergence scheduling.

[0012] In one possible implementation, the multi-source heterogeneous data specifically includes: hydropower station output, equipment operating status, telemetry and teleindication data, dispatching instructions, power grid topology changes, meteorological and environmental parameters, and historical operating records.

[0013] In this invention, due to the different construction histories of the source-side system and the network-side system, the large number of equipment manufacturers, the complexity of data formats, and the inconsistency of communication protocols, there has been a long-term "semantic break" and "system boundary" between the source and network, resulting in a typical data silo problem. To overcome the structural bottleneck, when acquiring multi-source heterogeneous data, a design architecture that supports multiple protocols, cross-formats, and asynchronous high-frequency acquisition mechanisms is adopted. This architecture can adapt to scheduling systems such as the IEC 61970 CIM / XML model standard, and is also compatible with WebService interface format, JMS message stream, TXT / EXCEL file import, and OSB service bus, enabling parallel data access and real-time synchronization from multiple systems such as the source-side control system, network-side SCADA, weather station, water dispatch system, and protection device.

[0014] S1 specifically refers to: acquiring multi-source heterogeneous data from multiple heterogeneous data source systems through a data flow control mechanism based on a weighted dynamic scheduling window.

[0015] Weighted dynamic scheduling windows are a data flow control mechanism that dynamically allocates weights to data source access to control the inflow of data from multiple sources. The dynamic adjustment of weights is based on factors such as the current traffic of the data source and system load, thereby ensuring data flow balance and system stability. Data flow control mechanisms are used to manage and control the speed and frequency of data flowing into the system from multiple source systems, preventing system overload and ensuring efficient and stable data processing.

[0016] The heterogeneous data source system specifically includes: source-side control system, grid-side SCADA, weather station, water control system, and protection device.

[0017] In one possible implementation, the data flow control mechanism based on weighted dynamic scheduling windows is specifically as follows: ; in, w i ( t ) indicates the first i A heterogeneous data source system t Access scheduling weight at any time D i ( t )express i A heterogeneous data source system t Data stream intensity at any given time R max This represents the target control rate for the total reception rate of multi-source heterogeneous data. i =1,2,…, N , N This indicates the total number of heterogeneous data source systems.

[0018] ; in, w i ( t+ 1) indicates the first i A heterogeneous data source system t+ Access scheduling weight at time 1 α Indicates the scheduling sensitivity coefficient. B i ( t ) indicates the first i The current buffer occupancy of each heterogeneous data source system in the system cache. B ref This indicates the reference buffer threshold.

[0019] Specifically, the data flow control mechanism based on weighted dynamic scheduling windows can dynamically compress the bandwidth usage of corresponding data sources when data volume surges, preventing local backlog from spreading to the global system and ensuring continuous and stable system operation. This mechanism effectively manages data inflows from multiple heterogeneous data sources, adjusts bandwidth allocation across different data sources, ensures that high-traffic sources do not overload the system, and provides sufficient data processing capacity for low-traffic sources. This improves the stability of data acquisition and the system's responsiveness, optimizes resource utilization, and reduces data processing latency, thereby providing real-time and accurate support for scheduling decisions.

[0020] S2: Preprocess multi-source heterogeneous data.

[0021] It should be noted that by filling in missing values, standardizing data formats, and removing noise, the quality and consistency of the data are improved, which can effectively ensure the accuracy and reliability of subsequent models and decisions, providing high-quality real-time data support for source network scheduling. At the same time, preprocessing can also accelerate data processing speed, reduce computational delays caused by data problems, and improve the overall efficiency of the system.

[0022] In one possible implementation, S2 specifically includes: When there are missing values ​​in multi-source heterogeneous data, the multi-source heterogeneous data is interpolated using a weighted least squares fitting function.

[0023] The weighted least squares fitting function is as follows: ; ; Where min represents minimization. β 0 indicates the intercept term. β 1 represents the slope coefficient. ω j Indicates the distance weighting factor. y j Indicates the first j Each sample point value, t j Indicates the first j The time corresponding to each sampling point value t k Indicates the first k The time corresponding to each reference sampling point λ The parameter represents the decay parameter, and exp represents the exponential function. m This represents the half width of the window.

[0024] β 0 represents the intercept term, which in the fitted model represents the theoretical predicted value of the dependent variable when the independent variable is zero. β 1 represents the slope coefficient, which is the change in the dependent variable for every unit increase in the independent variable, reflecting the degree of response of the sampling point to time or other variables.

[0025] In this invention, the data acquisition process also faces common quality problems such as inconsistent timestamps, missing fields, and chaotic measurement point coding. To address these issues, this invention introduces a data verification and preprocessing mechanism driven by metadata. All data must be compared with a predefined unified field dictionary. The system performs semantic mapping and encoding conversion on the measurement points and performs data supplementation or calculation when necessary.

[0026] To enhance the traceability of data throughout its entire lifecycle, a unique data signature fingerprint is calculated for each data record. A hash function h(x) is applied to the key field set x = {field name, value, timestamp, source ID} to form a data digest f = h(x). All data digests are bound to the original records for storage, enabling rapid traceability and consistency verification in scenarios such as data retrieval, log auditing, and error backtracking.

[0027] Reference manual attached Figure 2 The diagram illustrates a process diagram for constructing a digital data model provided by the present invention.

[0028] Reference manual attached Figure 3 The diagram illustrates a process flow diagram for constructing a heterogeneous graph provided by the present invention.

[0029] Reference manual attached Figure 4 The diagram illustrates a flow chart of a dynamic state mapping method provided by the present invention.

[0030] S3: Integrate preprocessed multi-source heterogeneous data to construct a digital data model for synchronous mapping of the state between source networks.

[0031] It should be noted that by integrating preprocessed multi-source heterogeneous data and constructing a digital data model, data from different sources and formats can be unified into a structured and computable model. This not only improves data consistency and availability but also effectively supports the synchronous mapping of the state between the source and the grid, enabling the mutual influence between source-side equipment and the grid state to be accurately reflected. Through this integration, the dispatching system can process and utilize data more efficiently, thereby making more accurate and real-time dispatching decisions.

[0032] In one possible implementation, S3 specifically includes: S301: The preprocessed multi-source heterogeneous data is sequentially processed into objects, relations, and semantic tags to obtain a data object model.

[0033] In this invention, the collected data is processed by objectification, relationalization, and semantic tagging. First, all structured data needs to be classified into several subject domains, such as "hydropower station equipment model", "real-time operating status", "scheduling event chain", "environmental parameter field", and "power grid topology relationship". The data within each subject domain has a relatively consistent logical structure and business meaning, and a unified object ID is used as the main index.

[0034] S302: Employ a two-level abstraction mechanism to determine the static information and dynamic behavioral characteristics of the data object model.

[0035] Specifically, by determining the static information and dynamic behavioral characteristics of the data object model, the fixed parameters (such as device ID, type, location, etc.) and changing behavioral characteristics (such as operating status, power output, etc.) of the equipment can be expressed and managed more clearly. This hierarchical structure makes the model more flexible and easier to expand, and can better capture the interaction between the equipment and the power grid, thereby improving the system's responsiveness to state changes and the accuracy of scheduling decisions.

[0036] In this invention, a two-level abstraction mechanism is adopted, namely the physical object layer and the logical feature layer. The former defines static information such as device ID, power station, category, and connection relationship, while the latter maps the dynamic behavior characteristics of the device, such as on / off status, active and reactive power output, and operating mode.

[0037] S303: Construct heterogeneous graphs based on static information and dynamic behavioral characteristics using graph structure modeling.

[0038] Specifically, the graph structure modeling method can intuitively and efficiently represent the complex relationship between source-side devices and grid-side power grid. Nodes represent devices and topology nodes, and edges represent their interactions and connections. The graph structure can clearly show the dependencies between various elements in the system and supports flexible state updates and path derivation.

[0039] In one possible implementation, S303 specifically includes: S3031: Based on static information and dynamic behavior characteristics, define a set of nodes and a set of edges. Specifically, the set of edges represents the scheduling logical connection relationship between the set of source-side devices and the set of network-side topology nodes.

[0040] S3032: Construct a heterogeneous graph based on the set of nodes and the set of edges.

[0041] The node set specifically consists of: ; in, V Represents a set of nodes. V s Indicates the set of source-side devices. V n Represents the set of network-side topology nodes. Indicates the intersection symbol.

[0042] The heterogeneous graph is specifically as follows: ; in, G Represents a heterogeneous graph. E Represents the set of edges.

[0043] S304: Perform dynamic state mapping on heterogeneous graphs to construct a digital data model.

[0044] Specifically, by mapping the nodes and edges in the heterogeneous graph to the real-time device status, the mutual influence between the source-side devices and the grid-side power grid can be reflected in real time. This enables timely and accurate synchronization of the source and grid status, supports efficient scheduling decisions and anomaly detection, and enhances the real-time performance and intelligence level of the system.

[0045] In one possible implementation, S304 specifically includes: S3041: Based on the heterogeneous graph, define the source-side device state vector and the network-side device state vector: ; in, x s ( t )express t The source-side device state vector at time t. T This indicates the transpose operation. x p ( t ) indicates the first p Individual source side devices t The state at any given moment, p =1,2,…, n , n This indicates the total number of source-side devices.

[0046] ; in, x n ( t )express t The network-side device state vector at time t, x q ( t ) indicates the first q Individual source-side devices t The state at any given moment, q = n +1, n +2,…, n+m , m This indicates the total number of network-side devices.

[0047] S3042: Establish the state transfer matrix between the source-side device state vector and the network-side device state vector: ; in, A Represents the state transfer matrix. ε ( t )express t The perturbation term at any given moment.

[0048] S3043: Estimate the state transfer matrix using the least squares method based on the historical state sequence. ; ; in, k =1,2,…, K , K This represents the total number of historical data. This represents the estimated state transfer matrix. Indicates historical samples.

[0049] Historical state sequences refer to state data collected over a period of time for both source-side equipment and the grid. This data records the operational status of the equipment and changes in the grid, typically stored in time series format. Least squares is a mathematical method commonly used in regression analysis, aiming to estimate model parameters by minimizing the sum of squared errors between predicted and observed values. The state transfer matrix is ​​a mathematical matrix describing how state changes in source-side equipment affect the state of the grid.

[0050] It should be noted that by estimating the state transfer matrix using the least squares method, the dynamic relationship between the source-side equipment and the grid-side power grid can be effectively learned from historical data. The least squares method optimizes the parameters by minimizing the error, so that the state transfer matrix can accurately reflect the influence between the two, thus improving the accuracy of the model.

[0051] S3043: Based on the estimated state transfer matrix, construct a digital data model to synchronize the states between source networks.

[0052] Specifically, by utilizing the estimated state transfer matrix, the dynamic relationship between source-side equipment and grid-side power grid can be accurately described, enabling real-time and accurate synchronous mapping of state changes between the source and grid, thereby optimizing the efficiency of scheduling decisions and power grid operation.

[0053] In this invention, based on the business characteristics of source-grid integrated scheduling operation, complex, chaotic, and heterogeneous data content is uniformly modeled to construct a digital data model with semantic consistency, structural clarity, and real-time scalability. This model needs to accurately express the physical structure, operating status, and scheduling capabilities of the source-side hydropower equipment, and also needs to be compatible with the grid-side topology, real-time power flow of the power grid, and scheduling plan requirements, thereby providing a unified data foundation for subsequent process orchestration, control linkage, and scheduling optimization.

[0054] In terms of model semantic management, a unified metadata description system has been established. All model objects are associated with attributes such as name, code, data source, update timestamp, and business-related tags. This supports filtering, indexing, and retrieval across multiple dimensions (such as equipment type, region, power plant affiliation, and operating status). Furthermore, a model version management mechanism ensures that existing processes are not affected when the model structure is adjusted or business requirements change. Each model structure adjustment generates an independent version number and change record, supporting process backtracking and model difference comparison.

[0055] Once completed, the data model is output in a unified structured format, organized by subject domain, indexed by entity ID, and labeled by time tag. It continuously interacts with the data access module to ensure that new data is automatically mapped into the corresponding model objects. Ultimately, it continuously provides stable and semantically clear scheduling foundation data to the process management module, supporting the unified processing of subsequent complex scheduling logic, operation ticket generation, alarm linkage, and log recording. Through the operation of this module, source network fusion data is systematically transformed into computable, traceable, and serviceable logical entities.

[0056] S4: Based on the digital data model, construct a source-network converged scheduling process model.

[0057] It should be noted that by using a digital data model to uniformly model the status, device behavior, and scheduling tasks of the source network system, the standardization and automation of various scheduling tasks between the source and network can be achieved. This provides comprehensive data support for the scheduling process, making scheduling decisions more accurate and efficient, and reducing human intervention. In addition, the construction of the scheduling process model can flexibly respond to different scheduling scenarios, improving the system's adaptability and optimization capabilities.

[0058] In this invention, each scheduling task is abstracted as a process instance. The process consists of a series of logical nodes, each representing a standard step in the scheduling process, such as plan creation, technical review, on-site confirmation, operation execution, and result feedback. These nodes are defined in a graph structure by the process modeler during process definition. F =( N,L ) indicates that among them N This represents the set of process nodes, where L⊆N×N represents the directed connections between nodes, i.e., the process path. Each node... n i ∈N corresponds to a task type, triggering condition, and operator role.

[0059] S5: Determine the optimal execution path of the scheduling process through the source-network convergence scheduling process model.

[0060] It's important to note that optimizing path selection ensures that scheduled tasks are executed in the most efficient way. By calculating the execution cost of each path, the optimal execution path is automatically selected, avoiding manual intervention and inefficient operations, thereby improving scheduling efficiency and saving time and resources. Furthermore, path optimization reduces system load, ensures the rational allocation of resources, and ultimately improves system response speed and overall operational efficiency.

[0061] In one possible implementation, S5 specifically includes: S501: Defines node triggering conditions for all process nodes in the source-network convergence scheduling process model: ; in, φ i ( x ( t )) indicates the first i Trigger functions for each process node, I ( ) represents a function, x 1( t ) represents a state item. x 2( t ) indicates the planning status. It represents the logical AND.

[0062] The value of the trigger function determines whether the current node is triggered, that is, whether the start conditions are met.

[0063] Specifically, defining the trigger conditions for each process node ensures that scheduling tasks start automatically when specific conditions are met, avoiding human intervention and improving the automation and accuracy of the scheduling process. By defining clear trigger conditions, it is possible to respond to changes in the status of equipment and the power grid in real time, ensuring that the scheduling process is executed efficiently and flexibly, improving the response speed of the scheduling system, reducing latency, and enhancing the system's intelligent processing capabilities.

[0064] In this invention, when When, it indicates a node If the execution conditions are met, the system automatically enters the running state. Taking a maintenance process as an example, if the equipment status is "running" and the scheduling plan is identified as "pending maintenance," then the equipment status sub-item in the status vector... Planning status .

[0065] S502: If the node triggering conditions are met, the optimal execution path is determined through the path cost function.

[0066] The path cost function is specifically as follows: ; in,C k Representing a path k The optimal execution path on the [path]. g =1,2,…, M k , M k Representing a path k The total number of nodes on γ g,m Representing a path k Upper g The weight coefficients of each node, T k,g Representing a path k Upper g The estimated execution time for each node.

[0067] In this invention, to ensure full traceability and compliance supervision of process behavior, during the process scheduling process, history data is recorded after each process node is executed. This data includes operator ID, execution time, input status snapshot, output status result, operation type, approval comments, etc., and is archived using the process instance ID as the primary key. The process history data constitutes a structured log record, which, in conjunction with the log standardization mechanism, constructs an operational closed loop and responsibility chain for the entire source-network scheduling process. All process execution actions are embedded with permission judgment logic. Based on the dynamic role authorization table provided after permission allocation, node-level access verification is performed to ensure that scheduling behavior is executed within the process definition and permission framework.

[0068] S6: Verify user permissions.

[0069] It should be noted that verifying user permissions ensures that each user can only access and operate resources within their authorized scope, effectively preventing unauthorized access and potential security risks. Precise permission verification ensures system operational compliance and data security. Role-based and attribute-based access control mechanisms ensure that user operations comply with legal, regulatory, and task requirements, thereby improving system security, stability, and auditability.

[0070] In one possible implementation, S6 specifically includes: S601: Authenticate the user's identity and generate an identity code and session token.

[0071] Specifically, the session token is: ; in, τ h user h The token represents the session token. Sign express, Kpriv This represents the private key. UIDh Indicates the first h A user's identification code. t login Represents the login timestamp, hash(profile) h ) represents user h A summary of roles and organizational information.

[0072] S602: Determine the extended access control functions based on the role access control model and the attribute access control model.

[0073] The extended access control functions are as follows: ; Permit() represents the access control judgment function. u This represents the user entity that initiated the access request. o Indicates the object being accessed. a Indicates an access action. f This represents the strategy function, and env represents the set of environment attributes for the access request.

[0074] o The object being accessed, i.e. the access target, can be a controllable entity within the system, such as a file, resource, or service. a This indicates the access action, such as read, write, execute, delete, etc.

[0075] In this invention, identity authentication adopts a strategy architecture that combines role-based access control (RBAC) and attribute-based access control (ABAC). Let the system user set be... The character set is The resource collection is The operation set is In the RBAC model, the mapping relationship between users and roles is first established. Then define the mapping relationship between roles and resource-operation pairs. When the user Assigned to a role ,and Authorized to perform operations Regarding resources The operation is allowed to proceed. Since scheduling operations are often constrained by context, such as time range, scheduling process status, event triggering conditions, and geographical region affiliation, the RBAC model struggles to cover these dynamic limitations. Therefore, the module introduces the ABAC mechanism to determine extended access control functions.

[0076] S603: Based on the identity code and session token, permissions are assigned and user permissions are determined by extending access control functions.

[0077] In this invention, to adapt to the complex collaborative needs of scheduling tasks across departments, systems, and regions, a process-driven dynamic permission generation mechanism is designed. Whenever the process management module generates a process instance... At that time, an access control table is automatically generated based on the process node definition and participant roles. Let the process node be... The corresponding resource operation permissions are Construct dynamic authorization relationships: ; in, Represents the set of authorized users for the current node. These represent the set of resources and the set of operations involved in the node, respectively. This refers to the time window during which the permissions for this node take effect. t start Indicates the start time of the time window. t end This indicates the end time of the time window. This mechanism ensures that permissions are "created on demand and expire automatically," achieving highly flexible control under the principle of least privilege.

[0078] To enhance the system's security and auditability, audit logs are simultaneously recorded when authentication operations occur. The log data structure includes the operation initiator, operation type, target resource, operation time, process, state context, and access authorization chain, and is stored in the security audit database. By constructing a behavior hash chain and signature verification, the integrity of the behavior logs can be verified, preventing tampering and repudiation. Furthermore, to address risks such as unauthorized access, abnormal operations, and batch calls, an access behavior discrimination mechanism based on anomaly detection is introduced, which identifies each user's recent activity. The behavior of each operation is vectorized into a feature sequence. The system uses Mahalanobis distance or the isolated forest algorithm to identify anomalies, and the identification results are fed back to the security management strategy layer to restrict access or tighten behavior auditing.

[0079] S7: When a user has the authority to retrieve the scheduling process, source-network convergence scheduling is performed according to the optimal execution path.

[0080] Reference manual attached Figure 5 The diagram shows a schematic of the structure of a remote centralized control data scheduling system provided by the present invention.

[0081] This invention provides a remote centralized control data scheduling system 20, including: a processor 201 and a memory 202; The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the remote centralized control data scheduling method described above and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0082] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0083] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0084] In the description of the embodiments of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "center," "top," "bottom," "top," "bottom," "inner," "outer," "inner side," and "outer side," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. "Inner side" refers to the interior or enclosed area or space. "Outer perimeter" refers to the area surrounding a specific component or specific area.

[0085] In the description of embodiments of the present invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0086] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "joining," and "assembly" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0087] In the description of embodiments of the present invention, specific features, structures, materials or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0088] In the description of the embodiments of the present invention, it should be understood that "-" and "~" represent a range of two numerical values, and this range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0089] In the description of embodiments of the present invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote centralized control data scheduling method, characterized in that, include: S1: Obtain multi-source heterogeneous data for source-network converged scheduling; S2: Preprocess the multi-source heterogeneous data; S3: Integrate preprocessed multi-source heterogeneous data to construct a digital data model for synchronous mapping of the state between source networks; S4: Based on the aforementioned digital data model, construct a source-network convergence scheduling process model; S5: Determine the optimal execution path of the scheduling process through the source-network convergence scheduling process model; S6: Verify user permissions; S7: When the user has the authority to access the scheduling process, source-network fusion scheduling is performed according to the optimal execution path.

2. The remote centralized control data scheduling method according to claim 1, characterized in that, The multi-source heterogeneous data specifically includes: hydropower station output, equipment operating status, telemetry and teleindication data, dispatch instructions, power grid topology changes, meteorological and environmental parameters, and historical operating records; S1 specifically refers to: obtaining the multi-source heterogeneous data from multiple heterogeneous data source systems through a data flow control mechanism based on a weighted dynamic scheduling window; The heterogeneous data source system specifically includes: a source-side control system, a network-side SCADA system, a weather station, a water control system, and protection devices.

3. The remote centralized control data scheduling method according to claim 1, characterized in that, The data flow control mechanism based on weighted dynamic scheduling windows is specifically as follows: ； in, w i ( t ) indicates the first i A heterogeneous data source system t Access scheduling weight at any time D i ( t )express i A heterogeneous data source system t Data stream intensity at any given time R max This represents the target control rate for the total reception rate of multi-source heterogeneous data. i =1,2,…, N , N This represents the total number of heterogeneous data source systems; ； in, w i ( t+ 1) indicates the first i A heterogeneous data source system t+ Access scheduling weight at time 1 α Indicates the scheduling sensitivity coefficient. B i ( t ) indicates the first i The current buffer occupancy of each heterogeneous data source system in the system cache. B ref This indicates the reference buffer threshold.

4. The remote centralized control data scheduling method according to claim 1, characterized in that, S2 specifically includes: When the multi-source heterogeneous data has missing values, the multi-source heterogeneous data is interpolated using the weighted least squares fitting function. The weighted least squares fitting function is specifically: ； ； Where min represents minimization. β 0 indicates the intercept term. β 1 represents the slope coefficient. ω j Indicates the first j Distance weighting factor for each sampling point, y j Indicates the first j Each sample point value, t j Indicates the first j The time corresponding to each sampling point value t k Indicates the first k The time corresponding to each reference sampling point λ The parameter represents the decay parameter, and exp represents the exponential function. m This represents the half width of the window.

5. The remote centralized control data scheduling method according to claim 1, characterized in that, S3 specifically includes: S301: The preprocessed multi-source heterogeneous data is sequentially processed into objects, relations, and semantic tags to obtain a data object model; S302: A two-level abstraction mechanism is adopted to determine the static information and dynamic behavior characteristics of the data object model; S303: Based on the static information and the dynamic behavior characteristics, construct a heterogeneous graph using a graph structure modeling method; S304: Perform dynamic state mapping on the heterogeneous graph to construct the digital data model.

6. The remote centralized control data scheduling method according to claim 5, characterized in that, Specifically, S303 includes: S3031: Based on the static information and the dynamic behavior characteristics, define a node set and an edge set, wherein the edge set is specifically the scheduling logical connection relationship between the source-side device set and the network-side topology node set; S3032: Construct the heterogeneous graph based on the set of nodes and the set of edges.

7. The remote centralized control data scheduling method according to claim 5, characterized in that, Specifically, S304 includes: S3041: Based on the heterogeneous graph, define the source-side device state vector and the network-side device state vector: ； in, x s ( t )express t The source-side device state vector at time t. T This indicates the transpose operation. x p ( t ) indicates the first p Individual source-side devices t The state at any given moment, p =1,2,…, n , n Indicates the total number of source-side devices; ； in, x n ( t )express t The network-side device state vector at time t, x q ( t ) indicates the first q Individual source-side devices t The state at any given moment, q = n +1, n +2,…, n+m , m Indicates the total number of network-side devices; S3042: Establish the state transfer matrix between the source-side device state vector and the network-side device state vector: ； in, A Represents the state transfer matrix. ε ( t )express t The perturbation term at any given time; S3043: Based on the historical state sequence, estimate the state transfer matrix using the least squares method: ； ； in, k =1,2,…, K , K This represents the total number of historical data. This represents the estimated state transfer matrix. Indicates historical samples; S3043: Based on the estimated state transfer matrix, construct the digital data model to synchronize the state between the source networks.

8. The remote centralized control data scheduling method according to claim 1, characterized in that, S5 specifically includes: S501: Define node triggering conditions for all process nodes in the source-network convergence scheduling process model: ； in, φ i ( x ( t )) indicates the first i Trigger functions for each process node, I ( ) represents a function, x 1( t ) represents a state item. x 2( t ) indicates the planning status. This represents the logical AND operator; S502: If the node triggering condition is met, determine the optimal execution path using the path cost function: ； in, C k Representing a path k The optimal execution path on the [path]. g =1,2,…, M k , M k Representing a path k The total number of nodes on γ g,m Representing a path k Upper g The weight coefficients of each node, T k,g Representing a path k Upper g The estimated execution time for each node.

9. The remote centralized control data scheduling method according to claim 1, characterized in that, S6 specifically includes: S601: Authenticate the user's identity and generate an identity code and session token. S602: Determine the extended access control functions based on the role-based access control model and the attribute-based access control model; S603: Based on the identity identifier and the session token, the user's permissions are determined by the extended access control function; Specifically, the session token is: ； in, τ h user h The token represents the session token. Sign express, Kpriv This represents the private key. UID h Indicates the first h A user's identification code. t login Represents the login timestamp, hash(profile) h ) represents user h Summary of roles and organizational information; The extended access control function is specifically: ； Permit() represents the access control judgment function. u This represents the user entity that initiated the access request. o This refers to the object being accessed, i.e., the target of the access. a Indicates an access action. f This represents the strategy function, and env represents the set of environment attributes for the access request.

10. A remote centralized control data scheduling system, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the remote centralized control data scheduling method as described in any one of claims 1 to 9.

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