A programmable data processing method and system for multi-level users

Abstract

This application discloses a programmable data processing method and system for multi-level users, involving power plant scheduling and data processing technologies. The method includes: constructing a tree diagram based on collected multi-source data; configuring the displayable node range for users at each user level; and dividing the displayable node range into the smallest data elements according to the tree diagram; setting default display methods and associated operations for any data element based on the semantic tag set for users with different permissions; monitoring changes in user permissions or node status to trigger corresponding decision scenarios, thereby instantiating a view within the decision scenario. The VDF is a set of data element query and assembly rules to achieve view instantiation after VDF execution. By introducing a semantic tag set, decision scenarios, and dynamic VDF execution, the method solves the problems of event flow situation awareness and the balance between permission security and user experience in existing technologies.

Description

Technical Field

[0001] This application relates to the fields of power plant dispatching and data processing technology, and in particular to a programmable data processing method and system for multi-level users. Background Technology

[0002] With the development of sensor technology, network communication technology, and computer technology, modern industrial control systems, especially the water dispatch automation systems of hydropower stations, can now achieve real-time acquisition and centralized monitoring of various information such as hydrology, water conditions, unit status, and gate opening. These systems typically collect massive amounts of real-time data and provide information support to users at different levels, including operation staff, professional management, and decision-making and scheduling, through human-machine interfaces (HMIs), reports, and trend charts. However, in practical applications, existing data processing and display methods still have the following technical shortcomings: Existing systems typically organize data by data source or equipment type (e.g., water level gauges, rain gauges, generator units), forming modular display interfaces (e.g., "Real-time Data Overview" and "Historical Report Query"). This organization ignores the semantic relationships of data within specific business processes. For example, "dam water level" data is a critical boundary condition in flood control scenarios, an input for calculating water head in power generation scenarios, and a basis for determining sensor malfunction in equipment maintenance scenarios. When users at different levels (e.g., dispatchers focusing on water level trends, operators focusing on real-time values, and administrators focusing on daily characteristic values) encounter the same set of functional modules, they often need to manually filter, compare, and piece together information from multiple interfaces to form the complete view required for decision-making. This not only leads to "information overload" at the operational level (the interface is filled with a large amount of irrelevant data) but also to "information deficiency" at the decision-making level (key related information is scattered in various places and difficult to obtain quickly).

[0003] Existing systems often use static, predefined interfaces, with their content, layout, and interactive relationships fixed during the design and development phase. However, industrial systems operate dynamically, and users' decision-making tasks evolve accordingly. For example, a system might transition from "routine operation monitoring" to "flood warning" due to a rapid rise in water levels, potentially triggering "gate control" operations. In these different decision-making scenarios, the data dimensions, level of detail, and types of information required by users vary significantly. Static interfaces cannot automatically adjust their focus and depth based on the evolving system situation, causing users to remain on a generic view incompatible with their current decision-making task at critical moments, delaying optimal action. Summary of the Invention

[0004] This application provides a programmable data processing method and system for multi-level users, which solves many shortcomings of existing technologies in terms of event flow situation awareness and the balance between permission security and user experience, and meets the needs of modern water management systems for data quality, decision-making efficiency and user experience.

[0005] This application proposes a programmable data processing method for multi-level users, including: A tree diagram is constructed based on the collected multi-source data. The constructed tree diagram includes a hierarchical structure with several nodes, and each level of nodes is configured with a corresponding user level. The multi-source data includes hydrological, water condition, unit, and gate data. Configure the range of nodes that can be displayed for users at each user level, and divide the range of nodes that can be displayed into the smallest data elements according to the tree diagram. Each data element includes a value, a dynamic confidence level, and a semantic tag set, wherein the semantic tag set includes a multi-dimensional computable structured description. For any data element, based on the semantic tag set, set the default display method and associated operations for users with different permissions; Monitor changes in user permissions or node status to trigger corresponding decision scenarios, and instantiate views in the decision scenarios. Each decision scenario is configured with an executable view description file (VDF) based on the tree diagram. The VDF is a set of data element query and assembly rules to achieve view instantiation after executing the VDF.

[0006] This application also proposes a programmable data processing system for multi-level users, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned programmable data processing method for multi-level users.

[0007] This application's embodiments address numerous shortcomings in existing technologies regarding event flow situation awareness and the balance between permission security and user experience by introducing semantic tag sets, decision-making scenarios, and VDF dynamic execution, thereby meeting the needs of modern water dispatching systems for data quality, decision-making efficiency, and user experience.

[0008] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0009] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a basic flowchart illustrating the programmable data processing method for multi-level users in this embodiment. Detailed Implementation

[0010] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0011] This application proposes a programmable data processing method for multi-level users, such as... Figure 1 As shown, it includes the following steps: In step S101, a tree diagram is constructed based on the collected multi-source data. The constructed tree diagram includes a hierarchical structure with several nodes, and each level of nodes is configured with a corresponding user level. The multi-source data includes at least hydrological data, hydrological data, unit data, and gate data. Hydrological data and hydrological data can be combined with data from observation stations, meteorological stations, etc., and unit data can include data from key unit equipment.

[0012] In step S102, the range of displayable nodes is configured for users at each user level, and the range of displayable nodes is divided into the smallest data elements according to the tree diagram. Each data element includes a value, a dynamic confidence level, and a semantic tag set. The semantic tag set includes a multi-dimensional computable structured description. For example, for a dam-back water level data element, the corresponding multi-dimensional computable structured description may include: range: primary range "hydrology", secondary range "water conditions"; space: "dam-back", "mainstream"; time: "real-time"; display method: "numerical", "red-orange-yellow"; decision scenario: "flood control scheduling", "power generation plan", "unit operation optimization"; data quality: "confidence level", etc. That is, the multi-dimensional computable (arrangeable) structured description is used to label and describe the corresponding data elements in multiple dimensions.

[0013] In step S103, for any data element, based on the semantic tag set, a default display method and associated operations are set for users with different permissions. In some embodiments, the display scope of data elements can be set for users with different permissions, and data elements within the permissions can be opened for users to perform operations within the permissions.

[0014] In step S104, changes in user permissions or node status are monitored to trigger corresponding decision scenarios, in which view instantiation is performed. An executable view description file (VDF) is configured for each decision scenario based on the tree diagram. The VDF is a set of data element query and assembly rules to achieve view instantiation after executing the VDF. In a specific example, the programmable data processing method of this application provides the same set of view interfaces, but with different instantiation results, for different user permissions and in conjunction with the hydrological environment. By executing the view description file (VDF), the requirements of modern water management systems for data quality, decision-making efficiency, and user experience are met.

[0015] In some embodiments, constructing a tree diagram based on collected multi-source data includes: First, the collected multi-source heterogeneous data is cleaned, verified, and formatted to transform it into indivisible business data elements. Then, based on the station-level physical structure, management logic, and business affiliation strength corresponding to the multi-source data, a multi-branch tree diagram is constructed. The multi-branch tree diagram includes: The root node describes the entire station-level water dispatching operation; Intermediate layer nodes describe the business domains or physical areas involved in scheduling. Each intermediate layer node contains data processing rules for the corresponding business domain or physical area, such as "hydrological information" and "power generation operation". Each intermediate layer node also contains rules for data aggregation for that subdomain (business domain or physical area).

[0016] Leaf nodes correspond to data elements. The data quality in a data element can be determined based on the relationship between nodes and the type of sensor. For example, if a node itself uses a high-precision sensor, it is given a high confidence level. If it belongs to manually entered data, external interface data, or jointly calculated data, the confidence level of the data element can be jointly calculated based on the nearest high-confidence data element.

[0017] The membership strength α is a membership strength coefficient defined for each parent-child node. The membership strength coefficient is determined by calculating the cosine similarity between the semantic label of the child node and the semantic vector of the parent node. Data elements are attached to the parent node with the highest membership strength α, and their association relationships with other parent nodes (and their corresponding α) are retained as orchestratable data elements for subsequent orchestration.

[0018] In some embodiments, configuring the range of nodes that can be displayed for users at each user level includes: Establish a mapping relationship between user hierarchy and tree diagram nodes, and define the basic range of displayable nodes for each user role; Based on the tree diagram, the range of displayable nodes is divided into the smallest data elements, and a multi-dimensional, computable, structured semantic tag set is constructed for each data element. The constructed semantic tag set includes: Attribute tags, business domain tags, and user view tags are used. Attribute tags describe the basic attributes of data elements, business domain tags define the degree of correlation between data elements in different business scenarios, and user view tags configure corresponding display methods for different user roles. For example, the aforementioned range and spatial tags are business domain tags, and the display method tags are user view tags. Attribute tags include numerical values ​​and confidence levels.

[0019] In some embodiments, monitoring changes in user permissions or node status to trigger a corresponding decision scenario, and instantiating the view in the decision scenario, includes: When any decision scenario is triggered, the influence range of key nodes under that decision scenario is determined based on the collected multi-source data and the business membership strength of the multi-branch tree diagram. The key node is the node most relevant to the decision scenario. Key nodes are determined based on the collected multi-source data. For example, in some cases, there may be a sudden change in data deviation at a certain node or type of node, exceeding the normal fluctuation range, such as a tributary's water level remaining consistently high. In such cases, the corresponding decision scenario is determined based on the business membership strength of that node in the multi-branch tree diagram, and that node is designated as the key node. Related downstream nodes are then identified to determine the influence range of the key node.

[0020] Retrieve data elements that belong to the scope of influence and have a confidence level greater than a specified threshold.

[0021] Based on the retrieved data elements, the corresponding VDF is executed to dynamically update nodes within the user's permissions. This application configures the corresponding VDF for the scenario and determines the view update under the current user permissions by finding the intersection between the queried data elements. This allows for continuous updates of the nodes that best match the decision scenario and user permissions within the user's permissions, providing an intuitive view presentation for the user.

[0022] In some embodiments, monitoring changes in user permissions or node status to trigger corresponding decision scenarios, and instantiating the view in the decision scenario, further includes: In any decision-making scenario, an event stream is constructed based on the view instantiated and dynamically changing semantic tag set. In some specific examples, the method of this embodiment further constructs an independent event stream for the user based on the current user permissions and historical operation data. The event stream includes changes in data elements, user operations, etc.

[0023] The event stream is aggregated within a specified time window to determine the data trend within the scope of influence. In any decision-making scenario, the business affiliation strength can be further calculated based on the aggregated data trend to determine whether a rearrangement of parent-child relationship data element nodes is necessary.

[0024] If a scenario state transition is triggered based on the data trend, the corresponding VDF is re-executed. In some examples, after re-orchestration, a branch scenario of any of the decision scenarios may be formed. For example, if the number of nodes after re-orchestration exceeds a specified threshold, or if there is a change in a key node, a scenario state transition is determined, and the branch VDF is repeatedly executed based on the intersection of the retrieved data elements and the scenario VDF, thereby further improving orchestration and enhancing the auxiliary function.

[0025] In some embodiments, when a scenario state transition is triggered based on the data trend, re-executing the corresponding VDF includes: Pattern matching is performed solely based on the data trends to determine suitable migration rules based on the matching results; In any decision scenario, the VDF branch corresponding to the migration rule is executed to maintain the decision scenario and migrate to the appropriate branch state. Different VDF branches are pre-configured for the same decision scenario, and information is passed between different VDF branches via shared key-value pairs. Each VDF branch represents different data element display states within the user's basic displayable node range in any decision scenario. In a specific example, different VDF branches can be formed based on the rearrangement results of the aforementioned example. In a specific example, based on the instantiated dynamic view, the local event flow similarity with each branch is calculated, and event flow tracking is performed. If the current arrangement result is dissimilar to all historical branches, a new branch is formed.

[0026] In some embodiments, in any decision scenario, executing the VDF branch corresponding to the migration rule to maintain the decision scenario and migrate to the adapted branch state includes: When any of the decision scenarios is activated for the first time, a view session is created based on the executed VDF to cover the entire lifecycle of the decision scenario. That is, in any decision scenario, the execution of the VDF creates a view session to cover the entire lifecycle of the decision scenario, and the VDF branch is used to make dynamic adjustments within the view session.

[0027] For any migrated VDF branch, a shared key is maintained for the migrated view session to record the interaction state and branch evolution history.

[0028] Under the view session, the results are merged and instantiated based on the semantic tag set of data elements according to the migrated VDF branch. That is, the migrated VDF branch is a local adjustment of the view session under the current decision scenario.

[0029] In some embodiments, when monitoring changes in user permissions, the method further includes: Identify the scope of changes to user permissions and mark the data elements within the scope of the changes. For data elements that need to be hidden, immediately perform the hiding operation in the view session. For example, the permission filter can be used to immediately perform the forced hiding operation on the currently rendered view without waiting for the view engine to fully redraw.

[0030] After executing the corresponding VDF, the newly added data elements are instantiated. In this way, the view can be adjusted and refreshed from the user's perspective, thereby improving event flow situation awareness, permission security, and user experience.

[0031] This application also proposes a programmable data processing system for multi-level users, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned programmable data processing method for multi-level users.

[0032] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on this disclosure that have equivalent elements, modifications, omissions, combinations (e.g., schemes involving intersections of various embodiments), adaptations, or changes. They are not limited to the examples described in this specification or during the implementation of this application, and such examples are to be construed as non-exclusive.

[0033] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. Other embodiments can be used by those skilled in the art when reading the above description.

[0034] The above embodiments are merely exemplary embodiments of this disclosure. Those skilled in the art can make various modifications or equivalent substitutions to this invention within the scope of the disclosure, and such modifications or equivalent substitutions should also be considered to fall within the protection scope of this invention.

Claims

1. A programmable data processing method for multi-level users, characterized in that, include: A tree diagram is constructed based on the collected multi-source data. The constructed tree diagram includes a hierarchical structure with several nodes, and each level of nodes is configured with a corresponding user level. The multi-source data includes hydrological, water condition, unit, and gate data. Configure the range of nodes that can be displayed for users at each user level, and divide the range of nodes that can be displayed into the smallest data elements according to the tree diagram. Each data element includes a value, a dynamic confidence level, and a semantic tag set, wherein the semantic tag set includes a multi-dimensional computable structured description. For any data element, based on the semantic tag set, set the default display method and associated operations for users with different permissions; Monitor changes in user permissions or node status to trigger corresponding decision scenarios, and instantiate views in the decision scenarios. Each decision scenario is configured with an executable view description file (VDF) based on the tree diagram. The VDF is a set of data element query and assembly rules to achieve view instantiation after executing the VDF.

2. The programmable data processing method for multi-level users as described in claim 1, characterized in that, Building a tree diagram based on collected multi-source data includes: Based on the station-level physical structure, management logic, and business affiliation strength corresponding to multi-source data, a multi-branch tree diagram is constructed, which includes: The root node describes the entire station-level water dispatching operation; Intermediate layer nodes describe the business domains or physical regions involved in scheduling. Each intermediate layer node contains data processing rules for the corresponding business domain or physical region. Leaf nodes correspond to data elements; The business membership strength is a membership strength coefficient defined for each parent-child node. The membership strength coefficient is determined by calculating the cosine similarity between the semantic label of the child node and the semantic vector of the parent node.

3. The programmable data processing method for multi-level users as described in claim 2, characterized in that, Configure the range of nodes that can be displayed for users at each user level, including: Establish a mapping relationship between user hierarchy and tree diagram nodes, and define the basic range of displayable nodes for each user role; Based on the tree diagram, the range of displayable nodes is divided into the smallest data elements, and a multi-dimensional, computable, structured semantic tag set is constructed for each data element. The constructed semantic tag set includes: The data includes attribute tags, business domain tags, and user view tags. The attribute tags describe the basic attributes of the data elements, the business domain tags define the degree of association of the data elements in different business scenarios, and the user view tags configure the corresponding display methods for different user roles.

4. The programmable data processing method for multi-level users as described in claim 3, characterized in that, Monitoring changes in user permissions or node status to trigger corresponding decision scenarios, and instantiating the view within those decision scenarios, includes: When any decision scenario is triggered, the influence range of the key node under any decision scenario is determined based on the collected multi-source data and the business membership strength of the multi-branch tree diagram. The key node is the node most relevant to the decision scenario. Retrieve data elements that belong to the influence range and have a confidence level greater than a specified threshold; Based on the retrieved data elements, execute the corresponding VDF to perform dynamic node updates with the user's permissions.

5. The programmable data processing method for multi-level users as described in claim 4, characterized in that, Monitoring changes in user permissions or node status to trigger corresponding decision-making scenarios, and instantiating views within those scenarios, also includes: In any decision-making scenario, an event flow is constructed based on the view instantiated and dynamically changing set of semantic tags; The event stream is aggregated within a specified time window to determine the data trend within the scope of influence. If the scenario state transition is triggered based on the data trend, the corresponding VDF will be re-executed.

6. The programmable data processing method for multi-level users as described in claim 5, characterized in that, If the scenario state transition is triggered based on the data trend, the corresponding VDF will be re-executed, including: Pattern matching is performed solely based on the data trends to determine suitable migration rules based on the matching results; In any decision scenario, the VDF branch corresponding to the migration rule is executed to maintain the decision scenario and migrate to the appropriate branch state. Different VDF branches are pre-configured for the same decision scenario. Information is passed between different VDF branches through shared key values. Each VDF branch is used to represent different data element display states within the range of user-based displayable nodes in any decision scenario.

7. The programmable data processing method for multi-level users as described in claim 6, characterized in that, In any decision scenario, executing the VDF branch corresponding to the migration rule to maintain the decision scenario and migrate to the appropriate branch state includes: Upon initial activation of any of the decision scenarios, a view session is created based on the executed VDF to cover the entire lifecycle of any of the decision scenarios. For any migrated VDF branch, a shared key is maintained for the migrated view session to record interaction states and branch evolution history; and, In the view session, results are merged and instantiated based on the semantic tag set of data elements according to the migrated VDF branch.

8. The programmable data processing method for multi-level users as described in claim 4, characterized in that, In the case of monitoring changes in user permissions, this also includes: Identify the scope of changes to user permissions and mark the data elements within the scope of the changes; For data elements that need to be hidden, hide them immediately in the view session; and, After executing the corresponding VDF, the newly added data elements will be instantiated.

9. A programmable data processing system for multi-level users, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the steps of the programmable data processing method for multi-level users as described in any one of claims 1 to 8.

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