Business dispatching method and related device combined with business and group leader cockpit

Abstract

The application discloses a business scheduling method combined with business and group leader cockpit and related equipment. The method constructs a three-dimensional dynamic data matrix by obtaining equipment operation data and environmental monitoring data of multiple power plants; analyzes business team operation logs and group leader decision records based on natural language processing technology to generate dynamic user portraits of business teams and group leaders; extracts real-time change characteristics according to the three-dimensional dynamic data matrix, generates dynamic scheduling strategy information according to equipment operation frequency characteristic coefficients, strategic indicator sensitivity coefficients and real-time change characteristics through a multi-objective optimization algorithm; and pushes equipment operation instructions of the dynamic scheduling strategy information to the business cockpit according to the corresponding business authority of the business cockpit, and pushes strategic decision support graphs of the visual scheduling instruction set to the group cockpit according to the corresponding business authority of the group cockpit. A comprehensive business scheduling solution is provided, which provides strong support for business and management.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a business scheduling method and related equipment that combines business operations with a group leadership dashboard. Background Technology

[0002] In the operation and management of energy groups, the coordinated dispatch of multiple power plants has long faced the dual challenges of data fragmentation and a disconnect between decision-making levels. Traditional dispatch systems typically employ a single-dimensional data monitoring model, with the business side focusing on collecting equipment operating parameters and the management side relying on periodic summary reports, resulting in insufficient alignment between real-time dispatch instructions and strategic objectives. Existing technologies still have the following limitations:

[0003] 1. Static data application: Traditional cockpits focus on data visualization and lack a dynamic decision-making mechanism to transform real-time data streams into executable scheduling instructions. The generation of scheduling strategies relies on manual experience and cannot adapt to the rapid changes in complex operating conditions of multiple power plants.

[0004] 2. Lack of role collaboration: The business team's operating interface is independent of the group's decision-making system, there is a decision-making lag between equipment-level anomaly handling and strategic-level resource allocation, and the efficiency of cross-level instruction transmission is low;

[0005] 3. Weak feedback loop: The parameters of the existing scheduling model are fixed and cannot be dynamically corrected based on business execution feedback and strategic goal deviations, resulting in cumulative errors between the scheduling strategy and real-time operating conditions.

[0006] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0007] This application provides a business scheduling method and related equipment that combines business operations with a group leadership dashboard. It aims to solve the problem that traditional scheduling systems typically adopt a single-dimensional data monitoring mode, where the business side focuses on collecting equipment operating parameters and the management side relies on periodic summary reports, resulting in insufficient matching between real-time scheduling instructions and strategic goals.

[0008] Firstly, this application provides a business scheduling method that combines business operations with a group leadership dashboard, the method comprising:

[0009] The system acquires equipment operation data and environmental monitoring data from multiple power plants, and constructs a three-dimensional dynamic data matrix based on preset data layering rules. The three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters, and management layer parameters. The equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators.

[0010] By analyzing the business team's operation logs and the group leader's decision-making records using natural language processing technology, dynamic user profiles of the business team and the group leader are generated. The dynamic user profile of the business team includes the device operation frequency characteristic coefficient, and the dynamic user profile of the group leader includes the strategic indicator sensitivity coefficient.

[0011] Real-time change features are extracted from the three-dimensional dynamic data matrix. Dynamic scheduling strategy information is generated by a multi-objective optimization algorithm based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change features. The dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes.

[0012] Based on the business permissions corresponding to the business dashboard, the device operation instructions with dynamic scheduling strategy information are pushed to the business dashboard, and based on the business permissions corresponding to the group leadership dashboard, the strategic decision support map of the visualized scheduling instruction set is pushed to the group leadership dashboard; the device operation instructions include at least a priority marker for handling abnormal working conditions, and the strategic decision support map includes at least a heat map of scheduling benefit prediction.

[0013] In some embodiments, constructing a three-dimensional dynamic data matrix based on preset data layering rules includes: standardizing the equipment operation data and environmental monitoring data to obtain the equipment layer basic data stream; dynamically layering the equipment layer basic data stream and corresponding equipment types based on a preset clustering algorithm; constructing a business layer data cube based on the time series characteristics of the business parameters and production plan data corresponding to the business dashboard; coupling and analyzing the management indicator data corresponding to the group leadership dashboard with preset group strategic goals, and generating management dynamic indicators through a sliding time window algorithm; and concatenating the equipment layer basic data stream, business layer data cube, and management dynamic indicators under a unified timestamp to form a three-dimensional dynamic data matrix with spatiotemporal correlation characteristics.

[0014] In some embodiments, the step of parsing business team operation logs and group leader decision records based on natural language processing technology to generate dynamic user profiles of the business team and group leaders includes: performing named entity recognition on the operation logs to extract device operation entities and operation timing features; calculating device operation frequency feature coefficients based on device operation entities, operation timing features, and operation interval time; performing semantic role labeling on group leader decision records to construct a strategic indicator association graph, calculating the strategic indicator sensitivity coefficients corresponding to the strategic indicator association graph through an attention mechanism; and inputting the operation frequency feature coefficients and strategic indicator sensitivity coefficients into the corresponding user profile generation network to generate dynamic user profiles of the business team and group leaders.

[0015] In some embodiments, the step of extracting real-time change features based on the three-dimensional dynamic data matrix includes: extracting time slice data of the three-dimensional dynamic data matrix through a sliding time window; extracting temporal change features of device layer parameters and correlation features of business layer parameters from the device layer parameters and business layer parameters of the three-dimensional dynamic data matrix; performing wavelet packet transform decomposition on the management layer parameters of the three-dimensional dynamic data matrix to extract multi-scale management indicator fluctuation features; and fusing the temporal change features, business layer parameter correlation features, and multi-scale management indicator fluctuation features to generate a feature vector containing the real-time change features.

[0016] In some embodiments, the step of generating dynamic scheduling strategy information based on the equipment operating frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change characteristics using a multi-objective optimization algorithm includes: constructing a multi-objective function that includes equipment loss cost, energy utilization efficiency, and strategic synergy; normalizing the equipment operating frequency characteristic coefficient as a constraint condition, and determining the weight allocation of the strategic indicator sensitivity coefficient using the entropy weight method; and dynamically correcting the Pareto front solution by introducing real-time change characteristics to generate dynamic scheduling strategy information that satisfies multiple constraints.

[0017] In some embodiments, the step of pushing equipment operation instructions with dynamic scheduling strategy information to the business dashboard based on the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visualized scheduling instruction set to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard, includes: dynamically encrypting the equipment operation instructions; matching the encrypted equipment operation instructions with the operation permission level of the business dashboard; and pushing the abnormal condition handling priority markers corresponding to the successfully matched equipment operation instructions to the business dashboard; homomorphically encrypting the strategic decision support map; generating the scheduling benefit prediction heatmap through a visualization rendering engine; matching the scheduling benefit prediction heatmap with the operation permission level of the group leadership dashboard; and pushing the successfully matched scheduling benefit prediction heatmap to the group leadership dashboard.

[0018] In some embodiments, after pushing device operation instructions for dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visual scheduling instruction set to the group leader dashboard according to the business permissions corresponding to the group leader dashboard, the method further includes: obtaining operation response data from the business dashboard and feedback correction instructions from the group leader dashboard; updating the weight parameters of the dynamic user profile according to the operation response data and feedback correction instructions from the group leader dashboard through a preset adaptive learning model, and reconstructing the indicators of the three-dimensional dynamic data matrix to complete the dynamic closed-loop optimization of the dynamic scheduling strategy information.

[0019] Secondly, this application provides a business scheduling device that combines business operations with a group leadership cockpit, including:

[0020] The data acquisition unit is used to acquire equipment operation data and environmental monitoring data from multiple power plants to construct a three-dimensional dynamic data matrix based on preset data layering rules. The three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters, and management layer parameters. The equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators.

[0021] The profile generation unit is used to analyze the business team's operation logs and the group leader's decision-making records based on natural language processing technology to generate dynamic user profiles of the business team and the group leader; the dynamic user profile of the business team includes the device operation frequency characteristic coefficient, and the dynamic user profile of the group leader includes the strategic indicator sensitivity coefficient.

[0022] The strategy generation unit is used to extract real-time change features based on the three-dimensional dynamic data matrix, and generate dynamic scheduling strategy information through a multi-objective optimization algorithm based on the equipment operation frequency feature coefficient, strategic indicator sensitivity coefficient, and real-time change features; the dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes.

[0023] The scheduling completion unit is used to push equipment operation instructions with dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and to push a strategic decision support map of the visual scheduling instruction set to the group leadership dashboard according to the business permissions corresponding to the group leadership dashboard; the equipment operation instructions include at least a priority marker for handling abnormal working conditions, and the strategic decision support map includes at least a heat map of scheduling benefit prediction.

[0024] Thirdly, this application provides a computer device, including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method provided in any embodiment of this application.

[0025] Fourthly, this application provides a computer-readable storage medium storing a computer program, wherein the computer-readable instructions, when executed by a processor, cause one or more processors to perform the method provided in any embodiment of this application.

[0026] This application discloses a business scheduling method and related equipment that integrates business operations with a group leadership dashboard. This business scheduling method provides a comprehensive scheduling solution by integrating multi-source data and utilizing natural language processing and multi-objective optimization algorithms, aiming to improve the collaborative efficiency of business operations and management.

[0027] By acquiring equipment operation data (such as unit status and load) and environmental monitoring data (such as emissions and temperature) from multiple power plants, the data is divided into equipment layer, business layer, and management layer, constructing a three-dimensional matrix that covers parameters such as equipment operation, production load, and cross-power plant collaborative scheduling.

[0028] Analyze the operational logs of the business team and the decision-making records of management to extract feature coefficients (such as operation frequency and sensitivity). Generate dynamic profiles of the business team and management to reflect their operational habits and concerns.

[0029] Real-time changing features are extracted from a 3D matrix for computational optimization. Combined with user profile features, a multi-objective optimization algorithm is used to generate dynamic scheduling strategies, covering equipment start-up and shutdown, load allocation, and resource allocation. Information is pushed based on role permissions; the business dashboard receives equipment instructions, and the management dashboard receives visualized maps. Heatmaps predicting abnormal operating conditions and their associated benefits are provided to aid decision-making.

[0030] The provided methods improve the timeliness and accuracy of scheduling decisions through real-time data integration and dynamic adjustments. By integrating equipment, business, and management data, a comprehensive perspective is provided, avoiding the limitations of single-dimensional analysis. Dynamic user profiles help scheduling strategies adapt to the needs of different teams and management levels, providing personalized support. Intuitive visualization tools, such as heatmaps, are provided to help management quickly understand benefit forecasts and improve decision-making efficiency. Information sharing and collaboration between business teams and management are promoted, resource allocation is optimized, and overall operational efficiency is improved.

[0031] In summary, this method provides a comprehensive business scheduling solution by integrating multi-source data and utilizing advanced algorithms and visualization technologies. It significantly improves the real-time performance, accuracy, and collaborative efficiency of scheduling, providing strong support for business and management.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic flowchart illustrating the steps of a business scheduling method combining business operations and a group leadership dashboard, as provided in an embodiment of this application.

[0035] Figure 2 This is a schematic diagram of the interface of the group leader's cockpit provided in one embodiment of this application;

[0036] Figure 3 This is a schematic diagram of the interface of the business cockpit provided in one embodiment of this application;

[0037] Figure 4 This is a schematic diagram of the structure of a business scheduling device combining business operations and a group leadership cockpit, provided in one embodiment of this application.

[0038] Figure 5 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0040] 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, 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.

[0041] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0042] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0043] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0044] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0045] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0046] In the operation and management of energy groups, the coordinated dispatch of multiple power plants has long faced the dual challenges of data fragmentation and a disconnect between decision-making levels. Traditional dispatch systems typically employ a single-dimensional data monitoring model, with the business side focusing on collecting equipment operating parameters and the management side relying on periodic summary reports, resulting in insufficient alignment between real-time dispatch instructions and strategic objectives. Existing technologies still have the following limitations:

[0047] 1. Static data application: Traditional cockpits focus on data visualization and lack a dynamic decision-making mechanism to transform real-time data streams into executable scheduling instructions. The generation of scheduling strategies relies on manual experience and cannot adapt to the rapid changes in complex operating conditions of multiple power plants.

[0048] 2. Lack of role collaboration: The business team's operating interface is independent of the group's decision-making system, there is a decision-making lag between equipment-level anomaly handling and strategic-level resource allocation, and the efficiency of cross-level instruction transmission is low;

[0049] 3. Weak feedback loop: The parameters of the existing scheduling model are fixed and cannot be dynamically corrected based on business execution feedback and strategic goal deviations, resulting in cumulative errors between the scheduling strategy and real-time operating conditions.

[0050] Therefore, a method is urgently needed to solve at least one of the above problems.

[0051] To resolve the above issues, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic flowchart illustrating a business scheduling method combining business operations and a group leadership dashboard, according to an embodiment of this application. The method is executed by a computer device.

[0052] To solve the above problem, please refer to Figure 1 Specifically, such as Figure 1 As shown, the provided method includes steps S101 to S104. The computer device can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc. This is used to implement steps S101 to S104 and their corresponding embodiments.

[0053] Please refer to Figures 2-3 Among them, the business cockpit is a visual operation platform for front-line business teams in power plants. It integrates real-time equipment operation data, abnormal handling instructions and operation priority information, supports rapid response to equipment abnormalities, execution of scheduling instructions, and feedback of execution results.

[0054] The core functionality lies in receiving specific operational instructions (such as equipment start / stop and load adjustment) from dynamic scheduling strategies. It displays priority markers for abnormal operating conditions (such as red alerts requiring immediate action). It also provides operation log uploads and execution progress feedback.

[0055] The Group Leadership Dashboard is a strategic decision support system for senior managers of the energy group. It uses visual graphs to display the efficiency of cross-power plant resource allocation, the achievement of strategic goals, and risk warnings, assisting in the formulation of long-term resource plans and strategic adjustments.

[0056] Its core functions include displaying heatmaps of dispatch benefit predictions (such as regional cost fluctuations and carbon emission trends), providing early warnings of strategic indicator deviations (such as a power plant continuously exceeding its cost limits), and supporting simulations of the impact of different dispatch schemes on strategic objectives (such as feasibility analysis of "increasing the proportion of wind power").

[0057] The steps are detailed below:

[0058] Step S101. Obtain equipment operation data and environmental monitoring data from multiple power plants, and construct a three-dimensional dynamic data matrix based on preset data layering rules; wherein, the three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters and management layer parameters, the equipment layer parameters include unit operation status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators.

[0059] Specifically, this step integrates operational data from multiple power plant equipment (such as unit temperature and power output) and environmental monitoring data (such as weather and grid demand), and divides the data into three dimensions through preset rules:

[0060] Equipment-level parameters: Real-time collected unit status (such as fault alarms and energy efficiency).

[0061] Business layer parameters: Production task allocation (such as load allocation for each power plant and power output priority).

[0062] Management parameters: Cross-power plant collaboration objectives (such as cost control and carbon emission targets).

[0063] Device data is collected in real time via IoT sensors, cleaned, and stored on a unified platform. Data stratification rules are defined, for example, sensor data is categorized as the device layer, production plan data as the business layer, and strategic KPIs as the management layer. The matrix is ​​dynamically updated, such as refreshing device status every 5 minutes and adjusting load distribution every hour.

[0064] It breaks away from the traditional single-data monitoring model, enabling real-time correlation of device, business, and management data. This provides a real-time and comprehensive data foundation for subsequent strategy generation, reducing information fragmentation.

[0065] Step S102. Based on natural language processing technology, analyze the business team's operation logs and the group leader's decision-making records to generate dynamic user profiles for the business team and the group leader; wherein the dynamic user profile of the business team includes the device operation frequency characteristic coefficient, and the dynamic user profile of the group leader includes the strategic indicator sensitivity coefficient.

[0066] Specifically, Natural Language Processing (NLP) is used to analyze two types of text data: Business team operation logs: extracting equipment operation frequency and common operation types (e.g., "Unit A is restarted 3 times a day"). Group leadership decision-making records: analyzing strategic priorities (e.g., "prioritizing carbon emission reduction" or "improving cross-regional power supply efficiency").

[0067] Two types of profiles are generated: Business team profile: quantifying operational habits (e.g., a high "equipment operation frequency characteristic coefficient" indicates frequent equipment intervention). Group leadership profile: quantifying strategic preferences (e.g., a high "strategic indicator sensitivity coefficient" indicates low tolerance for cost fluctuations). Keywords are extracted from log text (e.g., "downtime," "load adjustment"), and operation frequency is statistically analyzed. High-frequency words in decision-making records (e.g., "emission reduction," "profit") are analyzed, and sensitivity coefficients are calculated. Profiles are dynamically updated: for example, if leadership frequently mentions "disaster backup power supply" recently, the relevant sensitivity weight is increased. The behavioral patterns and decision-making preferences of different roles are quantified to provide personalized input for strategy generation. Through continuous learning of user behavior, profiles automatically adjust as operations and decisions change, improving collaborative efficiency.

[0068] Step S103. Extract real-time change features from the three-dimensional dynamic data matrix, and generate dynamic scheduling strategy information through a multi-objective optimization algorithm based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change features; the dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes.

[0069] Specifically, key changes are identified from a three-dimensional matrix, such as a sudden drop in the efficiency of a power plant unit or a surge in regional electricity demand. By combining the frequency of operations (e.g., whether the business team can respond quickly) and strategic sensitivity (e.g. whether leadership values ​​cost) in the user profile, algorithms (e.g., genetic algorithms) are used to balance multiple objectives: equipment safety (e.g., avoiding overload), business efficiency (e.g., achieving load targets), and strategic indicators (e.g., minimizing total cost).

[0070] Output strategies include: Equipment start-up and shutdown schemes: shutting down inefficient units and starting standby units. Load allocation ratios: allocating power supply tasks in high-demand areas to the most efficient power plants. Cross-plant resource allocation: utilizing backup fuel or maintenance teams from other power plants.

[0071] When a power plant experiences a sudden failure, the system calculates the load gap based on real-time data and, considering the leadership's high sensitivity to power supply stability, prioritizes allocating resources from nearby power plants. If the business team's operation frequency coefficient is low (indicating a slow response), more detailed handling procedures are automatically generated (such as "shut down valve B of unit within 15 minutes").

[0072] The strategy is adjusted in real time according to working conditions and user profiles, reducing reliance on manual experience. It balances safety, efficiency, and strategic goals, avoiding sacrificing one aspect for another.

[0073] Step S104. Push equipment operation instructions with dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and push the strategic decision support map of the visual scheduling instruction set to the group leadership dashboard according to the business permissions corresponding to the group leadership dashboard; the equipment operation instructions shall at least include priority markings for handling abnormal working conditions, and the strategic decision support map shall at least include a heat map of scheduling benefit prediction.

[0074] Specifically, the policy is broken down into two types of instructions based on permissions:

[0075] Operational Dashboard: Receives priority-marked operational instructions (e.g., "Immediately address unit C overheating"), supporting rapid execution. Group Leadership Dashboard: Receives visual graphs (e.g., heat maps showing 24-hour scheduling benefit forecasts) to support strategic decision-making.

[0076] On the business side: Equipment operation lists are pushed out, with anomalies marked in red (e.g., "Prioritize troubleshooting faulty units"). On the leadership side: A heat map is displayed, with red areas indicating potential future cost overruns for a power plant, and green indicating compliance. Furthermore, in terms of access control, the business team cannot view strategic-level cost data, and leadership does not directly operate equipment.

[0077] To avoid information overload, different roles receive only essential information. Leadership uses heatmaps to quickly identify risks, and business teams execute tasks efficiently according to priority. Real-time push notifications ensure cross-level collaboration, shortening the "problem identification - strategy development - execution" cycle.

[0078] In some embodiments, constructing a three-dimensional dynamic data matrix based on preset data layering rules includes: standardizing the equipment operation data and environmental monitoring data to obtain the equipment layer basic data stream; dynamically layering the equipment layer basic data stream and corresponding equipment types based on a preset clustering algorithm; constructing a business layer data cube based on the time series characteristics of the business parameters and production plan data corresponding to the business dashboard; coupling and analyzing the management indicator data corresponding to the group leadership dashboard with preset group strategic goals, and generating management dynamic indicators through a sliding time window algorithm; and concatenating the equipment layer basic data stream, business layer data cube, and management dynamic indicators under a unified timestamp to form a three-dimensional dynamic data matrix with spatiotemporal correlation characteristics.

[0079] By standardizing the format of equipment operation data (such as temperature and power) and environmental data (such as weather and grid load) from different power plants, unit differences are eliminated. Data is categorized by equipment type (such as coal-fired units and wind turbines), for example, by grouping the operating status of similar equipment together using algorithms. Based on the time patterns of production plans (such as peak-hour load allocation), business data blocks that change over time (such as hourly load targets) are constructed. Group strategic indicators (such as cost control) are combined with management data (such as power plant operating costs), and the trends of indicator changes are analyzed through dynamic time segmentation (such as the last 7 days). The equipment, business, and management data are aligned according to a unified time, forming a three-dimensional matrix that includes space (power plant location), time (change trends), and hierarchy (equipment / business / management).

[0080] If a wind farm experiences fluctuations in power generation due to changes in wind speed, the system automatically categorizes its data with other wind power equipment in the same area and links it to the load allocation plan at the business level and the regional power supply stability indicators at the management level.

[0081] Eliminate data format differences among multiple power plants to achieve cross-system integration. Real-time display of the linkage between equipment operation, business plans, and strategic goals; for example, quickly pinpointing the impact on overall costs when a power plant's efficiency declines. Through a spatiotemporal correlation matrix, provide a global perspective for subsequent scheduling, avoiding the blind spots of traditional single-dimensional monitoring.

[0082] In some embodiments, the step of parsing business team operation logs and group leader decision records based on natural language processing technology to generate dynamic user profiles of the business team and group leaders includes: performing named entity recognition on the operation logs to extract device operation entities and operation timing features; calculating device operation frequency feature coefficients based on device operation entities, operation timing features, and operation interval time; performing semantic role labeling on group leader decision records to construct a strategic indicator association graph, calculating the strategic indicator sensitivity coefficients corresponding to the strategic indicator association graph through an attention mechanism; and inputting the operation frequency feature coefficients and strategic indicator sensitivity coefficients into the corresponding user profile generation network to generate dynamic user profiles of the business team and group leaders.

[0083] Specifically, the operation log is analyzed by identifying key operations (such as "Unit A restart") from the logs and calculating the operation time, frequency, and interval (such as 3 times per hour). The core objectives in the decision-making process (such as "reducing carbon emissions") are extracted, and an objective relationship graph (such as the relationship between "carbon emissions - cost - electricity generation") is constructed.

[0084] Calculate the equipment operation frequency coefficient (a high coefficient indicates frequent equipment intervention). Simultaneously, quantify strategic sensitivity through weighted analysis (e.g., sensitivity to "cost" is higher than sensitivity to "power supply"). Adjust the characteristic coefficients in real time based on the latest logs and decision records.

[0085] If a business team frequently operates a particular unit to handle a fault, the system automatically increases its operation frequency coefficient and prioritizes providing detailed operating steps when generating subsequent dispatch instructions. Group leaders have recently emphasized "disaster backup power supply," and the system has increased the sensitivity of this indicator, prioritizing backup power allocation when generating strategies.

[0086] By quantifying the operational habits of business teams and the strategic preferences of leadership, scheduling strategies become more aligned with actual needs. Through dynamic profiling, the system automatically predicts the concerns of different roles, shortening the chain of "problem discovery - strategy formulation - instruction issuance." Strategies are automatically adjusted based on changes in user behavior; for example, simpler instructions are provided to teams with weaker operational capabilities.

[0087] In some embodiments, the step of extracting real-time change features based on the three-dimensional dynamic data matrix includes: extracting time slice data of the three-dimensional dynamic data matrix through a sliding time window; extracting temporal change features of device layer parameters and correlation features of business layer parameters from the device layer parameters and business layer parameters of the three-dimensional dynamic data matrix; performing wavelet packet transform decomposition on the management layer parameters of the three-dimensional dynamic data matrix to extract multi-scale management indicator fluctuation features; and fusing the temporal change features, business layer parameter correlation features, and multi-scale management indicator fluctuation features to generate a feature vector containing the real-time change features.

[0088] By capturing three-dimensional data at fixed time intervals (e.g., every 15 minutes), short-term changes (such as a sudden drop in the power of a certain unit) can be analyzed.

[0089] The equipment layer captures trends in equipment parameters (e.g., a continuous rise in temperature). The business layer analyzes the correlation between load allocation and equipment status (e.g., a power plant experiencing efficiency decline due to overload). Management identifies abnormal signals (e.g., three consecutive days of cost overruns) by decomposing strategic indicators (e.g., costs) into fluctuations at different time scales (e.g., hourly cost fluctuations, daily trends). Real-time changes in equipment, business, and management are integrated into a comprehensive feature vector (e.g., "Unit A efficiency decline + regional load overrun + abnormal cost fluctuations").

[0090] If a coal-fired unit experiences a decrease in efficiency due to a malfunction, the system can detect through time slicing that its power output is consistently below the threshold. This is also linked to load imbalance at the business layer and excessive costs at the management layer, triggering an "emergency allocation of backup units" command.

[0091] This approach tracks the entire process from equipment malfunctions to strategic deviations, avoiding the limitations of traditional methods that focus on only a single problem. Through multi-scale feature analysis, it provides early warnings of potential risks (such as abnormal cost fluctuations), reducing accumulated errors. The fused feature vectors clearly pinpoint the root cause of the problem; for example, by simultaneously considering equipment failures and strategic target deviations, it generates more reasonable scheduling schemes.

[0092] In some embodiments, the step of generating dynamic scheduling strategy information based on the equipment operating frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change characteristics using a multi-objective optimization algorithm includes: constructing a multi-objective function that includes equipment loss cost, energy utilization efficiency, and strategic synergy; normalizing the equipment operating frequency characteristic coefficient as a constraint condition, and determining the weight allocation of the strategic indicator sensitivity coefficient using the entropy weight method; and dynamically correcting the Pareto front solution by introducing real-time change characteristics to generate dynamic scheduling strategy information that satisfies multiple constraints.

[0093] Simultaneously optimize three core objectives: reduce equipment wear and tear (e.g., extend unit lifespan), improve energy efficiency (e.g., reduce unit power generation costs), and meet group strategic synergies (e.g., cross-power plant resource allocation aligns with emission reduction targets). Transform the operational frequency of business teams (e.g., daily operation limits) into constraints to avoid excessive reliance on manual intervention. Dynamically allocate target weights based on the group leadership's focus on strategic indicators (e.g., prioritizing cost control or power supply stability). Combine real-time data (e.g., sudden power plant failures) with dynamically adjusted strategies from a pre-defined optimal solution set (e.g., temporarily increasing standby unit load).

[0094] When electricity demand surges in a region, the system prioritizes power plants with high energy efficiency to increase load, while avoiding overuse of aging equipment and ensuring compliance with the group's carbon emission targets.

[0095] Avoid one-sided optimization due to a single objective (such as pursuing efficiency while ignoring equipment wear and tear). Use real-time data to drive strategy adjustments, such as quickly switching to backup plans in the event of sudden failures. Ensure that scheduling strategies are always aligned with the group's long-term goals (such as low-carbon transformation).

[0096] In some embodiments, the step of pushing equipment operation instructions with dynamic scheduling strategy information to the business dashboard based on the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visualized scheduling instruction set to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard, includes: dynamically encrypting the equipment operation instructions; matching the encrypted equipment operation instructions with the operation permission level of the business dashboard; and pushing the abnormal condition handling priority markers corresponding to the successfully matched equipment operation instructions to the business dashboard; homomorphically encrypting the strategic decision support map; generating the scheduling benefit prediction heatmap through a visualization rendering engine; matching the scheduling benefit prediction heatmap with the operation permission level of the group leadership dashboard; and pushing the successfully matched scheduling benefit prediction heatmap to the group leadership dashboard.

[0097] Specifically, business-side command encryption dynamically encrypts equipment operation commands (such as "shut down the faulty unit"), allowing only authorized business teams to decrypt and view them, preventing accidental operation or information leakage. Leadership-side graph encryption employs more advanced encryption technology on strategic decision-making graphs (such as cost forecast heatmaps) to ensure data security for leadership while maintaining visualization effectiveness. Access control ensures that business teams only receive abnormal handling commands (such as tasks marked "urgent") from the power plants under their jurisdiction.

[0098] The group's leaders viewed the overall heat map but were unable to directly operate the equipment.

[0099] A power plant's operations team received an encrypted instruction: "Inspect Unit A within one hour," but other power plant teams could not view this instruction. Group leaders saw a warning on the heat map stating "Career area may exceed cost limits tomorrow," but they lacked the authority to modify specific equipment parameters.

[0100] Hierarchical encryption prevents unauthorized operations and data leaks. Different roles only access necessary information to avoid information overload. Clearly define the boundaries between business team execution and leadership decision-making to reduce cross-level interference.

[0101] In some embodiments, after pushing device operation instructions for dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visual scheduling instruction set to the group leader dashboard according to the business permissions corresponding to the group leader dashboard, the method further includes: obtaining operation response data from the business dashboard and feedback correction instructions from the group leader dashboard; updating the weight parameters of the dynamic user profile according to the operation response data and feedback correction instructions from the group leader dashboard through a preset adaptive learning model, and reconstructing the indicators of the three-dimensional dynamic data matrix to complete the dynamic closed-loop optimization of the dynamic scheduling strategy information.

[0102] By acquiring real-time data on the execution results of scheduling instructions from business teams (e.g., whether faults have been repaired) and feedback from leadership on the effectiveness of strategies (e.g., whether costs have been met), the system automatically adjusts user profile weights (e.g., reducing the operation frequency coefficient after business team response speed improves) and 3D data matrix indicators (e.g., adding monitoring for new strategic goals) based on feedback data. The updated data and profiles are then re-input into the optimization model to generate more accurate scheduling strategies. If a business team repeatedly delays responding to a certain type of fault, the system automatically increases the priority coefficient for handling that type of fault, prioritizing the allocation of backup resources in subsequent strategies. If group leadership reports insufficient weight for "disaster recovery power supply," the system adds disaster recovery resource monitoring indicators to the matrix and optimizes subsequent scheduling plans.

[0103] The system continuously improves through feedback, reducing manual intervention. Strategies are dynamically updated as business teams enhance their capabilities or strategic goals adjust. Data and strategy discrepancies are corrected in real time to prevent systemic errors caused by prolonged implementation.

[0104] In some embodiments, by embedding AI prediction models into a three-dimensional dynamic data matrix, long-term trends of equipment-level parameters (such as vibration frequency and temperature) are analyzed to predict potential faults (such as "risk of unit bearing wear"). Combined with the operational frequency characteristics of the business team, preventative maintenance instructions are automatically generated (such as "reduce unit load by 10% in the next 3 days"). The prediction results are synchronized to the group's dashboard to demonstrate the impact of fault predictions on strategic objectives (such as "maintenance costs increase by 5%").

[0105] Shift from "post-failure handling" to "pre-failure prevention" to reduce downtime losses. Leadership anticipates maintenance cost fluctuations and adjusts resource allocation plans accordingly.

[0106] In some embodiments, operational instructions from the business dashboard (such as "shut down unit A") are stored on the blockchain, recording the operation time, the personnel executing the instructions, and changes in equipment status. Strategic decision-making instructions from the group dashboard (such as "reduce power supply to a certain area") verify permissions through smart contracts, ensuring that only authorized personnel can trigger critical instructions. End-to-end auditing functionality is provided to support rapid tracing of the causes of instruction execution deviations (such as "a load allocation error stemmed from unauthorized operation permissions").

[0107] Ensure that operational and decision-making data is tamper-proof and meets compliance audit requirements. Clearly define operational responsibilities across levels to reduce buck-passing and execution deviations.

[0108] In some embodiments, the business cockpit uses AR glasses to overlay real-time device data (such as pressure values ​​and current fluctuations) to guide on-site personnel through step-by-step maintenance (e.g., "rotate the valve to the marked position"). The group cockpit constructs a 3D digital twin sand table to simulate the operating status of all power plants in the group under different scheduling schemes (e.g., "the impact of shutting down a power plant on the regional power grid"), and supports gesture interaction to adjust strategic parameters.

[0109] Reduce the error rate of frontline staff and shorten the time for troubleshooting. Enable leadership to directly perceive the overall impact of strategic adjustments and improve the accuracy of decision-making.

[0110] In some embodiments, a dynamic pricing model is introduced into the cross-power plant resource allocation scheme, adjusting internal resource allocation prices in real time based on supply and demand (e.g., regional peak electricity consumption) (e.g., "coal-fired unit operating costs increase by 20% during peak periods"). The business dashboard displays current resource prices, guiding the team to select the most cost-effective allocation scheme (e.g., "prioritizing wind power to replace high-priced coal"). The group dashboard demonstrates the impact of price fluctuations on strategic objectives (e.g., "dynamic pricing reduces total quarterly costs by 8%"). This price lever guides efficient resource allocation and reduces waste. The real-time pricing model helps the group balance short-term expenditures with long-term strategic investments.

[0111] Please see Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of the business scheduling device 200 integrating business and group leadership dashboard provided in this application embodiment. The business scheduling device 200 integrating business and group leadership dashboard is used to execute the steps of the business scheduling method integrating business and group leadership dashboard shown in the above embodiments. The business scheduling device 200 integrating business and group leadership dashboard can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0112] like Figure 4As shown, the business scheduling device 200, which integrates business operations and the group leadership cockpit, includes:

[0113] The data acquisition unit 201 is used to acquire equipment operation data and environmental monitoring data from multiple power plants to construct a three-dimensional dynamic data matrix based on preset data layering rules. The three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters, and management layer parameters. The equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators.

[0114] The profile generation unit 202 is used to analyze the business team's operation logs and the group leader's decision-making records based on natural language processing technology to generate dynamic user profiles of the business team and the group leader; the dynamic user profile of the business team includes the device operation frequency characteristic coefficient, and the dynamic user profile of the group leader includes the strategic indicator sensitivity coefficient.

[0115] The strategy generation unit 203 is used to extract real-time change features based on the three-dimensional dynamic data matrix, and generate dynamic scheduling strategy information through a multi-objective optimization algorithm based on the equipment operation frequency feature coefficient, strategic indicator sensitivity coefficient and real-time change features; the dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios and cross-power plant resource allocation schemes.

[0116] The scheduling completion unit 204 is used to push equipment operation instructions with dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and to push a strategic decision support map of the visual scheduling instruction set to the group leadership dashboard according to the business permissions corresponding to the group leadership dashboard; the equipment operation instructions include at least a priority marker for handling abnormal working conditions, and the strategic decision support map includes at least a scheduling benefit prediction heatmap.

[0117] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the business scheduling device and each unit that combine business and group leadership cockpit described above can be referred to the corresponding processes in the business scheduling embodiments that combine business and group leadership cockpit described above, and will not be repeated here.

[0118] The aforementioned business scheduling, which combines business operations with the group leadership dashboard, can be implemented as a computer program, which can be used in, for example... Figure 4 It runs on the device shown.

[0119] Please see Figure 5 , Figure 5This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0120] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any combination of business and group leadership dashboard operations.

[0121] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0122] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When these computer programs are executed by the processor, the processor can perform any business scheduling that combines business operations with the group leadership cockpit.

[0123] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 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 terminal 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.

[0124] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also 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. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0125] In one embodiment, the processor is used to run a computer program stored in a memory to acquire equipment operation data and environmental monitoring data from multiple power plants, and to construct a three-dimensional dynamic data matrix based on preset data layering rules; wherein the three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters and management layer parameters, the equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators;

[0126] By analyzing the business team's operation logs and the group leader's decision-making records using natural language processing technology, dynamic user profiles of the business team and the group leader are generated. The dynamic user profile of the business team includes the device operation frequency characteristic coefficient, and the dynamic user profile of the group leader includes the strategic indicator sensitivity coefficient.

[0127] Real-time change features are extracted from the three-dimensional dynamic data matrix. Dynamic scheduling strategy information is generated by a multi-objective optimization algorithm based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change features. The dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes.

[0128] Based on the business permissions corresponding to the business dashboard, the device operation instructions with dynamic scheduling strategy information are pushed to the business dashboard, and based on the business permissions corresponding to the group leadership dashboard, the strategic decision support map of the visualized scheduling instruction set is pushed to the group leadership dashboard; the device operation instructions include at least a priority marker for handling abnormal working conditions, and the strategic decision support map includes at least a heat map of scheduling benefit prediction.

[0129] In some embodiments, constructing a three-dimensional dynamic data matrix based on preset data layering rules includes: standardizing the equipment operation data and environmental monitoring data to obtain the equipment layer basic data stream; dynamically layering the equipment layer basic data stream and corresponding equipment types based on a preset clustering algorithm; constructing a business layer data cube based on the time series characteristics of the business parameters and production plan data corresponding to the business dashboard; coupling and analyzing the management indicator data corresponding to the group leadership dashboard with preset group strategic goals, and generating management dynamic indicators through a sliding time window algorithm; and concatenating the equipment layer basic data stream, business layer data cube, and management dynamic indicators under a unified timestamp to form a three-dimensional dynamic data matrix with spatiotemporal correlation characteristics.

[0130] In some embodiments, the step of parsing business team operation logs and group leader decision records based on natural language processing technology to generate dynamic user profiles of the business team and group leaders includes: performing named entity recognition on the operation logs to extract device operation entities and operation timing features; calculating device operation frequency feature coefficients based on device operation entities, operation timing features, and operation interval time; performing semantic role labeling on group leader decision records to construct a strategic indicator association graph, calculating the strategic indicator sensitivity coefficients corresponding to the strategic indicator association graph through an attention mechanism; and inputting the operation frequency feature coefficients and strategic indicator sensitivity coefficients into the corresponding user profile generation network to generate dynamic user profiles of the business team and group leaders.

[0131] In some embodiments, the step of extracting real-time change features based on the three-dimensional dynamic data matrix includes: extracting time slice data of the three-dimensional dynamic data matrix through a sliding time window; extracting temporal change features of device layer parameters and correlation features of business layer parameters from the device layer parameters and business layer parameters of the three-dimensional dynamic data matrix; performing wavelet packet transform decomposition on the management layer parameters of the three-dimensional dynamic data matrix to extract multi-scale management indicator fluctuation features; and fusing the temporal change features, business layer parameter correlation features, and multi-scale management indicator fluctuation features to generate a feature vector containing the real-time change features.

[0132] In some embodiments, the step of generating dynamic scheduling strategy information based on the equipment operating frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change characteristics using a multi-objective optimization algorithm includes: constructing a multi-objective function that includes equipment loss cost, energy utilization efficiency, and strategic synergy; normalizing the equipment operating frequency characteristic coefficient as a constraint condition, and determining the weight allocation of the strategic indicator sensitivity coefficient using the entropy weight method; and dynamically correcting the Pareto front solution by introducing real-time change characteristics to generate dynamic scheduling strategy information that satisfies multiple constraints.

[0133] In some embodiments, the step of pushing equipment operation instructions with dynamic scheduling strategy information to the business dashboard based on the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visualized scheduling instruction set to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard, includes: dynamically encrypting the equipment operation instructions; matching the encrypted equipment operation instructions with the operation permission level of the business dashboard; and pushing the abnormal condition handling priority markers corresponding to the successfully matched equipment operation instructions to the business dashboard; homomorphically encrypting the strategic decision support map; generating the scheduling benefit prediction heatmap through a visualization rendering engine; matching the scheduling benefit prediction heatmap with the operation permission level of the group leadership dashboard; and pushing the successfully matched scheduling benefit prediction heatmap to the group leadership dashboard.

[0134] In some embodiments, after pushing device operation instructions for dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visual scheduling instruction set to the group leader dashboard according to the business permissions corresponding to the group leader dashboard, the method further includes: obtaining operation response data from the business dashboard and feedback correction instructions from the group leader dashboard; updating the weight parameters of the dynamic user profile according to the operation response data and feedback correction instructions from the group leader dashboard through a preset adaptive learning model, and reconstructing the indicators of the three-dimensional dynamic data matrix to complete the dynamic closed-loop optimization of the dynamic scheduling strategy information.

[0135] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the processor described above can be referred to the corresponding process in the business scheduling embodiments combining business and the group leadership cockpit described above, and will not be repeated here.

[0136] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the business scheduling method combining business and group leadership cockpit provided in the above embodiments of this application.

[0137] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0138] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A business scheduling method combining business operations and a group leadership dashboard, characterized in that, include: This method acquires equipment operation data and environmental monitoring data from multiple power plants to construct a three-dimensional dynamic data matrix based on preset data layering rules. The equipment operation data includes unit temperature and power output, while the environmental monitoring data includes weather and grid demand. The three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters, and management layer parameters. The equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators. Constructing the three-dimensional dynamic data matrix based on preset data layering rules includes: standardizing the equipment operation data and environmental monitoring data to obtain the equipment layer basic data stream; dynamically layering the data stream and corresponding equipment types based on a preset clustering algorithm; constructing a business layer data cube based on the time-series characteristics of business parameters and production plan data corresponding to the business dashboard; coupling and analyzing the management layer indicator data corresponding to the group leadership dashboard with preset group strategic goals, and generating management layer dynamic indicators through a sliding time window algorithm; and concatenating the equipment layer basic data stream, business layer data cube, and management layer dynamic indicators under a unified timestamp using matrix tensors to form a three-dimensional dynamic data matrix with spatiotemporal correlation characteristics. By analyzing the business team's operation logs and the group leader's decision-making records using natural language processing technology, dynamic user profiles of the business team and the group leader are generated. The dynamic user profile of the business team includes equipment operation frequency characteristic coefficients, and the dynamic user profile of the group leader includes strategic indicator sensitivity coefficients. The operation frequency characteristic coefficients are used to characterize the frequency of intervention in equipment, and the strategic indicator sensitivity coefficients are used to characterize the tolerance for cost fluctuations. Based on the three-dimensional dynamic data matrix, real-time change features are extracted. Dynamic scheduling strategy information is generated using a multi-objective optimization algorithm based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change features. The dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes. Extracting real-time change features from the three-dimensional dynamic data matrix includes: extracting time slice data from the three-dimensional dynamic data matrix using a sliding time window; extracting time-series change features of equipment layer parameters and correlation features of business layer parameters from the equipment layer parameters and business layer parameters of the three-dimensional dynamic data matrix; performing wavelet packet transform decomposition on the management layer parameters of the three-dimensional dynamic data matrix to extract multi-scale management indicator fluctuation features; and fusing the time-series change features, business layer parameter correlation features, and multi-scale management indicator fluctuation features to generate a feature vector containing the real-time change features. Based on the business permissions corresponding to the business dashboard, dynamic scheduling strategy information is pushed to the business dashboard, and a strategic decision support map of the visualized scheduling instruction set is pushed to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard. The equipment operation instructions include at least a priority marker for handling abnormal operating conditions, and the strategic decision support map includes at least a scheduling benefit prediction heatmap. The strategic decision support map is homomorphically encrypted, and the scheduling benefit prediction heatmap is generated through a visualization rendering engine.

2. The method according to claim 1, characterized in that, The process of analyzing business team operation logs and group leadership decision-making records using natural language processing technology to generate dynamic user profiles for business teams and group leaders includes: Named entity recognition is performed on the operation logs to extract device operation entities and operation timing features; Calculate the equipment operation frequency characteristic coefficient based on the equipment operation entity, operation timing characteristics, and operation interval time; Semantic role labeling is performed on the decision-making records of group leaders to construct a strategic indicator association graph. The sensitivity coefficient of the strategic indicator corresponding to the strategic indicator association graph is calculated through an attention mechanism. The operation frequency characteristic coefficient and the strategic indicator sensitivity coefficient are respectively input into the corresponding user profile generation network to generate dynamic user profiles for business teams and group leaders.

3. The method according to claim 1, characterized in that, The process of generating dynamic scheduling strategy information based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change characteristics using a multi-objective optimization algorithm includes: Construct a multi-objective function that includes equipment depreciation costs, energy utilization efficiency, and strategic synergy. The equipment operation frequency characteristic coefficient is normalized as a constraint condition, and the strategic indicator sensitivity coefficient is weighted using the entropy weight method. Real-time changing features are introduced into the Pareto front solution set for dynamic correction, generating dynamic scheduling strategy information that satisfies multiple constraints.

4. The method according to claim 1, characterized in that, The process of pushing device operation instructions with dynamic scheduling strategy information to the business dashboard based on the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visualized scheduling instruction set to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard, includes: The device operation commands are dynamically encrypted, and the encrypted device operation commands are matched with the operation permission level of the business cockpit. The priority flag for handling abnormal conditions corresponding to the successfully matched device operation commands is pushed to the business cockpit. The scheduling benefit prediction heatmap is matched with the operation permission level of the group leader's cockpit, and the successfully matched scheduling benefit prediction heatmap is pushed to the group leader's cockpit.

5. The method according to claim 1, characterized in that, After the step of pushing device operation instructions with dynamic scheduling strategy information to the business dashboard based on the business permissions corresponding to the business dashboard, and pushing a strategic decision support map of the visualized scheduling instruction set to the group leadership dashboard based on the business permissions corresponding to the group leadership dashboard, the following is also included: Acquire operational response data from the business dashboard and feedback correction instructions from the group leadership dashboard; The dynamic user profile's weight parameters are updated based on the operation response data and feedback correction instructions from the group leader's cockpit using a preset adaptive learning model. The three-dimensional dynamic data matrix is ​​then reconstructed to reconstruct the indicators, thus completing the dynamic closed-loop optimization of the dynamic scheduling strategy information.

6. A business scheduling device combining business operations and a group leadership cockpit, characterized in that, The device includes: A data acquisition unit is used to acquire equipment operation data and environmental monitoring data from multiple power plants to construct a three-dimensional dynamic data matrix based on preset data layering rules. The equipment operation data includes unit temperature and power output, and the environmental monitoring data includes weather and grid demand. The three-dimensional dynamic data matrix includes equipment layer parameters, business layer parameters, and management layer parameters. The equipment layer parameters include unit operating status parameters, the business layer parameters include production load allocation parameters, and the management layer parameters include cross-power plant collaborative scheduling indicators. Constructing the three-dimensional dynamic data matrix based on preset data layering rules includes: standardizing the equipment operation data and environmental monitoring data to obtain the equipment layer basic data stream; dynamically layering the equipment layer basic data stream and corresponding equipment types based on a preset clustering algorithm; constructing a business layer data cube based on the time series characteristics of the business parameters and production plan data corresponding to the business dashboard; coupling and analyzing the management layer indicator data corresponding to the group leadership dashboard with preset group strategic goals, and generating management layer dynamic indicators through a sliding time window algorithm; and concatenating the equipment layer basic data stream, business layer data cube, and management layer dynamic indicators under a unified timestamp using matrix tensors to form a three-dimensional dynamic data matrix with spatiotemporal correlation characteristics. The profile generation unit is used to analyze the business team's operation logs and the group leader's decision-making records based on natural language processing technology to generate dynamic user profiles for the business team and the group leader. The dynamic user profile of the business team includes equipment operation frequency characteristic coefficients, and the dynamic user profile of the group leader includes strategic indicator sensitivity coefficients. The operation frequency characteristic coefficients are used to characterize the frequency of intervention in equipment, and the strategic indicator sensitivity coefficients are used to characterize the tolerance for cost fluctuations. The strategy generation unit is used to extract real-time change features based on the three-dimensional dynamic data matrix, and to generate dynamic scheduling strategy information using a multi-objective optimization algorithm based on the equipment operation frequency characteristic coefficient, strategic indicator sensitivity coefficient, and real-time change features. The dynamic scheduling strategy information includes equipment start-up and shutdown schemes, load allocation ratios, and cross-power plant resource allocation schemes. The extraction of real-time change features based on the three-dimensional dynamic data matrix includes: extracting time slice data of the three-dimensional dynamic data matrix through a sliding time window; extracting time-series change features of equipment layer parameters and correlation features of business layer parameters from the equipment layer parameters and business layer parameters of the three-dimensional dynamic data matrix; performing wavelet packet transform decomposition on the management layer parameters of the three-dimensional dynamic data matrix to extract multi-scale management indicator fluctuation features; and fusing the time-series change features, business layer parameter correlation features, and multi-scale management indicator fluctuation features to generate a feature vector containing the real-time change features. The scheduling completion unit is used to push equipment operation instructions with dynamic scheduling strategy information to the business dashboard according to the business permissions corresponding to the business dashboard, and to push a strategic decision support map of the visual scheduling instruction set to the group leadership dashboard according to the business permissions corresponding to the group leadership dashboard; the equipment operation instructions include at least an abnormal working condition handling priority marker, and the strategic decision support map includes at least a scheduling benefit prediction heatmap; the strategic decision support map is homomorphically encrypted, and the scheduling benefit prediction heatmap is generated by the visualization rendering engine.

7. A computer device, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes one or more of the processors to perform the steps of the method as described in any one of claims 1 to 5.

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