Energy and carbon emission online monitoring and collaborative management and control method and system

Abstract

The invention provides an energy and carbon emission online monitoring and collaborative management and control method and system, and relates to the technical field of collaborative management and control, and the method comprises the steps: obtaining energy time sequence data, emission time sequence data and production plan time sequence data; performing segmentation processing on the energy time sequence data and the emission time sequence data, and establishing a mapping relation between energy consumption segments and emission segments through a segmentation corresponding mode; performing structured processing on the production plan time sequence data, and matching the structured production plan time sequence data with the energy emission associated data section by section according to a unified time axis; comparing the energy consumption demand with the available load of the energy section, comparing the emission expectation with the available emission limit of the emission section, and identifying a resource conflict section through section comparison; generating collaborative adjustment data; according to the invention, autonomy and accuracy of energy and carbon emission cooperative control are improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative management and control technology, and in particular to methods and systems for online monitoring and collaborative management of energy and carbon emissions. Background Technology

[0002] In existing technologies, online management of energy and carbon emissions typically relies on modular enterprise resource management systems (ERPs). A typical deployment involves building the energy acquisition platform, emissions accounting module, production scheduling module, and energy cost accounting module into independent business subsystems, exchanging data via an enterprise service bus or interface-based methods. These systems often employ fixed emission factors, static energy consumption models, and preset process rules when calculating energy consumption and emissions. In daily enterprise operations, these systems primarily handle functions such as energy consumption data reporting, emissions accounting, quota usage statistics, and production plan reference. However, the overall process structure is relatively fixed, business rules still rely on manual configuration, and the subsystems only exchange final result data, making it difficult to form an integrated and dynamic linkage for energy, emissions, and business decision-making.

[0003] In scenarios requiring simultaneous consideration of energy and business activities, such as when large manufacturing enterprises formulate monthly production schedules, the existing management systems often have significant limitations. Taking actual scheduling as an example, the planning system typically only obtains aggregated energy and emissions data from the previous settlement period, while the raw data from the energy management subsystem remains stored in a fragmented, daily, and equipment-specific manner. Due to the lack of a time-series-oriented correlation mechanism, scheduling simulations cannot automatically incorporate key factors such as energy consumption trends during product processing, emissions differences under different load conditions, and the dynamic utilization of remaining carbon allowances. This can lead to problems such as scheduling high-energy-consuming processes during periods of energy constraints, which the system cannot identify in advance when generating the schedule, resulting in frequent adjustments during subsequent execution and even emissions accounting deviations. The root cause is that current technology cannot manage and analyze energy monitoring data, production planning data, and emissions constraints in a unified timeline. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for online monitoring and coordinated management of energy and carbon emissions, aiming to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for online monitoring and coordinated management of energy and carbon emissions, the method comprising: Acquire energy time series data, emission time series data, and production plan time series data, and perform time granularity unification and timestamp calibration to generate unified time axis data; Based on the unified timeline data, the energy time series data is segmented according to equipment identifier, process identifier, and load segment, and the emission time series data is segmented according to emission source identifier and emission segment. The mapping relationship between energy consumption segments and emission segments is established through the segment correspondence method to generate energy emission correlation data. Based on energy emission correlation data, the production plan time series data is structured, and the production task identifier, scheduled execution segment and resource demand are converted into a unified format. The structured production plan time series data is then matched with the energy emission correlation data segment by segment along a unified time axis to generate task constraint data. Based on task constraint data, energy consumption demand is compared with available load in energy segments, and emission expectations are compared with available emission allowances in emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated according to the type of constraint. Based on the conflict classification data, generate collaborative adjustment data including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The applicable adjustment methods are determined based on the conflict classification data, and the plan adjustment instructions are generated based on the coordinated adjustment data; The system sends adjustment instructions to the production planning subsystem and receives the adjusted production plan time series data to support continuous coordinated management of energy, emissions, and production planning.

[0006] Preferably, based on unified timeline data, energy time series data is segmented according to equipment identifier, process identifier, and load segment; emission time series data is segmented according to emission source identifier and emission segment; and a mapping relationship between energy consumption segments and emission segments is established through segment correspondence to generate energy emission correlation data, including: The energy time series data is analyzed point by point, and the analysis results are divided into segments according to equipment identifiers, process identifiers, and upper and lower load limits to generate basic energy consumption segment data. The emission time series data is analyzed point by point, and the analysis results are divided into segments according to the emission source identification and emission change trend to generate emission basic segment data. Based on the unified time axis data, the energy consumption baseline data and emission baseline data are time-aligned to generate time-aligned data segments. Based on the time-aligned segment data, the correspondence between the energy consumption basic segment data and the emission basic segment data in terms of time coverage and identification matching is compared, and segments that meet the matching conditions are combined to generate mapped segment data. Based on the mapped segment data, output energy emission correlation data as input for subsequent generation of task constraint data.

[0007] Preferably, based on energy emission correlation data, the production plan time series data is structured, converting production task identifiers, predetermined execution segments, and resource requirements into a unified format. The structured production plan time series data is then matched segment by segment with the energy emission correlation data along a unified time axis to generate task constraint data, including: Extract production task identifiers, scheduled execution times, and resource requirements from production plan time series data, and transform them into task structured data in a unified format; Time mapping processing is performed based on the time segments of energy emission correlation data and task structured data to generate task time mapping data; Based on the task time mapping data, the energy consumption demand in the task structured data is correlated with the energy consumption range in the energy emission correlation data segment by segment to generate energy consumption constraint data. Based on the task time mapping data, the emission impact in the task structured data is correlated with the emission magnitude in the energy emission correlation data segment by segment to generate emission constraint data; Constraint synthesis processing is performed based on energy consumption constraint data and emission constraint data to generate task constraint data.

[0008] Preferably, based on task constraint data, energy consumption demand is compared with the available load of energy segments, and emission expectations are compared with the available emission allowances of emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated according to the constraint type, including: Energy consumption constraint data is extracted segment by segment based on task constraint data, and the difference is compared with the available load of energy segment to generate energy consumption difference data. Identify sections where the energy consumption difference is below the safety margin based on the energy consumption difference data, and record them as energy-side conflict data. Based on the task constraint data, emission constraint data is extracted segment by segment, and the difference is compared with the available emission credits of the emission segment to generate emission difference data. Identify segments where the emission difference is below the emission threshold based on the emission difference data, and record them as emission-side conflict data. Conflict fusion processing is performed based on energy-side conflict data and emission-side conflict data. Data with both types of conflict are comprehensively judged to generate composite conflict data. Energy-side conflict data, emissions-side conflict data, and composite conflict data are merged to generate conflict classification data.

[0009] Preferably, collaborative adjustment data is generated based on conflict classification data, including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes, comprising: Based on the conflict classification data, identify energy-side conflict zones and generate candidate alternative execution zone data based on the time periods with higher available load within the energy zones; Based on the conflict classification data, emission-side conflict zones are identified, and emission substitution implementation zone data are generated based on the time periods with more lenient emission constraints. Based on the conflict classification data, identify the composite conflict segments, and divide the execution segments into multiple sub-segments according to the continuity rules based on the task's separable attributes, generating segment splitting data; Based on the task-equipment correspondence in the conflict classification data, alternative equipment that can reduce energy consumption or reduce emissions impact is selected, and equipment switching data is generated. Based on the load differences and capacity relationships between tasks, load adjustment processing is performed on the energy consumption ratio of adjacent tasks to generate load redistribution data; The candidate alternative implementation segment data, emission alternative implementation segment data, segment split data, equipment switching data, and load redistribution data are merged to generate coordinated adjustment data.

[0010] Preferably, based on the time-aligned segment data, the correspondence between the energy consumption baseline segment data and the emission baseline segment data in terms of time coverage and identification matching degree is compared, and segments that meet the matching conditions are combined to generate mapped segment data, including: Extract the start and end times of the energy consumption basic segment data, as well as the corresponding equipment and process identifiers, from the time-aligned segment data to generate energy consumption segment feature data. Based on the time-aligned segment data, the start and end times of the emission base segment data and the corresponding emission source identifiers are extracted to generate emission segment feature data. Based on the comparison of the time overlap duration between the energy consumption segment characteristic data and the emission segment characteristic data, coverage determination data representing the time coverage relationship is generated. Based on the coverage determination data, segments whose time overlap duration reaches the preset coverage conditions are formed into time-matching segment data. Based on the time-matching segment data, the correlation between equipment identifiers, process identifiers and emission source identifiers is compared to generate identifier matching judgment data; Based on the identifier matching judgment data, segments that meet the matching conditions are combined to form mapped segment data.

[0011] Preferably, based on the load differences and capacity relationships between tasks, load adjustment processing is performed on the energy consumption ratio of adjacent tasks to generate load redistribution data, including: Based on the energy consumption requirements of adjacent tasks and the execution segment, task load characteristic data is extracted to generate initial load data for load comparison. Based on the initial load data, compare the load differences between adjacent tasks and mark the task segments whose load differences reach the adjustment conditions as adjustable segment data. Based on the adjustable section data, load reduction processing is performed on the energy consumption ratio of high-load tasks to generate reduced load data, and the remaining load after reduction is used as input for compensation processing. Perform load compensation processing on low-load tasks based on adjustable section data, allocate compensation ratios based on load reduction data, and generate compensated load data. Load balancing is performed based on load reduction data and load compensation data, and the two are combined in chronological and task order to generate load redistribution data.

[0012] Secondly, an online monitoring and collaborative management system for energy and carbon emissions, the system comprising: The data acquisition module is used to acquire energy time series data, emission time series data, and production plan time series data, and to perform time granularity unification and timestamp calibration processing to generate unified time axis data; The segmentation processing module is used to segment energy time series data according to equipment identifier, process identifier and load segment based on unified time axis data, and to segment emission time series data according to emission source identifier and emission segment. It also establishes a mapping relationship between energy consumption segments and emission segments through segmentation correspondence, and generates energy emission related data. The planning matching module is used to perform structured processing on production plan time series data based on energy emission correlation data. It converts production task identifiers, scheduled execution segments, and resource requirements into a unified format, and matches the structured production plan time series data with energy emission correlation data segment by segment along a unified time axis to generate task constraint data. The conflict identification module is used to compare energy consumption demand with available load in energy segments based on task constraint data, and to compare emission expectations with available emission quotas in emission segments. It identifies resource conflict segments through segment comparison and generates conflict classification data based on constraint type. Adjust the data generation module to generate collaborative adjustment data based on conflict classification data, including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The adjustment instruction generation module is used to determine the applicable adjustment method based on conflict classification data and generate planned adjustment instructions based on collaborative adjustment data. The planning update module is used to send planning adjustment instructions to the production planning subsystem and receive the adjusted production plan time series data to support continuous collaborative management and control of energy, emissions and production plans.

[0013] The above-described solution of the present invention has at least the following beneficial effects: First, by unifying the time granularity and calibrating the timestamps of energy time-series data, emission time-series data, and production planning time-series data, data from different business systems can be expressed under the same time benchmark. This fundamentally solves the problem in existing technologies where energy records are stored in a scattered manner on a daily and device-by-device basis, and are inconsistent with the aggregation cycle used by the planning system. By establishing a unified timeline, the task execution process, energy change process, and emission change process can be directly compared, laying the foundation for conducting cross-system time-series analysis.

[0014] Secondly, by segmenting energy time-series data and emission time-series data according to dimensions such as equipment identification, process identification, and emission source identification, and establishing a mapping relationship between energy consumption segments and emission segments through time correspondence, the static emission coefficients and fixed energy consumption models provided by traditional systems can be transformed into segmented data with dynamic changing characteristics. This invention, through this method, enables the correlation between energy consumption and emissions to have continuity and contextual relationships, overcoming the shortcomings of existing technologies that cannot reflect energy consumption trends in the processing process and differences in equipment emissions.

[0015] Furthermore, by matching the structured production plan time-series data with energy emission correlation data segment by segment, the energy consumption and emission impact required for each task during execution can be derived based on time alignment. This invention can identify changes in the pressure on the energy and emission systems caused by task execution in advance during the scheduling generation stage, rather than discovering conflicts only during the execution stage as in existing technologies, thereby improving the responsiveness of scheduling simulation to resource conditions.

[0016] Furthermore, by comparing task constraint data with available load in energy zones and available emission allowances in emission zones at the zone level, potential resource conflicts can be accurately identified under conditions of energy constraints or insufficient emission permits. This invention categorizes conflicts into energy-side, emission-side, and compound conflicts, enabling different types of conflicts to be addressed with different strategies, rather than relying on existing technologies that merely indicate abnormal results without distinguishing the underlying causes.

[0017] Subsequently, this invention can generate alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes based on conflict classification data. This allows scheduling adjustments to no longer rely on manual experience, but rather on a selection of multiple available strategies provided by the system. This not only enhances the feasibility of adjustment operations but also improves the coordination between production plans and energy constraints.

[0018] Finally, by feeding back the plan adjustment instructions to the production planning subsystem and recalculating energy emission correlations based on the adjusted production plan time series data, a closed-loop collaborative management process around the time axis can be formed. For example, in a large manufacturing enterprise, when the system identifies that a high-energy-consuming process was originally scheduled for the morning peak load period, this invention can automatically generate an adjustment instruction to split the process into two sub-segments and move them to lower load periods. After the production planning subsystem updates the task arrangement based on this instruction, this invention will again perform correlation analysis based on the new schedule, thereby achieving continuous linkage between energy changes, emission limits, and production scheduling. This invention enables the scheduling generation stage to avoid energy and emission conflicts in advance, reduces temporary adjustments during the execution stage, and improves the coordination and consistency of the entire production operation process. Attached Figure Description

[0019] Figure 1 This is a flowchart of the online monitoring and collaborative management method for energy and carbon emissions provided in the embodiments of the present invention. Detailed Implementation

[0020] 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.

[0021] like Figure 1 As shown, embodiments of the present invention propose a method for online monitoring and coordinated management of energy and carbon emissions, the method comprising: Acquire energy time series data, emission time series data, and production plan time series data, and perform time granularity unification and timestamp calibration to generate unified time axis data; Based on the unified timeline data, the energy time series data is segmented according to equipment identifier, process identifier, and load segment, and the emission time series data is segmented according to emission source identifier and emission segment. The mapping relationship between energy consumption segments and emission segments is established through the segment correspondence method to generate energy emission correlation data. Based on energy emission correlation data, the production plan time series data is structured, and the production task identifier, scheduled execution segment and resource demand are converted into a unified format. The structured production plan time series data is then matched with the energy emission correlation data segment by segment along a unified time axis to generate task constraint data. Based on task constraint data, energy consumption demand is compared with available load in energy segments, and emission expectations are compared with available emission allowances in emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated according to the type of constraint. Based on the conflict classification data, generate collaborative adjustment data including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The applicable adjustment methods are determined based on the conflict classification data, and the plan adjustment instructions are generated based on the coordinated adjustment data; The system sends adjustment instructions to the production planning subsystem and receives the adjusted production plan time series data to support continuous coordinated management of energy, emissions, and production planning.

[0022] In this embodiment of the invention, by performing time granularity unification and timestamp calibration on energy time series data, emission time series data, and production plan time series data, data from different sources are given a consistent time reference. This facilitates the formation of a unified time axis suitable for cross-system processing, thus providing a foundation for subsequent correlation calculations. By establishing a unified time axis, different types of data become comparable and can be processed continuously on the same time series, avoiding data misalignment problems caused by inconsistent time references in traditional energy management systems.

[0023] By segmenting energy and emission time-series data according to different identifiers and time periods, and then performing corresponding analysis on the two types of time periods after time alignment, a data structure reflecting the relationship between changes in energy consumption and emissions can be generated. This correlated data can display the emission changes corresponding to energy consumption behavior within a certain time period, providing a basis for subsequent production planning. Through this correlation method based on segment correspondence, the dynamic linkage between energy and emissions can be reflected on a unified time axis.

[0024] By structuring production task-related data and matching it segment by segment with energy emission-related data on a unified timeline, constraint data describing the pressure exerted by task execution on the energy and emission systems can be generated. This constraint data can characterize the superimposed energy consumption and emission demands during task execution, thereby establishing an accurate mapping relationship between production plans and energy emission status. This mapping method helps to predict resource consumption during task execution before plan adjustments, avoiding the discovery of resource conflicts only during the plan execution phase.

[0025] By comparing task constraint data with available load in energy zones and available emission allowances in emission zones, potential resource conflicts during task execution can be identified and categorized based on their sources. After classifying and processing these conflicts, different adjustment methods can be employed for different types of constraints, avoiding treating all conflicts as the same type of problem and improving the targeting of subsequent adjustment strategies.

[0026] By generating collaborative adjustment data based on conflict classification data to adjust task execution methods, various task adjustment strategies can be provided, including adjusting task execution segments, splitting tasks, selecting alternative equipment, or adjusting the load ratio between tasks. By setting different adjustment paths, different conflict situations can be flexibly addressed, reducing the impact on the established production rhythm.

[0027] By generating plan adjustment instructions based on feasible adjustment methods and sending the adjusted instructions to the production planning subsystem, a closed-loop management model can be formed between energy status, emission status, and production task scheduling. This model enables timely adjustments to the plan when constraints arise on the energy or emission side, and also allows task execution results to continue participating in the next round of collaborative analysis through feedback, improving the continuity and coordination of the management process.

[0028] In a preferred embodiment of the present invention, the applicable adjustment method is determined based on conflict classification data, and a plan adjustment instruction is generated based on collaborative adjustment data, specifically including: First, read the conflict type information from the conflict classification data, including energy-side conflicts, emission-side conflicts, or complex conflicts, and determine the task segments that need to be adjusted based on the start time, duration, and degree of conflict. Then, determine the applicable adjustment method according to the type of conflict. For example, when there is a conflict on the energy side, prioritize the alternative execution section or load redistribution; when there is a conflict on the emission side, prioritize the emission alternative section or equipment switching; and when there is a complex conflict, prioritize the section splitting method. Subsequently, the adjustment content corresponding to the selected adjustment method is read from the collaborative adjustment data, including alternative execution segments, the splitting position of task sub-segments, the list of switchable devices, or the adjustable load ratio; Next, based on the execution order of tasks in the original plan, the planned time window, and resource usage, the adjustments are compared with the current production task list to form adjustment parameters that can directly modify the production plan. Finally, the adjustment parameters are encapsulated into structured plan adjustment instructions, enabling the production planning subsystem to modify task execution segments, execution order, or execution equipment accordingly, thereby completing the plan adjustment preparation work.

[0029] In a preferred embodiment of the present invention, the plan adjustment instruction is sent to the production planning subsystem, and the adjusted production plan time series data is received, specifically including: First, the plan adjustment instructions are converted into a data format recognizable by the production planning subsystem, so that the adjustment information, including execution segments, task sequence, equipment selection and load allocation, is parseable; Subsequently, the plan adjustment instruction is sent to the production planning subsystem through a preset communication interface (such as an internal message queue or data interaction interface); After receiving the adjustment instruction, the production planning subsystem updates the relevant tasks in the original plan, including modifying the execution time, updating task dependencies, replacing execution equipment, or adjusting the task load. After the update is completed, the production planning subsystem generates new production plan time series data and returns it to the processing unit in this method; Finally, this method determines whether energy emission correlation data, task constraint data, or conflict classification data need to be reprocessed based on the adjusted production plan time series data, thereby forming a continuous collaborative management and control capability.

[0030] In a preferred embodiment of the present invention, based on unified timeline data, energy time series data is segmented according to equipment identifier, process identifier, and load segment; emission time series data is segmented according to emission source identifier and emission segment; and a mapping relationship between energy consumption segments and emission segments is established through segmentation correspondence to generate energy emission correlation data, including: The energy time series data is analyzed point by point, and the analysis results are divided into segments according to equipment identifiers, process identifiers, and upper and lower load limits to generate basic energy consumption segment data. The emission time series data is analyzed point by point, and the analysis results are divided into segments according to the emission source identification and emission change trend to generate emission basic segment data. Based on the unified time axis data, the energy consumption baseline data and emission baseline data are time-aligned to generate time-aligned data segments. Based on the time-aligned segment data, the correspondence between the energy consumption basic segment data and the emission basic segment data in terms of time coverage and identification matching is compared, and segments that meet the matching conditions are combined to generate mapped segment data. Based on the mapped segment data, output energy emission correlation data as input for subsequent generation of task constraint data.

[0031] In this embodiment of the invention, by analyzing energy time-series data and emission time-series data point by point, the originally continuous but unstructured monitoring curves can be transformed into basic segments with boundary information and characteristic attributes. After further alignment processing under a unified time axis, these basic segments form time-aligned segments reflecting the comparable temporal relationship between the two types of data. By comparing the time coverage between the energy consumption and emission basic segments within the time-aligned segments, the time periods in which the two types of segments coexist during actual operation can be identified, thus forming a time-matching basis for subsequent correlation analysis. Furthermore, by performing matching degree analysis on equipment identifiers, process identifiers, and emission source identifiers within the time-matched segments, segments with correlation in actual production operations can be selected. The mapped segment data obtained after selection reflects the correspondence between energy consumption changes and emission changes, providing a basic input for constructing energy emission correlation data, enabling subsequent steps to utilize this correlation to generate task constraints and identify conflicts.

[0032] In a preferred embodiment of the present invention, the energy time series data is analyzed point-by-point, and the analysis results are segmented according to equipment identifiers, process identifiers, and upper and lower load limits. Specifically, this includes: First, the energy time series data is read in chronological order, and the energy consumption value, equipment identifier, and process identifier corresponding to each time point are recorded. Subsequently, the energy consumption values ​​at consecutive time points are compared. When the energy consumption values ​​appear consecutively within a certain range and belong to the same equipment and process, these consecutive time points are summarized into a candidate energy consumption segment. Within the candidate energy consumption zone, the lowest and highest load values ​​are identified so that the zone can be further divided according to preset load ranges (e.g., low load zone, medium load zone, high load zone). After the division is completed, the start time, end time, equipment identifier, process identifier and load level are recorded for each energy consumption basic segment so that the segment can be used for subsequent matching processing. By using the above segmentation method, the originally continuous monitoring data is split into basic energy consumption segments with boundaries and semantic attributes, which facilitates cross-system correlation processing.

[0033] In a preferred embodiment of the present invention, the emission time series data is analyzed point-by-point, and the analysis results are segmented according to the emission source identification and emission change trend. Specifically, this includes: First, the emission time series data is read in chronological order, and the emission value and emission source identifier are obtained at each time point; Subsequently, by comparing the emission value change trends at adjacent time points, time periods in which emission values ​​showed continuous increases, continuous decreases, or remained stable were divided into separate emission candidate segments. Next, based on the emission source identification, it is determined whether the candidate segments come from the same emission source. If they belong to the same emission source, they are retained as a single emission segment. If there is a change in the source, they are split. Furthermore, for each emission baseline segment, the emission level range is calculated, including the minimum and maximum emissions that have occurred in that segment, to form emission characteristics that can be used for subsequent matching conditions; The final generated emission baseline data has time boundaries, emission trends, and emission source attributes, enabling it to be compared with the energy consumption baseline data.

[0034] In a preferred embodiment of the present invention, time alignment processing is performed on the energy consumption baseline data and the emission baseline data based on unified time axis data, specifically including: First, based on the smallest time granularity of the unified time axis data, the start and end times of the energy consumption base segment and the emission base segment are aligned to a unified time scale. Subsequently, the energy consumption baseline data and emission baseline data belonging to the same time scale range were classified separately, so that the two segments could form a comparable correspondence in time. If there is a slight offset between the start and end times of the energy consumption baseline section and the emission baseline section, the section boundaries will be aligned with the nearest time scale on a unified time axis according to the degree of time overlap between the sections, so that they are consistent with the unified time axis. The aligned segments are combined in order along a unified time axis to form time-aligned segment data. Each time-aligned segment contains energy consumption characteristics, emission characteristics, and their corresponding time range. Time-aligned segment data enables the generation of subsequent mapping segments to be compared under a strictly consistent time benchmark, improving the accuracy of correlation analysis.

[0035] In a preferred embodiment of the present invention, based on energy emission correlation data, the production plan time series data is structured, converting production task identifiers, predetermined execution segments, and resource requirements into a unified format. The structured production plan time series data is then matched segment by segment with the energy emission correlation data along a unified time axis to generate task constraint data, including: Extract production task identifiers, scheduled execution times, and resource requirements from production plan time series data, and transform them into task structured data in a unified format; Time mapping processing is performed based on the time segments of energy emission correlation data and task structured data to generate task time mapping data; Based on the task time mapping data, the energy consumption demand in the task structured data is correlated with the energy consumption range in the energy emission correlation data segment by segment to generate energy consumption constraint data. Based on the task time mapping data, the emission impact in the task structured data is correlated with the emission magnitude in the energy emission correlation data segment by segment to generate emission constraint data; Constraint synthesis processing is performed based on energy consumption constraint data and emission constraint data to generate task constraint data.

[0036] In this embodiment of the invention, by extracting production task identifiers, scheduled execution times, and resource requirements from production plan time-series data, the original business data oriented towards planning and scheduling can be transformed into structured task data, facilitating its integration with energy emission-related data. After time mapping processing, the structured task data allows task execution segments to be located along a unified time axis, enabling a temporal correlation between task execution behavior and energy and emission status. By utilizing the task time-mapping data and comparing the energy consumption demand in the structured task data with the energy consumption amplitude in the energy emission-related data, energy consumption constraint data for analyzing the energy consumption pressure of task execution can be generated. Simultaneously, by calculating the emission impact and emission amplitude, emission constraint data describing emission pressure can be obtained. Further synthesis of the energy consumption constraint data and emission constraint data forms task constraint data representing the dual impact of energy and emission sides during task execution, facilitating resource conflict identification and plan adjustment in subsequent processing.

[0037] In a preferred embodiment of the present invention, the process of extracting production task identifiers, scheduled execution times, and resource requirements from production plan time series data, and converting them into structured task data in a unified format, specifically includes: First, the production plan time series data is scanned in chronological order to identify the task number, the product name or process type corresponding to each task, as task identification information. Subsequently, the estimated start time and estimated end time of the task are extracted from the same production plan record, and these two time values ​​are recorded as the scheduled execution time interval of the task. Next, the energy consumption, equipment usage, or other relevant resource occupancy information required for the task is read from the production plan data, and this information is parsed into resource requirement data for the task. Then, in order to enable the task data to be used together with energy emission related data, the task identifier, the scheduled execution interval and the resource requirements are integrated according to a unified data structure so that the task can be expressed in a unified format. The resulting structured task data possesses identifiable task identifier attributes, locatable execution time attributes, and calculable resource requirement attributes, enabling it to serve as input data for subsequent task mapping processing.

[0038] In a preferred embodiment of the present invention, time mapping processing is performed based on the time segments of energy emission correlation data and task structured data to generate task time mapping data, specifically including: First, the pre-defined execution segments in the task structured data are expanded according to the smallest time granularity of a unified time axis, so that the task data can be arranged in continuous time units on a unified time axis. Subsequently, the time overlap detection was performed on the time segments corresponding to the expanded task execution time units and the energy emission associated data, and the overlapping parts were marked as "effective mapping time periods". For non-overlapping time periods, these are recorded as the time periods when the task failed to correspond to energy emission data, so that the resource usage of the task can be supplemented and judged later. Next, the task identifier, energy consumption change range, and emission change range within each effective mapping time period are combined to form the time mapping unit corresponding to the task. Finally, all time mapping units are sorted and merged according to the time sequence of task execution to form task time mapping data, so that a stable correspondence is established between the task execution process and the energy emission status.

[0039] In a preferred embodiment of the present invention, based on task time mapping data, the energy consumption demand in the task structured data is correlated with the energy consumption amplitude in the energy emission correlation data segment by segment to generate energy consumption constraint data, specifically including: First, read the energy consumption requirement of the task in the time period segment by segment from the task time mapping data, and at the same time read the energy consumption amplitude in the energy emission correlation data within the mapping time period. Subsequently, by comparing the energy demand with the energy consumption range, it was determined whether the task would put additional pressure on the energy system when it was executed in this section. If the energy demand of a task exceeds the energy consumption range, the segment is recorded as an energy-scarce segment, and the excess value is recorded as energy pressure data. If the energy consumption requirement of a task is less than or equal to the energy consumption range, then the segment is recorded as the energy-capable segment. Based on the energy consumption pressure data and carrying capacity data of all sections, energy consumption constraint data is generated and used as structured constraint information to represent the energy consumption pressure during task execution.

[0040] In a preferred embodiment of the present invention, emission constraint data is generated by performing segment-by-segment correlation calculations between the emission impact in the task structured data and the emission magnitude in the energy emission correlation data based on the task time mapping data, specifically including: First, the emission impact of the task in the corresponding time period is obtained segment by segment from the task time mapping data, and the emission magnitude in the energy emission correlation data of the time period is read at the same time. Subsequently, the emission impact was compared with the emission magnitude to determine whether the execution of the task would put pressure on the emission permit conditions of the emission system. When the emission impact exceeds the emission limit, the section is recorded as an emission-restricted section, and the portion exceeding the emission limit is calculated as emission pressure data; When the emission impact is not higher than the emission range, the section is recorded as an emission-bearing section. Ultimately, by aggregating emission-restricted zones and emission-capable zones, a data structure that can be used for conflict identification is formed, namely emission constraint data.

[0041] In a preferred embodiment of the present invention, constraint synthesis processing is performed based on energy consumption constraint data and emission constraint data to generate task constraint data, specifically including: First, the energy consumption constraint data and emission constraint data are compared in corresponding segments according to a unified time axis, and the energy consumption pressure data and emission pressure data within the same execution segment are read out. Subsequently, by comparing the magnitude of energy consumption pressure and emission pressure, it was determined whether the section had pressure on the energy side, emission side, or both sides. If a section contains both energy consumption pressure and emission pressure, then the section is marked as a composite pressure section. If a certain section only contains energy consumption pressure, it is marked as an energy-side pressure section; If a section contains only emission pressure, it is marked as an emission-side pressure section; Finally, all pressure segments are merged in chronological order to form task constraint data that can be directly used for resource conflict identification.

[0042] In a preferred embodiment of the present invention, based on task constraint data, energy consumption demand is compared with the available load of energy segments, and emission expectations are compared with the available emission quotas of emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated based on constraint types, including: Energy consumption constraint data is extracted segment by segment based on task constraint data, and the difference is compared with the available load of energy segment to generate energy consumption difference data. Identify sections where the energy consumption difference is below the safety margin based on the energy consumption difference data, and record them as energy-side conflict data. Based on the task constraint data, emission constraint data is extracted segment by segment, and the difference is compared with the available emission credits of the emission segment to generate emission difference data. Identify segments where the emission difference is below the emission threshold based on the emission difference data, and record them as emission-side conflict data. Conflict fusion processing is performed based on energy-side conflict data and emission-side conflict data. Data with both types of conflict are comprehensively judged to generate composite conflict data. Energy-side conflict data, emissions-side conflict data, and composite conflict data are merged to generate conflict classification data.

[0043] In this embodiment of the invention, by extracting energy consumption constraint information segment by segment from task constraint data and comparing the difference with the available load of energy segments, periods in which task execution may cause insufficient load in the energy system can be identified, thereby generating energy consumption difference data. Segments in the energy consumption difference data that are below the safety margin are marked as energy-side conflict segments, enabling potential energy supply shortages to be detected before planned adjustments. Similarly, by extracting emission constraint information and comparing it with the available emission allowances of emission segments, segments with insufficient emission permit conditions can be identified, thereby generating emission difference data and marking the corresponding emission-side conflict segments. For segments that simultaneously exhibit energy-side and emission-side conflicts, they can be identified as composite conflict segments through fusion processing. The final conflict classification data clearly shows the conflict type, allowing subsequent adjustment strategies to adopt different approaches based on different conflict sources, improving the matching degree between the adjustment plan and actual conditions.

[0044] In a preferred embodiment of the present invention, energy consumption constraint data is extracted segment by segment based on task constraint data, and the difference is compared with the available load of the energy segment to generate energy consumption difference data, specifically including: First, the energy consumption pressure value and energy consumption demand corresponding to each execution segment are read one by one from the task constraint data, and the two are combined as the energy consumption constraint data of that segment. Subsequently, the available load value that is consistent with the time range of the execution segment is extracted from the available load data of the energy segment, and it is compared with the energy consumption constraint data item by item. During the comparison, the available load value is subtracted from the energy consumption demand to determine the remaining resources; in this process, the existence of reserves is recorded only in words, without involving mathematical expressions. If the available load is greater than the energy demand, the result is marked as "sufficient load" for that section, and the corresponding remaining amount is recorded. If the available load is not higher than the energy demand, it is marked as "insufficient load" and the insufficient part is recorded as the energy consumption difference information of that section. Finally, the energy consumption difference information of all sections is sorted in chronological order to form energy consumption difference data that can be used for subsequent conflict identification.

[0045] In a preferred embodiment of the present invention, identifying sections where the energy consumption difference is lower than the safety margin based on energy consumption difference data and recording them as energy-side conflict data specifically includes: First, set an acceptable range for each energy consumption difference record to determine whether an energy-side conflict exists. Subsequently, the energy consumption difference data was read one by one, and each data point was compared with the preset safety margin range. When the remaining load represented by the energy consumption difference is continuously lower than the safety margin range, the section is marked as an energy-side conflict section. If the remaining load represented by the energy consumption difference is greater than the safety margin, then the section is marked as an energy-side achievable section. During the judgment process, situations where multiple consecutive sections are in a state of underload are recorded as a whole energy-side conflict section to avoid excessively fragmented conflict results. Finally, all segments identified as energy-side conflicts are organized to form energy-side conflict data, which serves as input for subsequent conflict fusion processing.

[0046] In a preferred embodiment of the present invention, emission constraint data is extracted segment by segment based on task constraint data, and the difference is compared with the available emission allowance for each emission segment to generate emission difference data, specifically including: First, the emission impact and emission pressure information corresponding to each execution segment are read from the task constraint data, and these two parts are used as the basic data for segment emission constraints. Subsequently, the emission permit value corresponding to the execution segment is extracted from the available emission credit data for the emission segment; Compare the emission permit values ​​with the emission impact values, and determine in text form whether the emission permit values ​​are insufficient to support the execution of the task; When the emission permit value is greater than the emission impact, the section is marked as an emission permit sufficient section. When the emission permit value is not higher than the emission impact, the section is marked as an emission-restricted section, and the emission difference is recorded as the basis for subsequent conflict judgment. Finally, the emission difference information and restricted status of all sections are organized in chronological order to form emission difference data.

[0047] In a preferred embodiment of the present invention, identifying segments where the emission difference is lower than the emission threshold based on emission difference data and recording them as emission-side conflict data specifically includes: First, set an emission safety threshold for the emission difference to determine whether the emission permit meets the standards; Subsequently, the emission difference data was analyzed segment by segment to identify segments with insufficient emission permits; If the emission difference indicates that the available emissions are continuously below the emission threshold, then the section is recorded as an emission-side conflict section. If the emission difference indicates that there is still a surplus within the emission permit range, then the section is recorded as an emission-side tolerable section; Multiple consecutive conflict zones on the emission side are merged and treated as a single conflict zone in subsequent fusion judgments. The processed emission-side conflict data will be used as input for subsequent complex conflict assessment.

[0048] In a preferred embodiment of the present invention, conflict fusion processing is performed based on energy-side conflict data and emission-side conflict data. A comprehensive judgment is made on data with both types of conflict simultaneously to generate composite conflict data. Specifically, this includes: First, align the energy-side conflict data and the emissions-side conflict data in chronological order to make the two types of conflict data comparable at the segment level; Subsequently, for each segment, it is determined whether it is marked as conflict in both energy-side conflict data and emissions-side conflict data. When a certain time period is displayed as a restricted state in both types of conflict data, the segment is marked as a composite conflict segment. If a conflict exists only on the energy side and not on the emissions side during a certain period, it will remain a separate conflict segment on the energy side; if a conflict exists only on the emissions side, it will remain a separate conflict segment on the emissions side. For complex conflict sections, the sources of conflict and the nature of overlap are further recorded so that the subsequent adjustment process can select the most appropriate adjustment method according to the degree of complex conflict. This results in composite conflict data, which is used to generate the final conflict classification data.

[0049] In a preferred embodiment of the present invention, energy-side conflict data, emission-side conflict data, and composite conflict data are merged to generate conflict classification data, specifically including: First, sort the three types of conflict data in chronological order and align the time ranges of the conflict segments for each category. Subsequently, time periods belonging to the compound conflict zone are marked as compound conflict type to avoid duplicate recording; For sections not marked as complex conflicts, check whether they belong to energy-side conflicts or emission-side conflicts, and mark the two types of individual conflicts as energy-side conflict type or emission-side conflict type respectively; Merge consecutive conflicting sections of the same type to make the conflicting sections appear continuous, which will facilitate subsequent processing by the adjustment module. Finally, the merged conflict types, conflict time ranges, and corresponding conflict degrees are integrated into structured conflict classification data and provided for use in subsequent adjustment steps.

[0050] In a preferred embodiment of the present invention, collaborative adjustment data, including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes, is generated based on conflict classification data, including: Based on the conflict classification data, identify energy-side conflict zones and generate candidate alternative execution zone data based on the time periods with higher available load within the energy zones; Based on the conflict classification data, emission-side conflict zones are identified, and emission substitution implementation zone data are generated based on the time periods with more lenient emission constraints. Based on the conflict classification data, identify the composite conflict segments, and divide the execution segments into multiple sub-segments according to the continuity rules based on the task's separable attributes, generating segment splitting data; Based on the task-equipment correspondence in the conflict classification data, alternative equipment that can reduce energy consumption or reduce emissions impact is selected, and equipment switching data is generated. Based on the load differences and capacity relationships between tasks, load adjustment processing is performed on the energy consumption ratio of adjacent tasks to generate load redistribution data; The candidate alternative implementation segment data, emission alternative implementation segment data, segment split data, equipment switching data, and load redistribution data are merged to generate coordinated adjustment data.

[0051] In this embodiment of the invention, by generating alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes based on conflict classification data, different types of resource conflicts can be decomposed into manageable operational objects. For energy-side conflicts, by identifying time segments with higher available load, new execution locations can be provided for tasks, alleviating pressure during periods of resource scarcity. For emission-side conflicts, by selecting segments with more lenient emission constraints, task execution can be completed without exceeding emission permits. For complex conflicts, by splitting the task execution segment into multiple shorter sub-segments, task execution can be flexibly distributed within segments where energy or emission conditions change, reducing concentrated pressure on the system. Furthermore, by selecting alternative equipment based on the correspondence between tasks and equipment, tasks can be executed on more suitable equipment, thereby reducing overall load or emissions. Load redistribution can adjust the energy consumption ratio between adjacent tasks, resulting in a smoother distribution of task load. The generation of these various adjustment methods provides more feasible execution schemes for tasks under resource constraints, improving scheduling flexibility.

[0052] In a preferred embodiment of the present invention, energy-side conflict zones are identified based on conflict classification data, and candidate alternative execution zone data are generated based on time periods with higher available load within the energy zones. Specifically, this includes: First, read the time segments marked as energy-side conflicts from the conflict classification data, and record the start time, end time, and degree of conflict of each segment; Subsequently, other time segments that differ from the energy-side conflict segments are retrieved from the available load data of the energy segments, and the available load levels of these segments are read. Based on the available load level, time periods with available load higher than a set reference load value are marked as potential periods suitable for carrying out tasks; These potential segments are sorted according to their start and end times, continuity, and available load, and time segments that meet the task execution duration requirements are selected as candidate alternative execution segments. Finally, the data of candidate alternative execution segments is stored in a structured manner, including their start and end times, available load, and potential suitability of tasks in that segment, providing input for subsequent selection of adjustment methods.

[0053] In a preferred embodiment of the present invention, emission-side conflict zones are identified based on conflict classification data, and emission substitution execution zone data is generated based on time periods with more lenient emission constraints. Specifically, this includes: First, the time periods belonging to emission-side conflicts are read from the conflict classification data, and the emission restriction information of the period is obtained, including emission pressure values ​​and emission permit status. Subsequently, other time segments that are different from the conflicting emission segments are selected from the available emission credit data of the emission segments, and the emission permit values ​​of these segments are read. Based on the emission permit values, time periods where the permit values ​​are higher than the emission impact required for mission execution are marked as periods with relatively lenient emission conditions; These sections with lenient conditions are screened based on emission permit values, continuity, and mission duration to select emission alternative execution sections that can be used to carry out the mission; Finally, the selected emission substitution implementation sections are recorded as emission substitution implementation section data, including the emission permit value, the duration of implementation, and the reasons for adaptation for the section, providing a basis for subsequent decision-making.

[0054] In a preferred embodiment of the present invention, composite conflict segments are identified based on conflict classification data, and the execution segments are divided into multiple sub-segments according to continuity rules based on the separability attributes of the task, generating segment splitting data, specifically including: First, the segments marked as complex conflicts are read from the conflict classification data, and it is confirmed that the segments have resource constraints on both the energy and emission sides. Subsequently, the task's splittable attributes are read from the task's structured data, including whether the task can be split, whether splitting will affect subsequent processes, and the minimum splittable execution time. Assuming the task can be broken down, the complex conflict zone is divided into multiple continuous but shorter sub-segments according to the time granularity of a unified time axis. For each sub-section, check its pressure status on the energy side and emission side, and mark the sub-section with lower pressure as the executable sub-section, and mark the sub-section with higher pressure as the sub-section to be adjusted. Finally, the start and end times, classification results, and reasons for splitting all sub-segments are combined to form segment splitting data for subsequent adjustments.

[0055] In a preferred embodiment of the present invention, based on the task-equipment correspondence in the conflict classification data, alternative equipment that can reduce energy consumption or reduce emissions is selected, and equipment switching data is generated, specifically including: First, obtain the equipment identification of the task within the conflict zone, as well as the energy consumption and emission characteristics of the equipment, from the conflict classification data; Subsequently, candidate devices with the same or compatible functions as the device are retrieved from the device library, and the energy consumption level, emission characteristics and operating capacity information of these candidate devices are read. The energy consumption characteristics of candidate equipment are compared with the energy consumption pressure of the original equipment in the conflict zone to select equipment that can reduce energy consumption requirements. At the same time, the emission characteristics of the candidate equipment are compared with the emission pressure of the original equipment in the same section to screen out equipment that can reduce the emission impact. If certain candidate devices can simultaneously reduce energy consumption pressure and emission pressure, they are marked as preferred alternative devices; Finally, the screening results are combined to generate device switching data, including device identification, execution capabilities, and adaptation reasons, providing input for the subsequent generation of collaborative adjustment data.

[0056] In a preferred embodiment of the present invention, based on time-aligned segment data, the correspondence between energy consumption baseline segment data and emission baseline segment data in terms of time coverage and identifier matching degree is compared, and segments that meet the matching conditions are combined to generate mapped segment data, including: Extract the start and end times of the energy consumption basic segment data, as well as the corresponding equipment and process identifiers, from the time-aligned segment data to generate energy consumption segment feature data. Based on the time-aligned segment data, the start and end times of the emission base segment data and the corresponding emission source identifiers are extracted to generate emission segment feature data. Based on the comparison of the time overlap duration between the energy consumption segment characteristic data and the emission segment characteristic data, coverage determination data representing the time coverage relationship is generated. Based on the coverage determination data, segments whose time overlap duration reaches the preset coverage conditions are formed into time-matching segment data. Based on the time-matching segment data, the correlation between equipment identifiers, process identifiers and emission source identifiers is compared to generate identifier matching judgment data; Based on the identifier matching judgment data, segments that meet the matching conditions are combined to form mapped segment data.

[0057] In this embodiment of the invention, by comparing the time coverage and identifier matching degree of the energy consumption base segment and the emission base segment based on time-aligned segment data, two originally independent segments can be combined into a logically related mapping segment. The time coverage determination ensures that the two segments have overlapping time periods in actual operation, enabling the generated mapping segment to reflect the true relationship between energy consumption and emission changes. The identifier matching degree comparison further determines whether there is a business relationship between equipment identifiers, process identifiers, and emission source identifiers, ensuring that the mapping relationship is not only based on time overlap but also has business logical meaning. Through the dual screening of time overlap data and identifier matching data, the final mapping segment can more accurately reflect the impact of energy consumption changes on emission changes. As an important component of energy emission correlation data, this mapping segment can provide reliable input for task constraint generation, enabling the impact of task execution on the energy system and emission system to be judged based on real data.

[0058] In a preferred embodiment of the present invention, energy consumption segment feature data is generated by extracting the start time, end time, and corresponding equipment and process identifiers from the time-aligned segment data, specifically including: First, locate the set of segments containing the basic energy consumption segment from the time-aligned segment data, and read the start and end times of each segment one by one; Subsequently, the device identification information recorded in each segment is extracted, such as the device number and device type corresponding to the energy consumption record. Next, extract process identification information from the same section, including the name or number of the production process corresponding to that energy consumption section; Then, the start time, end time, equipment identifier, and process identifier are merged into a complete energy consumption segment characteristic record; Finally, the feature records of all segments are organized in chronological or device order to form energy consumption segment feature data for subsequent matching processing.

[0059] In a preferred embodiment of the present invention, emission segment feature data is generated by extracting the start time, end time, and corresponding emission source identifier of the emission base segment data based on the time-aligned segment data, specifically including: First, the segment containing the emission baseline segment is read from the time-aligned segment data, and the start and end times of the segment are extracted so that the emission change range can be accurately located. Subsequently, emission source identification information is extracted from this section, including the name of the emission source unit, equipment number, or emission process category; Next, the start time, end time, and emission source identifier of the segment are combined into an emission segment characteristic record; Then, all extracted feature records are classified and organized to make it easier to compare segments from the same emission source in subsequent processing. Finally, the compiled emission segment characteristic records will be used as emission segment characteristic data for subsequent time coverage comparison and identification matching analysis.

[0060] In a preferred embodiment of the present invention, coverage determination data representing the time coverage relationship is generated by comparing the time overlap duration of energy consumption segment characteristic data and emission segment characteristic data, specifically including: First, the energy consumption segment characteristic data and emission segment characteristic data are arranged in chronological order to make the two lists comparable in the time dimension. Subsequently, each energy consumption segment and emission segment was compared pairwise to determine whether there was an overlap between their start and end times. When an overlap is found between two time ranges, the duration of the overlap is calculated based on the start and end times of the overlap. This calculation is achieved by judging the degree of overlap between the two time ranges and does not require the use of mathematical formulas. Next, the overlap duration is compared with the preset time coverage requirement (such as needing to reach a certain duration). If the condition is met, the segment is recorded as the segment with "time coverage satisfied". If the overlap duration is insufficient, the segment is recorded as "insufficient time coverage" for subsequent filtering. Finally, all coverage data is summarized in segment order to form coverage determination data.

[0061] In a preferred embodiment of the present invention, based on coverage determination data, segments whose time overlap duration reaches a preset coverage condition are formed into time-matching segment data, specifically including: First, based on the coverage determination data, all segments marked as "time coverage satisfied" are selected; Subsequently, the start time, end time, corresponding energy consumption segment characteristics, and emission segment characteristics of these segments are combined to ensure that each time-matching segment can reflect the common coverage of the energy consumption segment and the emission segment. Next, the time matching segments are adjusted according to the granularity of a unified time axis so that all segments can maintain the same time scale as other segments. Then, check if adjacent time-matching segments are continuous. If they can be merged into a longer continuous segment, then merge them as a single time-matching record. Finally, the processed time matching segments are arranged in order to form time matching segment data, which provides input for the next step of identifier matching.

[0062] In a preferred embodiment of the present invention, identifier matching judgment data is generated by comparing the correlation between equipment identifiers, process identifiers, and emission source identifiers based on time matching segment data, specifically including: First, extract the equipment identifier, process identifier, and emission source identifier for each record from the time-matching segment data; Subsequently, these identifiers are compared logically at the business level, such as determining whether the equipment belongs to the equipment sequence that generates the emission source, and whether the process is usually accompanied by the emission activity. Then, based on the comparison results, the degree of correlation is divided into levels such as "strong correlation", "weak correlation" or "no correlation", and the comparison level of each segment is recorded. Segments with a high degree of correlation are marked as "identifier match satisfied", while segments with a low degree of correlation are marked as "identifier match not satisfied". Finally, the association judgment results of all segments are combined in chronological order to form identifier matching judgment data, which serves as the basis for generating mapping segment data in the next step.

[0063] In a preferred embodiment of the present invention, based on identifier matching determination data, segments whose correlation level meets the matching condition are combined to form mapped segment data, specifically including: First, filter out all segments marked as "identity match satisfied" from the identifier matching judgment data; Subsequently, the energy consumption characteristics and emission characteristics of these sections are combined into a mapping record with a clear correspondence; Next, consecutive matching segments are merged to allow the correspondence to cover a longer time range, thereby improving the continuity of subsequent association analysis. Then, these mapping segments are arranged in chronological order to give the overall mapping a stable temporal structure; Finally, all mapped segments are output as mapped segment data, providing input for generating energy emission correlation data.

[0064] In a preferred embodiment of the present invention, load adjustment processing is performed on the energy consumption ratio of adjacent tasks based on the load difference and capacity relationship between tasks, generating load redistribution data, including: Based on the energy consumption requirements of adjacent tasks and the execution segment, task load characteristic data is extracted to generate initial load data for load comparison. Based on the initial load data, compare the load differences between adjacent tasks and mark the task segments whose load differences reach the adjustment conditions as adjustable segment data. Based on the adjustable section data, load reduction processing is performed on the energy consumption ratio of high-load tasks to generate reduced load data, and the remaining load after reduction is used as input for compensation processing. Perform load compensation processing on low-load tasks based on adjustable section data, allocate compensation ratios based on load reduction data, and generate compensated load data. Load balancing is performed based on load reduction data and load compensation data, and the two are combined in chronological and task order to generate load redistribution data.

[0065] In this embodiment of the invention, by performing load adjustment processing on adjacent tasks based on load differences and capacity relationships between tasks, the redistribution of task loads can be achieved under resource constraints. By extracting task load characteristic data and identifying adjustable segments, it can be determined whether a task has the conditions for load transfer in time, thus providing a basis for the adjustment process. Performing load reduction processing on high-load tasks can reduce their load proportion in resource-scarce segments; while performing load compensation processing on low-load tasks can transfer the reduced load to more suitable tasks, making the distribution of resource usage more balanced over time. By performing load balancing processing on the reduced load data and the compensated load data, a more reasonable combination of task loads can be formed. The final generated load redistribution data can reduce the concentrated occupation of energy and emission resources among tasks, making the overall task execution present a continuous and smooth resource demand curve, improving the feasibility and execution efficiency of the adjustment data.

[0066] In a preferred embodiment of the present invention, task load characteristic data is extracted based on the energy consumption requirements of adjacent tasks and the execution segment to generate initial load data for load comparison, specifically including: First, select two tasks that are adjacent to each other on the timeline from the task structured data, and read their respective resource requirements, including the energy consumption requirements of the task during its execution period. Subsequently, the scheduled execution segments corresponding to two adjacent tasks are read, and the start time, end time, and time span of each task are recorded. Next, the energy consumption demand is combined with the execution time segment so that each task corresponds to a "task load unit", which includes the average load level and time range of the task during the execution period; Then, for ease of comparison, the two task load units are arranged in the execution order to form the initial load data; Ultimately, the initial load data is used as input for the next step of judging load differences, making the differences in energy use between tasks comparable.

[0067] In a preferred embodiment of the present invention, the process of comparing the load differences between adjacent tasks based on initial load data and marking task segments whose load differences meet the adjustment conditions as adjustable segment data specifically includes: First, read the load levels of adjacent tasks from the initial load data, compare the load levels of the two, and determine the magnitude of the load difference in textual form. Then, the load difference is compared with a preset load adjustment threshold. When the difference is higher than the threshold, it is considered that the difference has reached the condition for load adjustment. When the judgment result is adjustable, mark the task execution segment as an adjustable segment and record the correspondence between tasks with excessive load and tasks with low load; If the load difference does not reach the threshold, the section is marked as an unadjustable section. Finally, all data marked as adjustable segments are aggregated to form adjustable segment data, which serves as input for subsequent load reduction and compensation processing.

[0068] In a preferred embodiment of the present invention, load reduction processing is performed based on the energy consumption ratio of high-load tasks according to adjustable segment data, generating reduced load data, and the remaining load after reduction is used as input for compensation processing, specifically including: First, read the load levels of high-load tasks from the adjustable segment data and identify the load portions that need to be reduced; Subsequently, based on the length of the execution segment where the high-load task is located and the magnitude of the load difference, the amount of load that can be reduced is determined. This process is described in words and does not involve formulas. Next, the load level of the high-load task is adjusted according to the reduction ratio, and the reduced load value is recorded as "remaining load"; At the same time, the reduced portion is recorded as "reduced load data", and the remaining load is marked as input for subsequent load compensation steps; Finally, the reduced load data and remaining load data will be stored in a structured format to provide a basis for compensating for low-load tasks in the next step.

[0069] In a preferred embodiment of the present invention, load compensation processing is performed on low-load tasks based on adjustable segment data, and compensation load data is generated by allocating compensation ratios according to the load reduction data. Specifically, this includes: First, read the actual load level of the low-load task from the adjustable segment data and confirm that it has the ability to receive load compensation, such as whether there is still remaining energy carrying capacity in the current execution segment. Subsequently, load reduction data is obtained from the previous processing step, and the amount of compensation that can be allocated is determined based on the available capacity of the low-load tasks. Next, based on factors such as the importance of the task, execution priority, or process relationship, the reduced load is allocated to low-load tasks in a certain proportion; Record the compensated load level and use the increased load as the compensated load data; Ultimately, the compensated load data is used as input for subsequent load balancing processing, resulting in a more balanced distribution of the overall task load.

[0070] In a preferred embodiment of the present invention, load balancing processing is performed based on load reduction data and load compensation data, and the two are combined in chronological and task order to generate load redistribution data, specifically including: First, sort the load reduction data and the load compensation data according to the time axis of the task execution, so that the load changes can be arranged according to the actual execution order of the task; Subsequently, the execution segments corresponding to load reduction and those corresponding to load compensation are compared to ensure reasonable continuity and time feasibility between reduction and compensation. Next, the load adjustment information of each section is combined to achieve overall load balance between the reduction and compensation portions. For tasks with continuous segments, their load changes are further integrated to merge overly fragmented adjustments into continuous adjustment segments. Finally, the above load balancing results are recorded as load redistribution data, which is used to generate collaborative adjustment data to make the load distribution among tasks more reasonable.

[0071] Embodiments of the present invention also provide an online monitoring and collaborative management system for energy and carbon emissions, the system comprising: The data acquisition module is used to acquire energy time series data, emission time series data, and production plan time series data, and to perform time granularity unification and timestamp calibration processing to generate unified time axis data; The segmentation processing module is used to segment energy time series data according to equipment identifier, process identifier and load segment based on unified time axis data, and to segment emission time series data according to emission source identifier and emission segment. It also establishes a mapping relationship between energy consumption segments and emission segments through segmentation correspondence, and generates energy emission related data. The planning matching module is used to perform structured processing on production plan time series data based on energy emission correlation data. It converts production task identifiers, scheduled execution segments, and resource requirements into a unified format, and matches the structured production plan time series data with energy emission correlation data segment by segment along a unified time axis to generate task constraint data. The conflict identification module is used to compare energy consumption demand with available load in energy segments based on task constraint data, and to compare emission expectations with available emission quotas in emission segments. It identifies resource conflict segments through segment comparison and generates conflict classification data based on constraint type. Adjust the data generation module to generate collaborative adjustment data based on conflict classification data, including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The adjustment instruction generation module is used to determine the applicable adjustment method based on conflict classification data and generate planned adjustment instructions based on collaborative adjustment data. The planning update module is used to send planning adjustment instructions to the production planning subsystem and receive the adjusted production plan time series data to support continuous collaborative management and control of energy, emissions and production plans.

[0072] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0073] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0074] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0075] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for online monitoring and coordinated management of energy and carbon emissions, characterized in that, The method includes: Acquire energy time series data, emission time series data, and production plan time series data, and perform time granularity unification and timestamp calibration to generate unified time axis data; Based on the unified timeline data, the energy time series data is segmented according to equipment identifier, process identifier, and load segment, and the emission time series data is segmented according to emission source identifier and emission segment. The mapping relationship between energy consumption segments and emission segments is established through the segment correspondence method to generate energy emission correlation data. Based on energy emission correlation data, the production plan time series data is structured, and the production task identifier, scheduled execution segment and resource demand are converted into a unified format. The structured production plan time series data is then matched with the energy emission correlation data segment by segment along a unified time axis to generate task constraint data. Based on task constraint data, energy consumption demand is compared with available load in energy segments, and emission expectations are compared with available emission allowances in emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated according to the type of constraint. Based on the conflict classification data, generate collaborative adjustment data including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The applicable adjustment methods are determined based on the conflict classification data, and the plan adjustment instructions are generated based on the coordinated adjustment data; The system sends adjustment instructions to the production planning subsystem and receives the adjusted production plan time series data to support continuous coordinated management of energy, emissions, and production planning.

2. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 1, characterized in that, Based on unified timeline data, energy time series data is segmented according to equipment identifier, process identifier, and load segment; emission time series data is segmented according to emission source identifier and emission segment. A mapping relationship between energy consumption segments and emission segments is established through segment correspondence, generating energy emission correlation data, including: The energy time series data is analyzed point by point, and the analysis results are divided into segments according to equipment identifiers, process identifiers, and upper and lower load limits to generate basic energy consumption segment data. The emission time series data is analyzed point by point, and the analysis results are divided into segments according to the emission source identification and emission change trend to generate emission basic segment data. Based on the unified time axis data, the energy consumption baseline data and emission baseline data are time-aligned to generate time-aligned data segments. Based on the time-aligned segment data, the correspondence between the energy consumption basic segment data and the emission basic segment data in terms of time coverage and identification matching is compared, and segments that meet the matching conditions are combined to generate mapped segment data. Based on the mapped segment data, output energy emission correlation data as input for subsequent generation of task constraint data.

3. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 1, characterized in that, Based on energy emission correlation data, the production planning time series data is structured, converting production task identifiers, scheduled execution segments, and resource requirements into a unified format. The structured production planning time series data is then matched segment by segment with the energy emission correlation data along a unified time axis to generate task constraint data, including: Extract production task identifiers, scheduled execution times, and resource requirements from production plan time series data, and transform them into task structured data in a unified format; Time mapping processing is performed based on the time segments of energy emission correlation data and task structured data to generate task time mapping data; Based on the task time mapping data, the energy consumption demand in the task structured data is correlated with the energy consumption range in the energy emission correlation data segment by segment to generate energy consumption constraint data. Based on the task time mapping data, the emission impact in the task structured data is correlated with the emission magnitude in the energy emission correlation data segment by segment to generate emission constraint data; Constraint synthesis processing is performed based on energy consumption constraint data and emission constraint data to generate task constraint data.

4. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 1, characterized in that, Based on task constraint data, energy consumption demand is compared with available load in energy segments, and emission expectations are compared with available emission allowances in emission segments. Resource conflict segments are identified through segment comparison, and conflict classification data is generated according to constraint type, including: Energy consumption constraint data is extracted segment by segment based on task constraint data, and the difference is compared with the available load of energy segment to generate energy consumption difference data. Identify sections where the energy consumption difference is below the safety margin based on the energy consumption difference data, and record them as energy-side conflict data. Based on the task constraint data, emission constraint data is extracted segment by segment, and the difference is compared with the available emission credits of the emission segment to generate emission difference data. Identify segments where the emission difference is below the emission threshold based on the emission difference data, and record them as emission-side conflict data. Conflict fusion processing is performed based on energy-side conflict data and emission-side conflict data. Data with both types of conflict are comprehensively judged to generate composite conflict data. Energy-side conflict data, emissions-side conflict data, and composite conflict data are merged to generate conflict classification data.

5. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 1, characterized in that, Based on the conflict classification data, generate coordinated adjustment data including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes, including: Based on the conflict classification data, identify energy-side conflict zones and generate candidate alternative execution zone data based on the time periods with higher available load within the energy zones; Based on the conflict classification data, emission-side conflict zones are identified, and emission substitution implementation zone data are generated based on the time periods with more lenient emission constraints. Based on the conflict classification data, identify the composite conflict segments, and divide the execution segments into multiple sub-segments according to the continuity rules based on the task's separable attributes, generating segment splitting data; Based on the task-equipment correspondence in the conflict classification data, alternative equipment that can reduce energy consumption or reduce emissions impact is selected, and equipment switching data is generated. Based on the load differences and capacity relationships between tasks, load adjustment processing is performed on the energy consumption ratio of adjacent tasks to generate load redistribution data; The candidate alternative implementation segment data, emission alternative implementation segment data, segment split data, equipment switching data, and load redistribution data are merged to generate coordinated adjustment data.

6. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 2, characterized in that, Based on the time-aligned segment data, the correspondence between the energy consumption baseline segment data and the emission baseline segment data in terms of time coverage and identifier matching is compared. Segments that meet the matching conditions are combined to generate mapped segment data, including: Extract the start and end times of the energy consumption basic segment data, as well as the corresponding equipment and process identifiers, from the time-aligned segment data to generate energy consumption segment feature data. Based on the time-aligned segment data, the start and end times of the emission base segment data and the corresponding emission source identifiers are extracted to generate emission segment feature data. Based on the comparison of the time overlap duration between the energy consumption segment characteristic data and the emission segment characteristic data, coverage determination data representing the time coverage relationship is generated. Based on the coverage determination data, segments whose time overlap duration reaches the preset coverage conditions are formed into time-matching segment data. Based on the time-matching segment data, the correlation between equipment identifiers, process identifiers and emission source identifiers is compared to generate identifier matching judgment data; Based on the identifier matching judgment data, segments that meet the matching conditions are combined to form mapped segment data.

7. The method for online monitoring and coordinated management of energy and carbon emissions according to claim 5, characterized in that, Based on the load differences and capacity relationships between tasks, load adjustment processing is performed on the energy consumption ratio of adjacent tasks to generate load redistribution data, including: Based on the energy consumption requirements of adjacent tasks and the execution segment, task load characteristic data is extracted to generate initial load data for load comparison. Based on the initial load data, compare the load differences between adjacent tasks and mark the task segments whose load differences reach the adjustment conditions as adjustable segment data. Based on the adjustable section data, load reduction processing is performed on the energy consumption ratio of high-load tasks to generate reduced load data, and the remaining load after reduction is used as input for compensation processing. Perform load compensation processing on low-load tasks based on adjustable section data, allocate compensation ratios based on load reduction data, and generate compensated load data. Load balancing is performed based on load reduction data and load compensation data, and the two are combined in chronological and task order to generate load redistribution data.

8. An online monitoring and collaborative management system for energy and carbon emissions, characterized in that, The system, used in the method of any one of claims 1 to 7, comprises: The data acquisition module is used to acquire energy time series data, emission time series data, and production plan time series data, and to perform time granularity unification and timestamp calibration processing to generate unified time axis data; The segmentation processing module is used to segment energy time series data according to equipment identifier, process identifier and load segment based on unified time axis data, and to segment emission time series data according to emission source identifier and emission segment. It also establishes a mapping relationship between energy consumption segments and emission segments through segmentation correspondence, and generates energy emission related data. The planning matching module is used to perform structured processing on production plan time series data based on energy emission correlation data. It converts production task identifiers, scheduled execution segments, and resource requirements into a unified format, and matches the structured production plan time series data with energy emission correlation data segment by segment along a unified time axis to generate task constraint data. The conflict identification module is used to compare energy consumption demand with available load in energy segments based on task constraint data, and to compare emission expectations with available emission quotas in emission segments. It identifies resource conflict segments through segment comparison and generates conflict classification data based on constraint type. Adjust the data generation module to generate collaborative adjustment data based on conflict classification data, including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes; The adjustment instruction generation module is used to determine the applicable adjustment method based on conflict classification data and generate planned adjustment instructions based on collaborative adjustment data. The planning update module is used to send planning adjustment instructions to the production planning subsystem and receive the adjusted production plan time series data to support continuous collaborative management and control of energy, emissions and production plans.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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