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

Abstract

The application provides an energy and carbon emission online monitoring and collaborative management method and system, relates to the technical field of collaborative management, and comprises the following steps: acquiring energy time series data, emission time series data and production plan time series data; performing segmented processing on the energy time series data and the emission time series data, and establishing a mapping relationship between energy consumption segments and emission segments through segmented correspondence; performing structured processing on the production plan time series data, and matching the structured production plan time series data and energy emission correlation data according to a uniform time axis in sections; comparing energy consumption demand and energy segment available load, and comparing emission expectation and emission segment available emission quota, and identifying resource conflict sections through sectional comparison; and generating collaborative adjustment data; and the application improves the autonomy and accuracy of energy and carbon emission collaborative management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of collaborative management, in particular to an energy and carbon emission online monitoring and collaborative management method and system. BACKGROUND

[0002] In the prior art, the online management of energy and carbon emissions is usually realized by relying on a modular enterprise resource management system. A typical deployment mode is to construct an energy collection platform, an emission accounting module, a production scheduling module, and an energy cost accounting module as independent business subsystems, and to exchange data through an enterprise service bus or an interface-based mode. The system usually adopts fixed emission factors, static energy consumption models, and preset process rules when calculating energy consumption and emissions. In the daily operation of the enterprise, the above-mentioned system mainly undertakes functions such as energy consumption data reporting, emission accounting, quota use statistics, and production plan reference. However, the overall process structure is relatively fixed, the business rules still rely on manual configuration, and only the final result data is transmitted between the subsystems, making it difficult to form an integrated dynamic linkage oriented to energy, emission, and business decision-making.

[0003] In scenarios where energy behavior and business behavior need to be considered simultaneously, such as when a large manufacturing enterprise formulates a monthly production schedule, the above-mentioned existing management system often has obvious limitations. Taking the actual scheduling process as an example, the planning system can usually only obtain the energy and emission summary data of the last settlement period, while the raw data of the energy management subsystem is still stored in the form of scattered records on a daily and device basis. Due to the lack of a time series-oriented correlation mechanism, the scheduling simulation cannot automatically introduce key factors such as the energy consumption trend of the product processing process, the emission differences of the equipment under different load conditions, and the dynamic occupation of the remaining carbon quota. As a result, problems such as scheduling results arranged in time periods where energy is in a limited state may occur, and the system cannot identify this conflict in advance when generating the schedule, resulting in the need for frequent adjustments in the subsequent execution phase, and even causing emission accounting deviations. The root cause lies in the fact that the existing technology cannot manage and link analysis of energy monitoring data, production plan data, and emission constraints on a unified time axis. SUMMARY

[0004] The present application aims to provide an energy and carbon emission online monitoring and collaborative management method and system, which aims to solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the technical solutions of the present application are as follows:

[0006] In a first aspect, an energy and carbon emission online monitoring and collaborative management method is provided, which comprises:

[0007] The energy time series data, the emission time series data and the production plan time series data are acquired, time granularity unification and timestamp calibration processing are performed, and unified time axis data is generated;

[0008] According to the unified time axis data, the energy time series data is segmented according to the equipment identifier, the process identifier and the load section, the emission time series data is segmented according to the emission source identifier and the emission section, and a mapping relationship between the energy consumption segment and the emission segment is established through the segmented corresponding manner, and energy emission correlation data is generated;

[0009] According to the energy emission correlation data, the production plan time series data is structured, the production task identifier, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched according to the unified time axis and the segmented section, and task constraint data is generated;

[0010] According to the task constraint data, the energy consumption demand and the available load of the energy section are compared, and the emission expectation and the available emission capacity of the emission section are compared, the resource conflict section is identified through the sectional comparison, and the conflict classification data is generated according to the limit type;

[0011] According to the conflict classification data, collaborative adjustment data including alternative execution section, section splitting scheme, equipment switching scheme or load redistribution scheme is generated;

[0012] According to the conflict classification data, the applicable adjustment mode is determined, and the plan adjustment instruction is generated based on the collaborative adjustment data;

[0013] The plan adjustment instruction is sent to the production plan subsystem, and the adjusted production plan time series data is received to support the continuous collaborative management and control of energy, emission and production plan.

[0014] Preferably, according to the unified time axis data, the energy time series data is segmented according to the equipment identifier, the process identifier and the load section, the emission time series data is segmented according to the emission source identifier and the emission section, and a mapping relationship between the energy consumption segment and the emission segment is established through the segmented corresponding manner, and energy emission correlation data is generated, including:

[0015] The energy time series data is analyzed point by point, and the segmented division processing is performed on the analysis result according to the equipment identifier, the process identifier and the load upper and lower limit, and the energy consumption basic section data is generated;

[0016] The emission time series data is analyzed point by point, and the segmented division processing is performed on the analysis result according to the emission source identifier and the emission change trend, and the emission basic section data is generated;

[0017] According to the unified time axis data, the energy consumption basic section data and the emission basic section data are time-aligned to generate time-aligned section data;

[0018] According to the time-aligned section data, the corresponding relationship of the energy consumption basic section data and the emission basic section data in time coverage and identification matching degree is compared, and the section that meets the matching condition is combined to generate mapping section data;

[0019] According to the mapping section data, energy emission correlation data is output as the input of subsequent generation task constraint data.

[0020] Preferably, according to the energy emission correlation data, the production plan time series data is structured, the production task identification, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched according to the unified time axis by section to generate task constraint data, including:

[0021] According to the production plan time series data, the production task identification, the scheduled execution time and the resource demand are extracted and converted into task structured data in a unified format;

[0022] According to the time section execution time mapping processing of the energy emission correlation data and the task structured data, task time mapping data is generated;

[0023] According to the task time mapping data, the energy consumption demand in the task structured data and the energy consumption range in the energy emission correlation data are associated and calculated by section to generate energy consumption constraint data;

[0024] According to the task time mapping data, the emission impact in the task structured data and the emission range in the energy emission correlation data are associated and calculated by section to generate emission constraint data;

[0025] According to the energy consumption constraint data and the emission constraint data, constraint synthesis processing is performed to generate task constraint data.

[0026] Preferably, according to the task constraint data, the energy consumption demand and the energy section available load are compared, and the emission expectation and the emission section available emission amount are compared, the resource conflict section is identified through section comparison, and the conflict classification data is generated according to the limit type, including:

[0027] According to the task constraint data, the energy consumption constraint data is extracted by section, and the difference value comparison is performed with the energy section available load to generate energy consumption difference value data;

[0028] According to the energy consumption difference value data, the section with energy consumption difference value lower than the safety margin is recorded as energy side conflict data;

[0029] According to the task constraint data, the emission constraint data is extracted segment by segment, and is compared with the available emission quota of the emission segment to generate emission difference data;

[0030] According to the emission difference data, the segments with emission difference lower than the emission threshold are identified and recorded as emission side conflict data;

[0031] According to the energy side conflict data and the emission side conflict data, a conflict fusion process is performed to comprehensively judge the data with both types of conflicts and generate composite conflict data;

[0032] The energy side conflict data, the emission side conflict data and the composite conflict data are merged to generate conflict classification data.

[0033] Preferably, according to the conflict classification data, collaborative adjustment data containing alternative execution segments, segment splitting schemes, device switching schemes or load redistribution schemes are generated, including:

[0034] According to the conflict classification data, the energy side conflict segments are identified, and candidate alternative execution segment data is generated according to the time segments with higher available load in the energy segment;

[0035] According to the conflict classification data, the emission side conflict segments are identified, and emission alternative execution segment data is generated according to the time segments with relatively loose emission constraints;

[0036] According to the conflict classification data, the composite conflict segments are identified, and the execution segments thereof are divided into multiple subsegments according to the continuity rule according to the task's splittable attribute to generate segment splitting data;

[0037] According to the task and device correspondence relationship in the conflict classification data, alternative devices that can reduce energy consumption or reduce emission impact are screened out to generate device switching data;

[0038] According to the load difference and capacity relationship between tasks, load adjustment processing is performed on the energy consumption proportion of adjacent tasks to generate load redistribution data;

[0039] The candidate alternative execution segment data, the emission alternative execution segment data, the segment splitting data, the device switching data and the load redistribution data are merged to generate collaborative adjustment data.

[0040] Preferably, according to the time alignment segment data, the corresponding relationship of the energy consumption basic segment data and the emission basic segment data in time coverage and identification matching degree is compared, and the segments meeting the matching conditions are combined to generate mapping segment data, including:

[0041] According to the time alignment segment data, the start time and end time of the energy consumption basic segment data and the corresponding device identifier and process identifier are extracted to generate energy consumption segment feature data;

[0042] According to the time alignment section data, the start time, the end time and the corresponding emission source identifier of the emission base section data are extracted, and emission section feature data is generated;

[0043] According to the comparison of energy consumption section feature data and emission section feature data, the time overlap length of the two types of sections is generated, and coverage degree judgment data representing the time coverage relationship is generated;

[0044] According to the coverage degree judgment data, the sections whose time overlap length reaches the preset coverage condition are formed into time matching section data;

[0045] According to the time matching section data, the association degree between the device identifier, the process identifier and the emission source identifier is compared, and identification matching judgment data is generated;

[0046] According to the identification matching judgment data, the sections whose association degree reaches the matching condition are combined to form mapping section data.

[0047] Preferably, according to the load difference between tasks and the capacity relationship, the energy consumption proportion of adjacent tasks is subjected to load adjustment processing, and load redistribution data is generated, including:

[0048] According to the energy consumption demand quantity and the execution section of adjacent tasks, task load feature data is extracted, and initial load data for load comparison is generated;

[0049] According to the initial load data, the load difference between adjacent tasks is compared, and the task section whose load difference reaches the adjustment condition is marked as adjustable section data;

[0050] According to the adjustable section data, the energy consumption proportion of high-load tasks is subjected to load reduction processing, and reduction load data is generated, and the remaining load after reduction is used as the input of compensation processing;

[0051] According to the adjustable section data, load compensation processing is performed on low-load tasks, and compensation proportion is allocated according to the reduction load data, and compensation load data is generated;

[0052] According to the reduction load data and the compensation load data, load balancing processing is performed, and the two are combined in time sequence and task sequence to generate load redistribution data.

[0053] The second aspect is an online monitoring and collaborative control system of energy and carbon emissions, which comprises:

[0054] The data acquisition module is used for acquiring energy time series data, emission time series data and production plan time series data, and performing time granularity unification and timestamp calibration processing to generate unified time axis data;

[0055] The segmented processing module is configured to segment the energy time series data according to the device identifier, the process identifier and the load segment according to the unified time axis data, segment the emission time series data according to the emission source identifier and the emission segment, and establish a mapping relationship between the energy consumption segments and the emission segments through a segmented correspondence manner, and generate energy emission correlation data.

[0056] The plan matching module is configured to structure the production plan time series data according to the energy emission correlation data, convert the production task identifier, the scheduled execution segment and the resource demand into a unified format, and match the structured production plan time series data and the energy emission correlation data according to the unified time axis and the segmented manner, and generate task constraint data.

[0057] The conflict identification module is configured to compare the energy consumption demand and the available load of the energy segment according to the task constraint data, compare the emission expectation and the available emission quota of the emission segment, identify the resource conflict segment through the segmented comparison, and generate conflict classification data according to the limit type.

[0058] The adjustment data generation module is configured to generate collaborative adjustment data including a replacement execution segment, a segment splitting scheme, a device switching scheme or a load redistribution scheme according to the conflict classification data.

[0059] The adjustment instruction generation module is configured to determine the applicable adjustment mode according to the conflict classification data, and generate a plan adjustment instruction based on the collaborative adjustment data.

[0060] The plan updating module is configured to send the plan adjustment instruction to the production plan subsystem, and receive the adjusted production plan time series data to support the continuous collaborative management and control of the energy, the emission and the production plan.

[0061] The above-mentioned scheme of the present application at least has the following beneficial effects:

[0062] Firstly, by performing the time granularity unification and the timestamp calibration processing on the energy time series data, the emission time series data and the production plan time series data, the data from different business systems can be expressed under the same time reference, and the problem that the energy records are dispersedly stored in the form of day by day and device by device and are inconsistent with the summary period used by the plan system in the prior art is fundamentally solved. By establishing the unified time axis, the task execution process, the energy change process and the emission change process can be directly comparable, and a foundation is laid for carrying out the cross-system time series analysis.

[0063] Secondly, by segmenting the energy time series data and the emission time series data according to the equipment identifier, the process identifier, the emission source identifier and other dimensions, and establishing the mapping relationship between the energy consumption segments and the emission segments through the time correspondence, the static emission coefficient and the fixed energy consumption model provided by the traditional system can be converted into segmented data with dynamic change characteristics. In this way, the present application makes the correlation between energy consumption and emission have continuity and context relationship, and makes up for the defects that the existing technology cannot reflect the energy consumption trend and the equipment emission difference in the processing process.

[0064] Thirdly, by matching the structured production plan time series data with the energy emission correlation data segment by segment, the energy consumption and emission impact required by each task during execution can be deduced in a time-aligned manner. The present application can identify the pressure change of the energy and emission system caused by task execution in advance during the scheduling generation stage, instead of discovering the conflict in the execution stage as in the prior art, thereby improving the response capability to resource conditions during scheduling simulation.

[0065] Further, by comparing the task constraint data with the energy segment available load and the emission segment available emission allowance at the segment level, resource conflicts that may occur under energy limitation or insufficient emission permission can be accurately identified. The present application classifies the conflicts into energy side, emission side and composite conflict, so that different types of conflicts can correspond to different solving strategies, instead of the method of the prior art which can only prompt the abnormal result but cannot distinguish the cause.

[0066] Subsequently, the present application can generate alternative execution segments, segment splitting schemes, device switching schemes or load redistribution schemes according to the conflict classification data, so that scheduling adjustment is no longer dependent on manual experience, but is selected based on the multiple available strategies provided by the system. This not only enhances the feasibility of adjustment operation, but also improves the coordination ability between production plan and energy constraints.

[0067] Finally, by feeding back the plan adjustment instruction to the production plan subsystem and redeveloping the energy emission correlation calculation according to 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 the high energy consumption process is originally scheduled in the early peak load segment, the present application can automatically generate an adjustment instruction to split the process into two subsegments and move them to the low load period respectively. After the production plan subsystem updates the task arrangement according to the instruction, the present application will again perform correlation analysis based on the new schedule, so as to realize the continuous linkage between energy change, emission limitation and production scheduling. The present application can avoid energy and emission conflicts in the scheduling generation stage, reduce temporary adjustments in the execution stage, and improve the coordination and consistency of the entire production operation process. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 is a flow chart of the energy and carbon emission online monitoring and collaborative management method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0069] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0070] As shown in Figure 1 , the embodiment of the present application proposes an energy and carbon emission online monitoring and collaborative management method, which comprises:

[0071] obtaining energy time series data, emission time series data and production plan time series data, and performing time granularity unification and timestamp calibration processing to generate unified time axis data;

[0072] According to the unified time axis data, the energy time series data is segmented according to the equipment identifier, the process identifier and the load section, the emission time series data is segmented according to the emission source identifier and the emission section, and the mapping relationship between the energy consumption segment and the emission segment is established through the segmented correspondence to generate energy emission correlation data;

[0073] According to the energy emission correlation data, the production plan time series data is structured, the production task identifier, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched by the unified time axis by section to generate task constraint data;

[0074] According to the task constraint data, the energy consumption demand and the available load of the energy segment are compared, and the emission expectation and the available emission capacity of the emission segment are compared, the resource conflict section is identified through the section comparison, and the conflict classification data is generated according to the limit type;

[0075] According to the conflict classification data, collaborative adjustment data containing alternative execution section, section splitting scheme, equipment switching scheme or load redistribution scheme is generated;

[0076] According to the conflict classification data, the applicable adjustment mode is determined, and the plan adjustment instruction is generated based on the collaborative adjustment data;

[0077] The plan adjustment instruction is sent to the production plan subsystem, and the adjusted production plan time series data is received to support the continuous collaborative management of energy, emission and production plan.

[0078] In the embodiments of the present application, by performing time granularity unification and timestamp calibration processing on energy time series data, emission time series data and production plan time series data, the data from different sources are provided with consistent time reference, which facilitates the formation of a unified time axis suitable for cross-system processing, thereby providing a basic condition for subsequent correlation calculation. By establishing a unified time axis, different categories of data are comparable and can be continuously processed on the same time series, avoiding the data misplacement problem caused by inconsistent time reference in traditional energy management systems.

[0079] By performing segmented processing on energy time series data and emission time series data according to different identifiers and segments, and performing corresponding analysis on the two types of segments after time alignment, a data structure reflecting the relationship between energy consumption change and emission change can be generated. This correlation data can show the emission change corresponding to the energy consumption behavior in a certain time period, providing a basis for subsequent production plan matching. Through this correlation method based on segment correspondence, the dynamic linkage characteristics of energy and emission can be reflected under the unified time axis.

[0080] By structuring the data related to production tasks and matching them with energy emission correlation data in segments on the unified time axis, constraint data describing the stress on energy systems and emission systems caused by task execution can be generated. This constraint data can represent the energy consumption demand and emission demand superimposed during task execution, thereby establishing an accurate mapping relationship between production plan and energy emission state. This mapping method helps to estimate the resource occupation during task execution before plan adjustment, avoiding resource conflicts during plan execution.

[0081] By comparing task constraint data with energy segment available load and emission segment available emission allowance, resource conflict situations that may occur during task execution can be identified and classified according to the conflict sources. After classifying the conflicts, different adjustment methods can be used according to different types of limiting factors, avoiding treating all conflicts as the same type of problem, and improving the pertinence of subsequent adjustment strategies.

[0082] By generating collaborative adjustment data for adjusting task execution mode according to conflict classification data, various task adjustment strategies can be provided, including adjusting task execution segment, splitting tasks, selecting alternative equipment or adjusting task load ratio. By setting different adjustment paths, different conflict situations can be flexibly handled, reducing the impact on the established production rhythm.

[0083] By generating the plan adjustment instruction according to the feasible adjustment mode and sending the adjusted instruction to the production plan subsystem, a closed-loop management mode between the energy state, the emission state and the production task arrangement can be formed. The mode enables the plan to be adjusted in time when the energy side or the emission side appears a limitation, and also enables the task execution result to continue to participate in the collaborative analysis of the next round through feedback, thereby improving the continuity and coordination of the management process.

[0084] In a preferred embodiment of the present application, the applicable adjustment mode is determined according to the conflict classification data, and the plan adjustment instruction is generated based on the collaborative adjustment data, specifically including:

[0085] Firstly, the type information of the conflict to which the conflict belongs is read from the conflict classification data, including the energy side conflict, the emission side conflict or the composite conflict, and the task section that needs to be adjusted is confirmed according to the starting time period, the duration and the conflict degree of the conflict;

[0086] Then, the applicable adjustment mode is determined according to the conflict type, for example, the alternative execution section or the load redistribution is preferentially selected in the energy side conflict, the emission alternative section or the equipment switching is preferentially selected in the emission side conflict, and the section splitting mode is preferentially selected in the composite conflict;

[0087] Subsequently, the adjustment content corresponding to the selected adjustment mode is read from the collaborative adjustment data, including the alternative execution section, the splitting position of the task sub-section, the switchable equipment list or the adjustable load proportion;

[0088] Then, the adjustment content is compared with the current production task list according to the execution order of the task in the original plan, the plan time window and the resource usage, so as to form the adjustment parameter that can directly modify the production plan;

[0089] Finally, the adjustment parameter is packaged into a structured plan adjustment instruction, so that the production plan subsystem can modify the task execution section, the execution order or the execution equipment according to the adjustment instruction, thereby completing the adjustment preparation work of the plan.

[0090] In a preferred embodiment of the present application, the plan adjustment instruction is sent to the production plan subsystem, and the adjusted production plan time sequence data is received, specifically including:

[0091] Firstly, the plan adjustment instruction is converted into a data format that can be recognized by the production plan subsystem, so that the adjustment information including the execution section, the task order, the equipment selection and the load allocation has the resolvability;

[0092] Subsequently, the plan adjustment instruction is sent to the production plan subsystem through a preset communication interface (such as an internal message queue or a data interaction interface);

[0093] The production planning subsystem updates the related tasks in the original plan after receiving the adjustment instruction, including modifying the execution time, updating the task dependency relationship, replacing the execution equipment or adjusting the task load;

[0094] After the update is completed, the production planning subsystem generates new production planning time series data and returns it to the processing unit in the method;

[0095] Finally, the method determines whether the energy emission correlation data, task constraint data or conflict classification data needs to be reprocessed according to the adjusted production planning time series data, thereby forming the ability of continuous collaborative management and control.

[0096] In a preferred embodiment of the present application, according to the unified time axis data, the energy time series data is segmented according to the equipment identifier, process identifier and load section, the emission time series data is segmented according to the emission source identifier and emission section, and the mapping relationship between the energy consumption section and the emission section is established through the segmented corresponding manner to generate the energy emission correlation data, including:

[0097] The energy time series data is analyzed point by point, and the segmented division processing is performed on the analysis result according to the equipment identifier, process identifier and load upper and lower limit to generate the energy consumption basic section data;

[0098] The emission time series data is analyzed point by point, and the segmented division processing is performed on the analysis result according to the emission source identifier and emission change trend to generate the emission basic section data;

[0099] According to the unified time axis data, the energy consumption basic section data and the emission basic section data are time-aligned to generate time-aligned section data;

[0100] According to the time-aligned section data, the corresponding relationship of the energy consumption basic section data and the emission basic section data in time coverage and identifier matching degree is compared, and the section that meets the matching condition is combined to generate mapping section data;

[0101] According to the mapping section data, the energy emission correlation data is output as the input of the subsequent generation of task constraint data.

[0102] In the embodiment of the present application, by analyzing the energy time series data and the emission time series data at each time point, the originally continuous but unstructured monitoring curve can be converted into a basic section with boundary information and characteristic attributes. After further alignment processing of the basic sections under the unified time axis, the time-aligned sections reflecting the comparable relationship between the two types of data in time can be formed. By comparing the time coverage between the energy consumption basic section and the emission basic section in the time-aligned section, the time periods during which the two types of sections appear together in the actual operation can be identified, thereby forming a time matching basis for subsequent correlation analysis. Then, by matching degree analysis of the equipment identifier, process identifier and emission source identifier in the time matching section, the section combinations with correlation in actual production operation can be screened out. The mapping section data obtained after screening can reflect the corresponding relationship between energy consumption change and emission change, providing basic input for constructing energy emission correlation data, so that the subsequent steps can use the correlation relationship to carry out task constraint generation and conflict identification.

[0103] In a preferred embodiment of the present application, the energy time series data is analyzed at each time point, and the analysis results are subjected to section division processing according to the equipment identifier, process identifier and load upper and lower limits, specifically including:

[0104] Firstly, 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;

[0105] Subsequently, the energy consumption values of consecutive time points are compared, and when the energy consumption values continuously appear within a certain range and belong to the same equipment and process, these consecutive time points are summarized as a candidate energy consumption section;

[0106] In the candidate energy consumption section, the minimum load value and the maximum load value are identified in order to further divide the section according to the preset load interval (such as low load section, medium load section, high load section);

[0107] After the division is completed, the start time, end time, equipment identifier, process identifier and load level are recorded for each energy consumption basic section, so that the section can be used for subsequent matching processing;

[0108] Through the above section division method, the originally continuous monitoring data is divided into energy consumption basic sections with boundaries and semantic attributes, which is convenient for cross-system correlation processing.

[0109] In a preferred embodiment of the present application, the emission time series data is analyzed at each time point, and the analysis results are subjected to section division processing according to the emission source identifier and emission change trend, specifically including:

[0110] Firstly, the emission time series data is read in time sequence, and the emission value and emission source identifier at each time point are obtained;

[0111] Subsequently, by comparing the emission value change trend of adjacent time points, the time period with continuously rising, continuously falling or stable emission value is divided into separate emission candidate segments;

[0112] Then, according to the emission source identifier, it is judged whether the candidate segment is from the same emission source, if it is from the same emission source, it is kept as a single emission segment, if there is a source change, it is split;

[0113] Further, the emission level range of each emission basic segment is calculated, including the minimum and maximum emission amount appeared in the segment, to form the emission characteristics that can be used for subsequent matching conditions;

[0114] The finally generated emission basic segment data has time boundary, emission trend and emission source attribute, so that it can be compared with the energy consumption basic segment.

[0115] In a preferred embodiment of the present application, according to the unified time axis data, the energy consumption basic segment data and the emission basic segment data are time-aligned, specifically including:

[0116] Firstly, according to the minimum time granularity of the unified time axis data, the start time and end time of the energy consumption basic segment and the emission basic segment are aligned to the unified time scale;

[0117] Subsequently, the energy consumption basic segment data and the emission basic segment data belonging to the same time scale range are classified respectively, so that the two segments form a comparable corresponding relationship in time;

[0118] If the start and end time of the energy consumption basic segment and the emission basic segment exist small offset, then according to the time overlap degree of the segment, the segment boundary is aligned to the nearest time scale on the unified time axis, so that it is consistent with the unified time axis;

[0119] The aligned segments are combined into time-aligned segment data in the order of the unified time axis, each time-aligned segment contains energy consumption characteristics, emission characteristics and corresponding time range;

[0120] The time-aligned segment data enables the generation of subsequent mapping segments to be compared under strict consistent time reference, improving the accuracy of correlation analysis.

[0121] In a preferred embodiment of the present application, according to the energy emission correlation data, the production plan time series data is structured, the production task identifier, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched by section according to a unified time axis to generate task constraint data, including:

[0122] According to the production plan time series data, the production task identifier, the scheduled execution time and the resource demand are extracted and converted into task structured data in a unified format;

[0123] According to the time section execution time mapping processing of the energy emission correlation data and the task structured data, task time mapping data is generated;

[0124] According to the task time mapping data, the energy consumption demand in the task structured data and the energy consumption amplitude in the energy emission correlation data are associated and calculated by section to generate energy consumption constraint data;

[0125] According to the task time mapping data, the emission impact in the task structured data and the emission amplitude in the energy emission correlation data are associated and calculated by section to generate emission constraint data;

[0126] According to the constraint synthesis processing of the energy consumption constraint data and the emission constraint data, task constraint data is generated.

[0127] In an embodiment of the present application, by extracting the production task identifier, the scheduled execution time and the resource demand from the production plan time series data, the original business data oriented to plan arrangement can be converted into structured task data, which is convenient for combination with the energy emission correlation data. After time mapping processing of the task structured data, the task execution section can be positioned according to a unified time axis, so that the task execution behavior can be time-correlated with the energy and emission state. By using the task time mapping data, the energy consumption demand in the task structured data and the energy consumption amplitude in the energy emission correlation data can be compared to generate energy consumption constraint data for analyzing the energy consumption pressure of task execution. At the same time, the emission impact and the emission amplitude are calculated to obtain emission constraint data describing the emission pressure. After further synthesis of the energy consumption constraint data and the emission constraint data, task constraint data representing the double influence of the energy side and the emission side during task execution can be formed, which is convenient for resource conflict identification and plan adjustment in subsequent processing.

[0128] In a preferred embodiment of the present application, according to the production plan time series data, the production task identifier, the scheduled execution time and the resource demand are extracted and converted into task structured data in a unified format, specifically including:

[0129] Firstly, the production plan time series data is scanned in time sequence, and the task number, product name or process type corresponding to each task is identified from the data as task identification information;

[0130] Subsequently, the expected start time and the expected end time of the task are extracted from the same production plan record, and the two time values are recorded as the scheduled execution time interval of the task;

[0131] Then, the energy consumption, equipment usage demand or other related resource occupation information required by the task is read from the production plan data, and these information is parsed into resource demand data of the task;

[0132] Then, in order to enable the task data to be used together with the energy emission association data, the task identification, scheduled execution interval and resource demand are integrated according to a unified data structure, so that the task can be expressed in a unified format;

[0133] Finally, the structured task data formed has identifiable task identification attribute, locatable execution time attribute and calculable resource demand attribute, so that it can be used as input data for subsequent task mapping processing.

[0134] In a preferred embodiment of the present application, according to the time section execution time mapping processing of the energy emission association data and the task structured data, task time mapping data is generated, specifically including:

[0135] Firstly, the scheduled execution section in the task structured data is expanded according to the minimum time granularity of the unified time axis, so that the task data can be arranged in continuous time units on the unified time axis;

[0136] Subsequently, the expanded task execution time unit and the corresponding time section of the energy emission association data are detected one by one for time overlap, and the overlapping part is marked as "effective mapping time section";

[0137] For the time section without overlap, it is recorded as the time section corresponding to the task which cannot be matched with the energy emission data, so as to make a supplementary judgment on the resource occupation of the task later;

[0138] Then, the task identification, energy consumption change interval and emission change interval in each effective mapping time section are combined to form the time mapping unit corresponding to the task;

[0139] Finally, all the time mapping units are sorted and merged according to the time sequence of task execution to form the task time mapping data, so as to establish a stable corresponding relationship between the task execution process and the energy emission state.

[0140] In a preferred embodiment of the present application, according to the task time mapping data, the energy consumption demand in the task structured data is associated with the energy consumption range in the energy emission associated data for each section to generate energy consumption constraint data, which specifically includes:

[0141] First, the energy consumption demand of the task in the time section is read from the task time mapping data, and the energy consumption range in the energy emission associated data in the mapping time section is read at the same time;

[0142] Subsequently, by comparing the energy consumption demand and the energy consumption range, it is determined whether the task execution in the section will cause the energy system to bear additional pressure;

[0143] If the energy consumption demand of the task is greater than the energy consumption range, the section is recorded as an energy consumption stressed section, and the value of the excess part is recorded as energy consumption pressure data;

[0144] If the energy consumption demand of the task is less than or equal to the energy consumption range, the section is recorded as an energy consumption bearable section;

[0145] According to the energy consumption pressure data and the bearable data of all sections, energy consumption constraint data is generated, which is used as structured constraint information representing the energy consumption pressure of the task execution.

[0146] In a preferred embodiment of the present application, according to the task time mapping data, the emission impact in the task structured data is associated with the emission range in the energy emission associated data for each section to generate emission constraint data, which specifically includes:

[0147] First, the emission impact of the task in the corresponding time section is obtained from the task time mapping data, and the emission range in the energy emission associated data in the time section is read at the same time;

[0148] Subsequently, the emission impact and the emission range are compared to determine whether the task execution will cause the emission system to be under pressure;

[0149] When the emission impact is higher than the emission range, the section is recorded as an emission limited section, and the part exceeding the emission range is calculated as emission pressure data;

[0150] When the emission impact is not higher than the emission range, the section is recorded as an emission bearable section;

[0151] Finally, by summarizing the emission limited section and the emission bearable section, a data structure that can be used for conflict identification, i.e., emission constraint data, is formed.

[0152] In a preferred embodiment of the present application, according to the energy consumption constraint data and the emission constraint data, constraint synthesis processing is performed to generate task constraint data, which specifically includes:

[0153] Firstly, the energy consumption constraint data and the emission constraint data are compared according to a unified time axis, and the energy consumption pressure data and the emission pressure data in the same execution section are read out;

[0154] Subsequently, by comparing the size relationship between the energy consumption pressure and the emission pressure, it is judged that the section has pressure on the energy side, the emission side or both sides;

[0155] If a section contains both energy consumption pressure and emission pressure, the section is marked as a composite pressure section;

[0156] If a section contains only energy consumption pressure, it is marked as an energy side pressure section;

[0157] If a section contains only emission pressure, it is marked as an emission side pressure section;

[0158] Finally, all the pressure sections are combined in time sequence to form task constraint data that can be directly used for resource conflict identification.

[0159] In a preferred embodiment of the present application, according to the task constraint data, the energy consumption demand is compared with the available load of the energy section, and the emission expectation is compared with the available emission capacity of the emission section, the resource conflict section is identified by section comparison, and the conflict classification data is generated according to the limit type, including:

[0160] According to the task constraint data, the energy consumption constraint data is extracted section by section, and is compared with the available load of the energy section to generate energy consumption difference data;

[0161] According to the energy consumption difference data, the section with energy consumption difference lower than the safety margin is recorded as energy side conflict data;

[0162] According to the task constraint data, the emission constraint data is extracted section by section, and is compared with the available emission capacity of the emission section to generate emission difference data;

[0163] According to the emission difference data, the section with emission difference lower than the emission threshold is recorded as emission side conflict data;

[0164] According to the energy side conflict data and the emission side conflict data, conflict fusion processing is performed, comprehensive judgment is made on the data with both types of conflicts, and composite conflict data is generated;

[0165] The energy side conflict data, the emission side conflict data and the composite conflict data are combined to generate conflict classification data.

[0166] In the embodiment of the present application, by extracting energy consumption constraint information from task constraint data and comparing it with available load of energy section, time periods in which task execution may cause insufficient load in energy system can be identified, thereby generating energy consumption difference data. Sections with energy consumption difference lower than safety margin are marked as energy side conflict sections, so that potential insufficient energy supply can be found before plan adjustment. Similarly, by extracting emission constraint information and comparing it with available emission quota of emission section, sections with insufficient emission permit condition can be identified, thereby generating emission difference data and marking corresponding emission side conflict sections. For sections with both energy side conflict and emission side conflict, they can be identified as composite conflict sections through fusion processing. The finally formed conflict classification data can clearly show conflict types, so that subsequent adjustment strategies can take different ways according to different conflict sources, thereby improving the matching degree of adjustment scheme and actual conditions.

[0167] In a preferred embodiment of the present application, energy consumption constraint data is extracted from task constraint data section by section, and compared with available load of energy section to generate energy consumption difference data, which specifically includes:

[0168] Firstly, energy consumption pressure value and energy consumption demand of each execution section corresponding to the task constraint data are read one by one, and the two are combined as energy consumption constraint data of the section;

[0169] Subsequently, available load value consistent with the time range of the execution section is extracted from the available load data of the energy section, and compared with the energy consumption constraint data item by item;

[0170] In the comparison, the available load value is subtracted from the energy consumption demand to determine the resource surplus; in this process, only whether there is surplus is recorded in a textual manner, without involving mathematical expressions;

[0171] If the available load is greater than the energy consumption demand, the result is marked as "sufficient load" for the section, and the corresponding surplus is recorded;

[0172] If the available load is not higher than the energy consumption demand, it is marked as "insufficient load", and the insufficient part is recorded as energy consumption difference information of the section;

[0173] Finally, all the energy consumption difference information of the sections is sorted in time sequence to form energy consumption difference data which can be used for subsequent conflict identification.

[0174] In a preferred embodiment of the present application, according to the energy consumption difference data, sections with energy consumption difference lower than safety margin are recorded as energy side conflict data, which specifically includes:

[0175] First, an acceptable energy consumption difference range is set for each energy consumption difference record to determine whether an energy side conflict is formed;

[0176] Subsequently, the energy consumption difference data is read piece by piece, and each piece of data is compared with the preset safety margin range;

[0177] When the residual load amount represented by the energy consumption difference is continuously lower than the safety margin range, the section is marked as an energy side conflict section;

[0178] If the residual load amount represented by the energy consumption difference is greater than the safety margin range, the section is marked as an energy side bearable section;

[0179] In the judgment process, if a plurality of consecutive sections are in a state of insufficient load, the energy side conflict section is recorded as a whole section to avoid excessive fragmentation of the conflict result;

[0180] Finally, all sections determined to be energy side conflicts are sorted to form energy side conflict data as input for subsequent conflict fusion processing.

[0181] In a preferred embodiment of the present application, the emission constraint data is extracted from the task constraint data by section, and compared with the available emission quota of the emission section to generate emission difference data, which specifically includes:

[0182] First, the emission impact amount and emission pressure information corresponding to each execution section are read from the task constraint data, which are used as the basic data of the section emission constraint;

[0183] Subsequently, the emission permit value corresponding to the execution section is extracted from the available emission quota data of the emission section;

[0184] The emission permit value is compared with the emission impact amount to determine in text form whether the emission permit value is sufficient to support task execution;

[0185] When the emission permit value is greater than the emission impact amount, the section is marked as an emission permit sufficient section;

[0186] When the emission permit value is not higher than the emission impact amount, the section is marked as an emission restricted section, and the emission difference is recorded as basic information for subsequent conflict judgment;

[0187] Finally, the emission difference information and restricted state of all sections are sorted in chronological order to form the emission difference data.

[0188] In a preferred embodiment of the present application, the section with an emission difference lower than the emission threshold is identified according to the emission difference data, and is recorded as emission side conflict data, which specifically includes:

[0189] First, set an emission safety threshold for the emission difference, for judging whether the emission permit is up to standard;

[0190] Subsequently, the emission difference data is judged by section, identifying sections with insufficient emission permits;

[0191] If the emission difference indicates that the available emission amount is continuously lower than the emission threshold, the section is recorded as an emission-side conflict section;

[0192] If the emission difference indicates that there is still a surplus within the emission permit range, the section is recorded as an emission-side loadable section;

[0193] A plurality of continuous emission-side conflict sections are combined and processed to participate in subsequent fusion judgment as a whole conflict section;

[0194] The sorted emission-side conflict data is used as input for subsequent composite conflict judgment.

[0195] In a preferred embodiment of the present application, conflict fusion processing is performed according to energy-side conflict data and emission-side conflict data, comprehensive judgment is performed on data with both types of conflicts, composite conflict data is generated, and specifically includes:

[0196] First, the energy-side conflict data and the emission-side conflict data are aligned in time sequence, so that the two types of conflict data have comparability at the section level;

[0197] Subsequently, it is judged for each section whether it is marked as a conflict in both the energy-side conflict data and the emission-side conflict data;

[0198] When a time period is shown as a limited state in both types of conflict data, the section is marked as a composite conflict section;

[0199] If a time period only has a conflict on the energy side and not on the emission side, it is kept as an energy-side single conflict section; if only the emission side has a conflict, it is kept as an emission-side single conflict section;

[0200] The composite conflict section is further recorded for its conflict source and overlapping properties, so that the subsequent adjustment process can select the most suitable adjustment method according to the degree of composite conflict;

[0201] Finally, composite conflict data is formed, which is used to generate final conflict classification data.

[0202] In a preferred embodiment of the present application, energy-side conflict data, emission-side conflict data, and composite conflict data are combined to generate conflict classification data, specifically including:

[0203] First, sort the three types of conflict data in time sequence, and align the time ranges of the conflict sections of each type;

[0204] Subsequently, the time period belonging to the composite conflict section is marked as a composite conflict type in priority to avoid repeated recording;

[0205] For sections not marked as composite conflicts, check whether they belong to energy side conflicts or emission side conflicts, and mark the two types of single conflicts as energy side conflict types or emission side conflict types respectively;

[0206] Merge consecutive conflict sections of the same type to make the conflict sections as a whole have continuous characteristics, facilitating subsequent adjustment modules for processing;

[0207] Finally, the merged conflict type, conflict time range and corresponding conflict degree are integrated into structured conflict classification data, which is provided for subsequent adjustment steps.

[0208] In a preferred embodiment of the present application, the collaborative adjustment data including alternative execution sections, section splitting schemes, device switching schemes or load redistribution schemes are generated according to the conflict classification data, including:

[0209] According to the conflict classification data, the energy side conflict section is identified, and candidate alternative execution section data is generated according to the time section with higher available load in the energy section;

[0210] According to the conflict classification data, the emission side conflict section is identified, and emission alternative execution section data is generated according to the time section with more relaxed emission constraints;

[0211] According to the conflict classification data, the composite conflict section is identified, and its execution section is divided into multiple sub-sections according to the continuity rule according to the task's splittable attribute, to generate section splitting data;

[0212] According to the task and device correspondence in the conflict classification data, alternative devices that can reduce energy consumption or reduce emission impact are screened out to generate device switching data;

[0213] According to the load difference and capacity relationship between tasks, load adjustment processing is performed on the energy consumption proportion of adjacent tasks to generate load redistribution data;

[0214] The candidate alternative execution section data, emission alternative execution section data, section splitting data, device switching data and load redistribution data are merged to generate collaborative adjustment data.

[0215] In the embodiments of the present application, different types of resource conflicts can be decomposed into operation objects that can be processed by generating alternative execution segments, segment splitting schemes, device switching schemes or load redistribution schemes according to the conflict classification data. For energy side conflicts, by identifying time segments with higher available load, new execution locations can be provided for the task, so that the pressure of resource shortage period is alleviated. For emission side conflicts, by selecting segments with more relaxed emission constraints, task execution can be completed without breaking the emission permit. For composite conflicts, by splitting the task execution segment into multiple shorter sub-segments, task execution can be flexibly distributed within the segment where energy or emission conditions change, reducing the centralized pressure on the system. In addition, by selecting alternative devices according to the correspondence between the task and the device, the task can be executed on a more suitable device, thereby reducing the overall load or emission, and load redistribution processing can be performed by adjusting the energy consumption proportion between adjacent tasks, so that the load of the task presents a more gentle distribution. The generation of the above-mentioned multiple adjustment methods makes the task have more feasible execution schemes under resource limitation conditions, and improves the scheduling flexibility.

[0216] In a preferred embodiment of the present application, the energy side conflict segment is identified according to the conflict classification data, and candidate alternative execution segment data is generated according to the time segment with higher available load in the energy segment, specifically including:

[0217] First, read the time segment marked as energy side conflict from the conflict classification data, and record the start time, end time and conflict degree of the segment;

[0218] Subsequently, retrieve other time segments different from the energy side conflict segment from the available load data of the energy segment, and read the available load level of these segments;

[0219] According to the available load level, mark the time segment with available load higher than the set reference load value as a potential segment suitable for carrying the task;

[0220] Sort these potential segments according to their start and end times, continuity and available load, and select time segments that meet the task execution time length requirement as candidate alternative execution segments;

[0221] Finally, the data structure of the candidate alternative execution segment is stored, including its start and end times, available load and potential adaptation of the task in the segment, providing input for subsequent selection of adjustment methods.

[0222] In a preferred embodiment of the present application, the emission side conflict segment is identified according to the conflict classification data, and the emission alternative execution segment data is generated according to the time segment with more relaxed emission constraints, specifically including:

[0223] Firstly, read the time section belonging to the emission side conflict from the conflict classification data, and obtain the emission limit information of the section, including the emission pressure value and the emission permission situation;

[0224] Subsequently, select other time sections different from the emission side conflict section from the available emission quota data of the emission section, and read the emission permission values of these sections;

[0225] According to the emission permission value, mark the time section whose permission value is higher than the emission impact required for task execution as a section with relatively loose emission conditions;

[0226] Screen the sections with loose conditions according to the emission permission value, continuity and task execution time, and select the emission alternative execution sections available for executing the task;

[0227] Finally, record the selected emission alternative execution sections as emission alternative execution section data, including the emission permission value, executable time length and adaptation reason of the section, to provide a basis for subsequent decision-making.

[0228] In a preferred embodiment of the present application, the composite conflict section is identified according to the conflict classification data, and the execution section of the task is divided into multiple sub-sections according to the continuity rule according to the splittable attribute of the task, to generate section splitting data, specifically including:

[0229] Firstly, read the section marked as a composite conflict from the conflict classification data, and confirm that the section has resource limitations on both the energy side and the emission side;

[0230] Subsequently, read the splittable attribute of the task from the task structured data, including whether the task allows splitting, whether splitting affects subsequent processes, and the minimum splittable execution time;

[0231] On the premise that the task is splittable, divide the composite conflict section according to the time granularity of the unified time axis, and divide it into multiple continuous but shorter sub-sections;

[0232] Check the pressure situation of each sub-section on the energy side and the emission side, and preferentially mark the sub-section with lower pressure as an executable sub-section, and mark the sub-section with higher pressure as a to-be-adjusted sub-section;

[0233] Finally, combine the start and end times, classification results and splitting reasons of all sub-sections to form section splitting data for subsequent adjustment.

[0234] In a preferred embodiment of the present application, according to the task and device correspondence relationship in the conflict classification data, the alternative device that can reduce energy consumption or reduce emission impact is screened out, and device switching data is generated, specifically including:

[0235] Firstly, the device identifier used by the task in the conflict section is obtained from the conflict classification data, and the energy consumption characteristics and emission characteristics of the device are obtained;

[0236] Subsequently, candidate devices with the same or compatible functions as the original device are retrieved from the device library, and the energy consumption levels, emission characteristics, and operation capability information of the candidate devices are read;

[0237] The energy consumption characteristics of the candidate devices are compared with the energy consumption pressure of the original device in the conflict section, and devices that can reduce the energy consumption demand are screened out;

[0238] Meanwhile, the emission characteristics of the candidate devices are compared with the emission pressure of the original device in the same section, and devices that can reduce the emission impact are screened out;

[0239] If some candidate devices can simultaneously reduce the energy consumption pressure and the emission pressure, they are marked as preferred replacement devices;

[0240] Finally, the screening results are combined to generate device switching data, including device identifier, execution capability, and adaptation reason, which provides input for subsequent generation of collaborative adjustment data.

[0241] In a preferred embodiment of the present application, according to the time alignment section data, the correspondence between the energy consumption basic section data and the emission basic section data in terms of time coverage and identifier matching degree is compared, and the sections that meet the matching conditions are combined to generate mapping section data, including:

[0242] According to the time alignment section data, the start time, end time, and corresponding device identifier and process identifier of the energy consumption basic section data are extracted to generate energy consumption section feature data;

[0243] According to the time alignment section data, the start time, end time, and corresponding emission source identifier of the emission basic section data are extracted to generate emission section feature data;

[0244] According to the comparison of energy consumption section feature data and emission section feature data, the time overlap length of the two types of sections is generated to generate coverage degree determination data representing the time coverage relationship;

[0245] According to the coverage degree determination data, the sections with time overlap length reaching the preset coverage condition are formed into time matching section data;

[0246] According to the time matching section data, the correlation degree between the device identifier, process identifier, and emission source identifier is compared to generate identifier matching determination data;

[0247] According to the identifier matching determination data, the sections with correlation degree reaching the matching condition are combined to form the mapping section data.

[0248] In the embodiments of the present application, by comparing the time coverage and the identity matching degree of the energy consumption basic section and the emission basic section according to the time alignment section data, the two types of sections originally independent of each other can be combined into a mapping section with logical correlation. The determination of the time coverage can ensure that the two types of sections have a cross time in actual operation, so that the generated mapping section can reflect the real energy consumption and emission change relationship. The comparison of the identity matching degree can further determine whether the device identity, the process identity and the emission source identity have business correlation, so that the mapping relationship not only based on time overlap, but also has business logic meaning. Through the double screening of the time overlap data and the identity matching data, the final mapping section can more accurately reflect the influence of energy consumption change on emission change. As an important part of the energy emission correlation data, the mapping section can provide reliable input for task constraint generation, so that the influence of the task execution on the energy system and the emission system can be judged based on real data.

[0249] In a preferred embodiment of the present application, the start time and the end time of the energy consumption basic section data and the corresponding device identity and process identity are extracted from the time alignment section data to generate energy consumption section feature data, specifically including:

[0250] Firstly, the section set containing the energy consumption basic section is located from the time alignment section data, and the start time and the end time of each section are read;

[0251] Subsequently, the device identity information recorded in each section is extracted, for example, the device number, device type and the like corresponding to the energy consumption record;

[0252] Then, the process identity information is extracted from the same section, including the production process name or process number corresponding to the energy consumption section;

[0253] The start time, end time, device identity and process identity are combined into a complete energy consumption section feature record;

[0254] Finally, the feature records of all sections are arranged in time sequence or device sequence to form the energy consumption section feature data for subsequent matching processing.

[0255] In a preferred embodiment of the present application, the start time and the end time of the emission basic section data and the corresponding emission source identity are extracted from the time alignment section data to generate emission section feature data, specifically including:

[0256] Firstly, the section containing the emission basic section is read from the time alignment section data, and the start time and the end time of the section are extracted, so that the emission change interval can be accurately located;

[0257] Subsequently, the emission source identification information is extracted from the section, including the name of the emission source unit, the equipment number or the emission process category;

[0258] Next, the start time, the end time and the emission source identification of the section are combined into an emission section feature record;

[0259] Then, all the extracted feature records are sorted and arranged, so that the sections of the same emission source are more easily compared in subsequent processing;

[0260] Finally, the sorted emission section feature records are used as emission section feature data for subsequent time coverage comparison and identification matching degree analysis.

[0261] In a preferred embodiment of the present application, according to the time overlap length of the two types of sections compared by the energy consumption section feature data and the emission section feature data, coverage determination data representing the time coverage relationship is generated, specifically including:

[0262] First, the energy consumption section feature data and the emission section feature data are arranged in time sequence respectively, so that the two lists have comparability in the time dimension;

[0263] Subsequently, each energy consumption section and emission section is compared two by two to determine whether there is an intersection interval between the start time and the end time;

[0264] When it is found that the time ranges of the two sections overlap, the overlap duration is calculated according to the start time and the end time of the overlap; this calculation is achieved by judging the degree of overlap of the two time ranges, without using mathematical formulas;

[0265] Next, the overlap duration is compared with the preset time coverage requirement (such as reaching a certain duration), and if the condition is met, the section is recorded as a section with "time coverage satisfied";

[0266] If the overlap duration is insufficient, it is recorded as a section with "insufficient time coverage" for subsequent filtering;

[0267] Finally, all coverage situations are summarized in order according to the section to form coverage determination data.

[0268] In a preferred embodiment of the present application, according to the coverage determination data, the sections with time overlap length reaching the preset coverage condition are formed into time matching section data, specifically including:

[0269] First, all the sections marked as "time coverage satisfied" are selected according to the coverage determination data;

[0270] Then, the start time, the end time, the corresponding energy consumption section feature and the emission section feature of the sections are combined so that each time matching section can reflect the common coverage of the energy consumption section and the emission section;

[0271] Next, the time matching sections are adjusted according to the granularity of the unified time axis so that all the sections can keep consistent time scales with other sections;

[0272] Then, it is checked whether the adjacent time matching sections have continuity, and if they can be combined into a longer continuous section, they are combined as a time matching record;

[0273] Finally, the processed time matching sections are sorted in order to form time matching section data and provide input for the next identification matching.

[0274] In a preferred embodiment of the present application, the identification matching judgment data is generated by comparing the correlation degree between the equipment identification, the process identification and the emission source identification according to the time matching section data, and specifically includes:

[0275] First, the equipment identification, the process identification and the emission source identification contained in each record of the time matching section data are extracted;

[0276] Then, the identifications are logically compared in the business layer, for example, it is judged whether the equipment belongs to the equipment sequence generating the emission source, and whether the process usually accompanies the emission activity;

[0277] Then, the correlation degree is divided into levels such as "strong correlation", "weak correlation" or "no correlation" according to the comparison results, and the comparison level of each section is recorded;

[0278] The sections with higher correlation degree are marked as "identification matching satisfied", and the sections with weaker correlation degree are marked as "identification matching not satisfied";

[0279] Finally, the correlation judgment results of all the sections are combined in time sequence to form the identification matching judgment data, which is used as the basis for generating the mapping section data in the next step.

[0280] In a preferred embodiment of the present application, the mapping section data is combined according to the identification matching judgment data when the correlation degree reaches the matching condition, and specifically includes:

[0281] First, all the sections marked as "identification matching satisfied" are screened out from the identification matching judgment data;

[0282] Then, the energy consumption features and the emission features of the sections are combined into a mapping record with clear corresponding relationship;

[0283] Then, the continuous eligible segments are merged to enable the correspondence to cover a longer time range, so as to improve the continuity of subsequent correlation analysis.

[0284] Then, the mapping segments are arranged in chronological order, so that the overall mapping presents a stable time sequence structure.

[0285] Finally, all the mapping segments are output as mapping segment data, which provides input for generating energy emission correlation data.

[0286] In a preferred embodiment of the present application, according to the relationship between the load difference and the production capacity of tasks, load adjustment processing is performed on the energy consumption proportion of adjacent tasks to generate load redistribution data, including:

[0287] According to the energy consumption demand and the execution segment of the adjacent tasks, task load feature data is extracted to generate initial load data for load comparison;

[0288] According to the initial load data, the load difference between adjacent tasks is compared, and the task segment whose load difference meets the adjustment condition is marked as adjustable segment data;

[0289] According to the adjustable segment data, load reduction processing is performed on the energy consumption proportion of high-load tasks to generate reduced load data, and the remaining load after reduction is used as input for compensation processing;

[0290] According to the adjustable segment data, load compensation processing is performed on low-load tasks, a compensation ratio is allocated according to the reduced load data, and compensation load data is generated;

[0291] According to the reduced load data and the compensation load data, load balancing processing is performed, and the two are combined in chronological order and task order to generate load redistribution data.

[0292] In the embodiment of the present application, by performing load adjustment processing on adjacent tasks according to the relationship between the load difference and the production capacity of tasks, the task load can be redistributed under resource limitation conditions. By extracting task load feature data and identifying adjustable segments, it can be determined whether the task has load transfer conditions in time, so that the adjustment process has a basis. Load reduction processing is performed on high-load tasks to reduce their load proportion in resource-intensive segments; and load compensation processing is performed on low-load tasks to transfer part of the load to more suitable tasks, so that the distribution of resource use on the time axis is more balanced. Through load balancing processing of the reduced load data and the compensation load data, a more reasonable task load combination can be formed. The finally generated load redistribution data can reduce the concentrated occupation of energy and emission resources between tasks, so that the task execution presents a continuous and gentle resource demand curve as a whole, improving the feasibility and execution efficiency of the adjustment data.

[0293] In a preferred embodiment of the present application, the task load characteristic data is extracted according to the energy consumption requirement of adjacent tasks and the execution section, and initial load data for load comparison is generated, specifically including:

[0294] Firstly, two adjacent tasks in time sequence are selected from the task structured data, and the resource requirement of each task is read, including the energy consumption requirement of the task during the execution period;

[0295] Subsequently, the predetermined execution section corresponding to the two adjacent tasks is read, and the execution start time, end time and time span of each task are recorded;

[0296] Then, the energy consumption requirement and the execution time section are combined, so that each task corresponds to a "task load unit", which contains the average load level and time range of the task during the execution period;

[0297] Then, for the purpose of comparison, the two task load units are arranged in execution order to form initial load data;

[0298] Finally, the initial load data is used as the input for the next step of judging the load difference, so that the difference in energy use of the tasks is comparable.

[0299] In a preferred embodiment of the present application, the load difference between adjacent tasks is compared according to the initial load data, and the task section that meets the adjustment condition is marked as adjustable section data, specifically including:

[0300] Firstly, the load level of adjacent tasks is read from the initial load data, and the load size of the two is compared to determine the size of the load difference in a textual manner;

[0301] Subsequently, the load difference is compared with the preset load adjustment threshold value, and when the difference is higher than the threshold value, it is considered that the difference has reached the condition that load adjustment can be performed;

[0302] When the judgment result is adjustable, the task execution section is marked as an adjustable section, and the corresponding relationship between the task with high load and the task with low load is recorded;

[0303] When the load difference does not reach the threshold value, the section is marked as an unadjustable section;

[0304] Finally, all the data marked as adjustable sections are summarized to form adjustable section data, which is used as the input for subsequent load reduction and compensation processing.

[0305] In a preferred embodiment of the present application, the energy consumption proportion of the high-load task is adjusted according to the adjustable section data, the reduced load data is generated, and the remaining load after reduction is used as the input for compensation processing, specifically including:

[0306] Firstly, read the load level of the high-load task from the adjustable section data, and identify the load part that needs to be reduced;

[0307] Subsequently, determine the amount of reducible load according to the length of the execution section where the high-load task is located and the size of the load difference, which is determined by a textual description rather than a formula;

[0308] Next, adjust the load level of the high-load task by the reducible proportion, and record the reduced load value as "remaining load";

[0309] At the same time, record the reduced part as "reduced load data", and mark the remaining load as the input of the subsequent load compensation step;

[0310] Finally, structure the reduced load data and the remaining load data for storage, providing a basis for the next step of compensating for low-load tasks.

[0311] In a preferred embodiment of the present application, load compensation processing is performed on low-load tasks according to the adjustable section data, and a compensation proportion is allocated according to the reduced load data to generate compensation load data, which specifically includes:

[0312] Firstly, read the actual load level of the low-load task from the adjustable section data, and confirm its ability to receive load compensation, such as having remaining energy carrying space in the current execution section;

[0313] Subsequently, obtain the reduced load data from the previous processing step, and determine the compensable amount according to the loadable space of the low-load task;

[0314] Next, according to factors such as the importance of the task, the execution priority, or the process relationship, allocate the reduced load to the low-load task in a certain proportion;

[0315] Record the load level after compensation, and take the increased load part as compensation load data;

[0316] Finally, take the compensation load data as the input of the subsequent load balancing processing, so that the overall task load presents a more balanced distribution.

[0317] In a preferred embodiment of the present application, load balancing processing is performed according to the reduced load data and the compensation load data, and the two are combined in chronological order and task order to generate load redistribution data, which specifically includes:

[0318] Firstly, sort the reduced load data and the compensation load data according to the time axis of the execution task, so that the load change can be arranged according to the actual execution order of the task;

[0319] Then, the execution section corresponding to the load reduction is compared with the execution section corresponding to the load compensation to ensure that the load reduction and the load compensation have reasonable continuity and time feasibility;

[0320] Next, the load adjustment information of each section is combined to achieve load balance between the reduction part and the compensation part as a whole;

[0321] For the case of continuous sections for some task execution, the load change is further integrated to combine the excessive fragmentation adjustment into continuous adjustment sections;

[0322] Finally, the load balance result is recorded as load redistribution data, which is used to generate collaborative adjustment data to make the load between tasks more reasonable.

[0323] Embodiments of the present application also provide an online monitoring and collaborative control system for energy and carbon emissions, which comprises:

[0324] A data acquisition module is configured to acquire energy time series data, emission time series data and production plan time series data, and perform time granularity unification and timestamp calibration processing to generate unified time axis data;

[0325] A segmentation processing module is configured to segment the energy time series data according to equipment identification, process identification and load section based on the unified time axis data, segment the emission time series data according to emission source identification and emission section, and establish a mapping relationship between energy consumption segments and emission segments through segmentation correspondence to generate energy emission association data;

[0326] A plan matching module is configured to structure the production plan time series data according to the energy emission association data, convert production task identification, scheduled execution section and resource demand into a unified format, and match the structured production plan time series data with the energy emission association data according to the unified time axis in each section to generate task constraint data;

[0327] A conflict identification module is configured to compare energy consumption demand with energy section available load, and compare emission expectation with emission section available emission capacity, identify resource conflict sections through section comparison, and generate conflict classification data according to the limit type;

[0328] An adjustment data generation module is configured to generate collaborative adjustment data including alternative execution section, section splitting scheme, equipment switching scheme or load redistribution scheme according to the conflict classification data;

[0329] An adjustment instruction generation module is configured to determine the applicable adjustment mode according to the conflict classification data, and generate plan adjustment instructions based on the collaborative adjustment data;

[0330] A plan update module is configured to send plan adjustment instructions to the production plan subsystem and receive adjusted production plan time series data to support continued co-management of energy, emissions, and production plans.

[0331] It should be noted that the system is a system corresponding to the above method, all the implementation manners in the above method embodiment are applicable to this embodiment, and the same technical effects can be achieved.

[0332] Embodiments of the application also provide a computing device, comprising: a processor, a memory storing a computer program, when the computer program is run by the processor, the method as described above is executed. All the implementation manners in the above method embodiment are applicable to this embodiment, and the same technical effects can be achieved.

[0333] Embodiments of the application also provide a computer readable storage medium, storing instructions, when the instructions are run on a computer, the computer executes the method as described above. All the implementation manners in the above method embodiment are applicable to this embodiment, and the same technical effects can be achieved.

[0334] The above is the preferred embodiment of the application, it should be noted that for those skilled in the art, without departing from the principles of the application, a number of improvements and refinements can be made, these improvements and refinements should also be considered as the protection scope of the application.

Claims

1. An online monitoring and collaborative management method for energy and carbon emissions, characterized in that, The method comprises: acquiring energy time series data, emission time series data and production plan time series data, and performing time granularity unification and timestamp calibration processing to generate unified time axis data; According to the unified time axis data, the energy time series data is segmented according to the equipment identifier, the process identifier and the load section, the emission time series data is segmented according to the emission source identifier and the emission section, and the mapping relationship between the energy consumption section and the emission section is established through the segmentation corresponding mode, to generate energy emission correlation data; According to the energy emission correlation data, the production plan time series data is structured, the production task identifier, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched according to the unified time axis, to generate task constraint data; According to the task constraint data, the energy consumption demand and the available load of the energy section are compared, and the emission expectation and the available emission capacity of the emission section are compared, the resource conflict section is identified through section comparison, and the conflict classification data is generated according to the restriction type; According to the conflict classification data, the collaborative adjustment data including the alternative execution section, the section splitting scheme, the equipment switching scheme or the load redistribution scheme is generated; According to the conflict classification data, the applicable adjustment mode is determined, and the plan adjustment instruction is generated based on the collaborative adjustment data, specifically including: Read the conflict type information from the conflict classification data, including energy side conflict, emission side conflict or composite conflict, and confirm the task section that needs to be adjusted according to the starting time period, the duration and the conflict degree of the conflict; According to the conflict type, the applicable adjustment mode is determined, the alternative execution section or the load redistribution is preferred in energy side conflict, the emission alternative section or the equipment switching is preferred in emission side conflict, and the section splitting mode is preferred in composite conflict; Read the adjustment content corresponding to the selected adjustment mode from the collaborative adjustment data; According to the execution order of the task in the original plan, the plan time window and the resource usage, the adjustment content is compared with the current production task list to form the adjustment parameter that can directly modify the production plan; The adjustment parameter is packaged into a structured plan adjustment instruction, so that the production plan subsystem can modify the task execution section, the execution order or the execution equipment according to the adjustment parameter; The plan adjustment instruction is sent to the production plan subsystem, and the adjusted production plan time series data is received to support the continuous collaborative management and control of energy, emission and production plan. 2.The method of claim 1, wherein, According to the unified time axis data, the energy time series data is segmented according to the equipment identifier, the process identifier and the load section, the emission time series data is segmented according to the emission source identifier and the emission section, and the mapping relationship between the energy consumption section and the emission section is established through the segmentation corresponding mode, to generate energy emission correlation data, including: The energy time series data is analyzed point by point, and the segmented division processing is performed on the analysis result according to the equipment identifier, the process identifier and the load upper and lower limit, to generate energy consumption basic section data; The time point-by-time point analysis is performed on the emission time series data, the section division processing is performed on the analysis result according to the emission source identification and the emission change trend, and emission basic section data is generated; According to the unified time axis data, the energy consumption basic section data and the emission basic section data are time-aligned to generate time-aligned section data; According to the time-aligned section data, the corresponding relationship of the energy consumption basic section data and the emission basic section data in time coverage and identification matching degree is compared, and the section that meets the matching condition is combined to generate mapping section data; According to the mapping section data, the energy emission correlation data is output as the input of the subsequent generation of task constraint data. 3.The method of claim 1, wherein, According to the energy emission correlation data, the production plan time series data is structured, the production task identification, the scheduled execution section and the resource demand are converted into a unified format, and the structured production plan time series data and the energy emission correlation data are matched by section according to the unified time axis to generate task constraint data, including: According to the production plan time series data, the production task identification, the scheduled execution time and the resource demand are extracted and converted into task structured data in a unified format; According to the time section execution time mapping processing of the energy emission correlation data and the task structured data, task time mapping data is generated; According to the task time mapping data, the energy consumption demand in the task structured data and the energy consumption range in the energy emission correlation data are associated and calculated by section to generate energy consumption constraint data; According to the task time mapping data, the emission impact in the task structured data and the emission range in the energy emission correlation data are associated and calculated by section to generate emission constraint data; According to the constraint synthesis processing of the energy consumption constraint data and the emission constraint data, the task constraint data is generated. 4.The method of claim 1, wherein, According to the task constraint data, the energy consumption demand and the energy section available load are compared, and the emission expectation and the emission section available emission capacity are compared, the resource conflict section is identified through section comparison, and the conflict classification data is generated according to the limit type, including: According to the task constraint data, the energy consumption constraint data is extracted by section, and the difference value comparison is performed with the energy section available load to generate energy consumption difference value data; According to the energy consumption difference value data, the section with the energy consumption difference value lower than the safety margin is recorded as the energy side conflict data; According to the task constraint data, the emission constraint data is extracted by section, and the difference value comparison is performed with the emission section available emission capacity to generate emission difference value data; According to the emission difference value data, the section with the emission difference value lower than the emission threshold is recorded as the emission side conflict data; According to the conflict fusion processing of the energy side conflict data and the emission side conflict data, the comprehensive judgment is performed on the data that simultaneously exists two types of conflicts to generate composite conflict data; The energy side conflict data, the emission side conflict data and the composite conflict data are merged to generate conflict classification data. 5.The method of claim 1, wherein, According to the conflict classification data, the collaborative adjustment data including the alternative execution section, the section splitting scheme, the equipment switching scheme or the load redistribution scheme is generated, including: According to the conflict classification data, identify the energy side conflict section, and generate candidate alternative execution section data according to the time section in which the available load in the energy section is higher than the set reference load value; According to the conflict classification data, identify the emission side conflict section, and generate emission alternative execution section data according to the time section in which the emission constraint meets the emission impact required by task execution; According to the conflict classification data, identify the composite conflict section, and divide the execution section of the task according to the continuity rule according to the split attribute of the task to generate section splitting data; According to the task and device correspondence relationship in the conflict classification data, filter out alternative devices that can reduce energy consumption or reduce emission impact, and generate device switching data; According to the load difference and production capacity relationship between tasks, perform load adjustment processing on the energy consumption proportion of adjacent tasks to generate load redistribution data; Merge the candidate alternative execution section data, emission alternative execution section data, section splitting data, device switching data and load redistribution data to generate collaborative adjustment data. 6.The method of claim 2, wherein, According to the time alignment section data, compare the corresponding relationship of the energy consumption basic section data and the emission basic section data in time coverage and identification matching degree, and combine the sections that meet the matching conditions to generate mapping section data, including: According to the time alignment section data, extract the start time, end time, and corresponding device identifier and process identifier of the energy consumption basic section data to generate energy consumption section feature data; According to the time alignment section data, extract the start time, end time, and corresponding emission source identifier of the emission basic section data to generate emission section feature data; According to the comparison of energy consumption section feature data and emission section feature data, the time overlap length of the two types of sections is generated. Coverage judgment data representing the time coverage relationship is generated; According to the coverage judgment data, the sections with time overlap length reaching the preset coverage condition are formed into time matching section data; According to the time matching section data, compare the correlation degree between the device identifier, process identifier and emission source identifier to generate identification matching judgment data; According to the identification matching judgment data, the sections that meet the matching condition are combined to form the mapping section data. 7.The method of claim 5, wherein, According to the load difference and production capacity relationship between tasks, perform load adjustment processing on the energy consumption proportion of adjacent tasks to generate load redistribution data, including: According to the energy demand and execution section of adjacent tasks, task load feature data is extracted to generate initial load data for load comparison; According to the initial load data, compare the load difference between adjacent tasks, and mark the task section with load difference reaching the adjustment condition as adjustable section data; According to the adjustable section data, perform load reduction processing on the energy consumption proportion of high load tasks to generate reduced load data, and the remaining load after reduction is used as the input of compensation processing; According to the adjustable section data, perform load compensation processing on the low load task, and according to the reduced load data, allocate the compensation proportion to generate compensation load data; According to the reduced load data and compensation load data, perform load balancing processing, combine them in time sequence and task sequence to generate load redistribution data.

8. An online monitoring and collaborative management system for energy and carbon emissions, characterized in that, The system is applied to the method of any one of claims 1-7, and comprises: a data acquisition module configured to acquire energy time series data, emission time series data, and production plan time series data, and perform time granularity unification and timestamp calibration processing to generate unified time axis data; a segmentation processing module configured to perform segmentation processing on the energy time series data according to equipment identification, process identification, and load segment based on the unified time axis data, perform segmentation processing on the emission time series data according to emission source identification and emission segment, and establish a mapping relationship between energy consumption segments and emission segments through segmentation correspondence to generate energy emission association data; a plan matching module configured to perform structured processing on the production plan time series data according to the energy emission association data, convert production task identification, scheduled execution segment, and resource demand into a unified format, and match the structured production plan time series data with the energy emission association data according to a unified time axis and by segment to generate task constraint data; a conflict identification module configured to compare energy consumption demand with available load of energy segments and compare emission expectation with available emission capacity of emission segments according to the task constraint data, identify resource conflict segments through segment comparison, and generate conflict classification data according to restriction types; an adjustment data generation module configured to generate collaborative adjustment data including alternative execution segments, segment splitting schemes, equipment switching schemes, or load redistribution schemes according to the conflict classification data; an adjustment instruction generation module configured to determine applicable adjustment methods according to the conflict classification data, and generate plan adjustment instructions based on the collaborative adjustment data; a plan updating module configured to send the plan adjustment instructions to a production plan subsystem, and receive adjusted production plan time series data to support continuous collaborative management and control of energy, emissions, and production plans.

9. A computing device, comprising: comprise: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-7.

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

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