Intelligent scheduling management system and method for green energy storage transformation

Abstract

The invention belongs to the technical field of energy management, and provides an intelligent scheduling management system and method for green energy storage transformation. The method comprises the following steps: in a management interface, generating a production overview based on manual setting of a user; connecting the production overview with each production device; calculating the residual time length between the current moment and the end moment of the production cycle, determining a prediction strategy according to the residual time length, and predicting energy consumption prediction data corresponding to the production overview according to the prediction strategy based on the actual working parameters and the connection relationship of each production device; and outputting the energy consumption prediction data and deviation data between the energy consumption prediction data and the energy consumption plan of the production cycle formulated by the user to the user. According to the invention, enterprises can be helped to know the energy consumption trend in the production process in advance, production tasks can be arranged reasonably, unnecessary energy waste is reduced, and green and energy-saving production is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an intelligent dispatching management system and method for green energy storage transformation. Background Art

[0002] With the acceleration of industrialization, as the main place of energy consumption, the effectiveness of energy management in factories is increasingly critical to reducing operating costs and achieving sustainable development. At present, although most factories have realized the importance of energy management and taken certain measures, there are still many problems in the formulation and management of energy consumption plans.

[0003] Existing energy management methods can often only achieve simple collection and statistics of energy consumption data, and lack systematic planning tools. When formulating energy consumption plans, factories often rely on rough estimates of manual experience and historical data, and are unable to accurately combine complex factors such as the factory's actual production process and equipment operating characteristics, resulting in poor scientificity and rationality of the plan. During the execution of the plan, due to the lack of effective real-time monitoring and dynamic adjustment mechanisms, once there are emergencies such as changes in production tasks and equipment failures, energy consumption plans will be difficult to adapt, resulting in energy waste or insufficient supply, seriously affecting production efficiency and economic benefits.

[0004] Therefore, how to efficiently manage the energy consumption of the factory to achieve low-cost and efficient operation of the factory is a technical problem that needs to be solved. Summary of the invention

[0005] In this regard, the present invention provides an intelligent scheduling management method, system, electronic device, computer storage medium and computer program product for green energy storage transformation to solve the above technical problems.

[0006] The present invention discloses an intelligent scheduling management method for green energy storage transformation, which includes the following steps: in a management interface, generating a production overview based on a user's manual settings, wherein the production overview includes a connection relationship between various production equipment based on a production process, and energy consumption parameters of various production equipment; connecting the production overview with various production equipment; calculating the remaining time between the current moment and the end moment of this production cycle, determining a prediction strategy based on the remaining time, and predicting energy consumption prediction data corresponding to the production overview according to the prediction strategy based on actual working parameters of each production equipment and the connection relationship; and outputting the energy consumption prediction data and its deviation data from the energy consumption plan for this production cycle formulated by the user to the user.

[0007] In some embodiments, the prediction strategy determined based on the remaining time includes: retrieving a pre-constructed first conversion relationship and a second conversion relationship, matching and calculating the remaining time with the first conversion relationship, and obtaining a corresponding first prediction period; determining the number of energy types for formulating the energy consumption plan, and obtaining an adjustment period based on the number of energy types and the second conversion relationship, subtracting the first prediction period from the adjustment period, and obtaining a second prediction period; wherein the remaining time in the first conversion relationship is negatively correlated with the first prediction period, and the number of energy types in the second conversion relationship is positively correlated with the adjustment period; and formulating the prediction strategy based on the second prediction period.

[0008] In some embodiments, the method also includes: calling a pre-built third conversion relationship; then deriving the adjustment period according to the number of energy types and the second conversion relationship, including: deriving the first adjustment period according to the number of energy types and the second conversion relationship; counting the number of production equipment in the production overview, and deriving the second adjustment period according to the number of equipment and the third conversion relationship; wherein the number of equipment in the third conversion relationship is negatively correlated with the second adjustment period; and taking the sum of the first adjustment period and the second adjustment period as the adjustment period.

[0009] In some embodiments, the energy consumption plan is determined by at least one of the following methods: converting the planned energy consumption amounts of various energy types into monetary terms, and accumulating the converted monetary values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into calorific values, and accumulating the converted calorific value values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into carbon emissions, and accumulating the converted carbon emission values ​​to obtain a corresponding energy consumption plan.

[0010] In some embodiments, based on the actual working parameters of each production equipment and the connection relationship, energy consumption forecast data corresponding to the production overview is predicted according to the prediction strategy, including: calling a prediction model, the prediction model is pre-trained using a training data set; according to the second prediction cycle, controlling the prediction model to predict the actual working parameters of each production equipment and the connection relationship, and obtaining energy consumption forecast data corresponding to the production overview; the energy consumption forecast data refers to the predicted value of the total energy consumption of all production equipment in the production overview during this production cycle.

[0011] An embodiment of the present invention also discloses an intelligent scheduling and management system for green energy storage transformation, the system comprising an overview construction module, an energy consumption prediction module, and an output module; the overview construction module is used to generate a production overview based on the user's manual settings in the management interface, the production overview including the connection relationship between various production equipment based on the production process, and the energy consumption parameters of each production equipment; the production overview is connected to each production equipment; the energy consumption prediction module is used to calculate the remaining time between the current moment and the end moment of this production cycle, determine a prediction strategy based on the remaining time, and predict energy consumption prediction data corresponding to the production overview according to the prediction strategy based on the actual working parameters of each production equipment and the connection relationship; the output module is used to output the energy consumption prediction data and its deviation data from the energy consumption plan for this production cycle formulated by the user to the user.

[0012] In some embodiments, the energy consumption prediction module is used to: retrieve the pre-constructed first conversion relationship and second conversion relationship, match and calculate the remaining time with the first conversion relationship, and obtain the corresponding first prediction period; determine the number of energy types for formulating the energy consumption plan, and obtain the adjustment period according to the number of energy types and the second conversion relationship, and subtract the first prediction period from the adjustment period to obtain the second prediction period; wherein the remaining time in the first conversion relationship is negatively correlated with the first prediction period, and the number of energy types in the second conversion relationship is positively correlated with the adjustment period; and formulate the prediction strategy according to the second prediction period.

[0013] In some embodiments, the energy consumption prediction module is used to: also call a pre-built third conversion relationship; then the adjustment period is obtained according to the number of energy types and the second conversion relationship, including: obtaining a first adjustment period according to the number of energy types and the second conversion relationship; counting the number of production equipment in the production overview, and obtaining a second adjustment period according to the number of equipment and the third conversion relationship; wherein the number of equipment in the third conversion relationship is negatively correlated with the second adjustment period; and taking the sum of the first adjustment period and the second adjustment period as the adjustment period.

[0014] In some embodiments, the energy consumption plan is determined by at least one of the following methods: converting the planned energy consumption amounts of various energy types into monetary terms, and accumulating the converted monetary values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into calorific values, and accumulating the converted calorific value values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into carbon emissions, and accumulating the converted carbon emission values ​​to obtain a corresponding energy consumption plan.

[0015] In some embodiments, the energy consumption prediction module is used to: call up a prediction model, which is pre-trained using a training data set; according to the second prediction cycle, control the prediction model to predict the actual working parameters of each production equipment and the connection relationship, and obtain energy consumption prediction data corresponding to the production overview; the energy consumption prediction data refers to the predicted value of the total energy consumption of all production equipment in the production overview during this production cycle.

[0016] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in any of the preceding items.

[0017] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0018] The present invention also discloses a computer program product. When the computer program product is executed by an electronic device, any of the above methods is implemented.

[0019] The beneficial effects of the present invention are: (1) by integrating the connection relationship and energy consumption parameters of production equipment and predicting energy consumption, it can help enterprises understand the energy consumption trend in the production process in advance, facilitate the reasonable arrangement of production tasks, reduce unnecessary energy waste, and achieve green and energy-saving production.

[0020] (2) The output of energy consumption forecast data and energy consumption plan deviation data provides users with clear feedback on production energy consumption, which helps users to adjust production strategies in a timely manner, improve the level of refinement of production management, optimize production processes, and thus improve the overall production efficiency and economic benefits of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a flow chart of an intelligent dispatching management method for green energy storage transformation disclosed in an embodiment of the present invention.

[0023] Figure 2 It is a schematic diagram of the production overview and editing process disclosed in the embodiment of the present invention.

[0024] Figure 3 It is a structural schematic diagram of an intelligent dispatching and management system for green energy storage transformation disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0026] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0027] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent scheduling management method for green energy storage transformation, the method comprising the following steps: S10, in a management interface, generating a production overview based on a user's manual settings, the production overview including a connection relationship between various production equipment based on a production process, and energy consumption parameters of each production equipment; connecting the production overview with each production equipment.

[0028] Specifically, Figure 2 As shown in the figure, in the management interface, users can manually add production equipment involved in production one by one according to the actual production situation, and connect these equipment one by one according to the production process; then, input the energy consumption parameters of each production equipment, which include the energy consumption of production equipment under different operating conditions, such as power, power consumption per unit time, etc. The production overview generated in this way covers multiple key information of the production process. The connection relationship between each production equipment shows the architecture of the entire production system and clarifies the upstream and downstream logical relationship between the equipment. For example, in a manufacturing production process, raw material processing equipment is connected to semi-finished product assembly equipment, and then to finished product packaging equipment.

[0029] After the setup is completed, the production overview is connected to each production equipment, so that the scheduling management system can obtain the operating data of all production equipment in the production overview in real time and perform analysis for various purposes.

[0030] S20, calculate the remaining time between the current moment and the end moment of this production cycle, determine a prediction strategy based on the remaining time, and based on the actual working parameters of each production equipment and the connection relationship, predict the energy consumption prediction data corresponding to the production overview according to the prediction strategy.

[0031] Specifically, the remaining time between the current moment and the end moment of this production cycle is first calculated. The present invention is configured to determine a suitable prediction strategy based on the remaining time to support the probability of achieving the energy consumption plan formulated by the user. Then, predictions are made based on the actual working parameters of each production equipment (such as the real-time operating speed of the equipment, work intensity, etc.) and the connection relationship between them. In this way, energy consumption forecast data corresponding to the entire production overview is predicted according to the selected prediction strategy, that is, the total energy consumption of the entire production system in this production cycle is predicted. Obviously, the total energy consumption is composed of the known actual energy consumption and the predicted energy consumption within the remaining time.

[0032] The connection relationship between production equipment can be used to analyze the working status of the corresponding equipment, and then determine more accurate energy consumption. For example, upstream equipment A manufactures semi-finished products, and downstream equipment B transmits the semi-finished products through a conveyor belt. When equipment A stops manufacturing, the operating load of equipment B gradually decreases, and the energy consumption of equipment B in the next period of time will be relatively reduced.

[0033] S30, outputting the energy consumption forecast data and the deviation data between the energy consumption forecast data and the energy consumption plan for the current production cycle formulated by the user to the user.

[0034] Specifically, the predicted energy consumption forecast data is displayed to the user so that the user can understand the expected energy consumption. At the same time, the deviation data between the energy consumption forecast data and the energy consumption plan for this production cycle formulated by the user is calculated. For example, the energy consumption plan for this production cycle formulated by the user is 1000 kWh, and the predicted energy consumption data is 1200 kWh, then the deviation data is 200 kWh. These two data are output to the user so that the user can adjust and optimize the production based on this information to increase the probability of achieving the energy consumption plan.

[0035] By integrating the connection relationship of production equipment and energy consumption parameters and forecasting energy consumption, enterprises can understand the energy consumption trend in the production process in advance, arrange production tasks reasonably, reduce unnecessary energy waste, and achieve green and energy-saving production. Secondly, the output of energy consumption forecast data and energy consumption plan deviation data provides users with clear production energy consumption feedback, which helps users to adjust production strategies in time, improve the refinement of production management, optimize production processes, and thus improve the overall production efficiency and economic benefits of the enterprise.

[0036] In some embodiments, the prediction strategy determined based on the remaining time includes: retrieving a pre-constructed first conversion relationship and a second conversion relationship, matching and calculating the remaining time with the first conversion relationship, and obtaining a corresponding first prediction period; determining the number of energy types for formulating the energy consumption plan, and obtaining an adjustment period based on the number of energy types and the second conversion relationship, subtracting the first prediction period from the adjustment period, and obtaining a second prediction period; wherein the remaining time in the first conversion relationship is negatively correlated with the first prediction period, and the number of energy types in the second conversion relationship is positively correlated with the adjustment period; and formulating the prediction strategy based on the second prediction period.

[0037] In the embodiment of the present invention, the first conversion relationship and the second conversion relationship mentioned above are pre-constructed.

[0038] Among them, the first conversion relationship is used to describe the corresponding relationship between the remaining time and the first prediction cycle. This corresponding relationship presents a negative correlation characteristic, which means that as the remaining time increases (that is, the current moment is closer to the beginning of the current production cycle), the first prediction cycle is shortened accordingly, so that a higher frequency of energy consumption forecast can be made in the early stage of the current production cycle, so as to timely discover the trend of energy consumption exceeding the standard, and then timely adjust and optimize the production status of the production equipment to improve the probability of achieving the energy consumption plan; and as the remaining time decreases (that is, the current moment is closer to the end of the current production cycle), the first prediction cycle is correspondingly extended, because when entering the middle and late stages of the current production cycle, the actual effect of adjusting and optimizing the production status of the production equipment to improve the probability of achieving the energy consumption plan gradually becomes lower. In other words, the remaining adjustment time is already difficult to achieve a significant reduction in energy consumption. At this time, a lower frequency of energy consumption forecast is performed, thereby reducing the data processing load. The first conversion relationship is, for example: for every hour the remaining time is reduced, the first prediction cycle is increased by 0.2 hours.

[0039] In the actual production process of the factory, multiple energy types may be involved, such as electricity, natural gas, coal, etc. The user may need to plan and control the total energy consumption including all energy types. At the same time, different energy types correspond to different types of production equipment, and the difficulty and effect of "adjustment and optimization" of different types of production equipment are also quite different. Therefore, the present invention is further configured to determine the adjustment period for appropriately lowering the above-mentioned first prediction period according to the number of energy types involved in the above-mentioned energy plan. Specifically: First, the number of energy types involved in the above-mentioned energy consumption plan is statistically formulated, and the pre-constructed second conversion relationship describes the positive correlation between the adjustment period and the number of energy types. Based on this positive correlation, the more energy types there are, the longer the corresponding adjustment period is set, so that the first prediction period can be shortened to a greater extent, that is, a higher frequency of energy consumption prediction is performed; the fewer energy types there are, the shorter the corresponding adjustment period is set, so that the first prediction period can be shortened to a smaller extent, that is, a lower frequency of energy consumption prediction is performed. The second conversion relationship is, for example: for each additional energy type, the adjustment period increases by 0.1 hours.

[0040] The first prediction period obtained by the first conversion relationship is subtracted from the adjustment period obtained according to the number of energy types and the second conversion relationship to obtain the second prediction period. For example, if the first prediction period is 3 hours and the adjustment period is 0.2 hours, then the second prediction period is 2.8 hours.

[0041] The second prediction period of the present invention comprehensively considers two factors: the remaining time and the number of energy types. Compared with the first prediction period obtained only based on the remaining time, it is more in line with the energy consumption prediction needs under complex production scenarios, and can provide a more reasonable time period setting for subsequent prediction of energy consumption data according to the selected prediction strategy, thereby increasing the probability of achieving the energy consumption plan.

[0042] In some embodiments, the method also includes: calling a pre-built third conversion relationship; then deriving the adjustment period according to the number of energy types and the second conversion relationship, including: deriving the first adjustment period according to the number of energy types and the second conversion relationship; counting the number of production equipment in the production overview, and deriving the second adjustment period according to the number of equipment and the third conversion relationship; wherein the number of equipment in the third conversion relationship is negatively correlated with the second adjustment period; and taking the sum of the first adjustment period and the second adjustment period as the adjustment period.

[0043] In the embodiment of the present invention, the above considerations the number of energy types to determine the appropriate adjustment cycle. This embodiment further considers the number of production equipment added in the production overview, that is, the total number of equipment that can be used for adjustment and optimization to determine the appropriate adjustment cycle. The more the number of production equipment in the production overview, that is, the number of objects targeted for adjustment and optimization, the easier it is to formulate an optimal adjustment optimization strategy with a smaller adjustment range for more equipment, so that the interference with production equipment can be minimized under the premise of reducing energy consumption. At this time, a lower frequency prediction is adapted; and the fewer the number of production equipment in the production overview, that is, the number of objects targeted for adjustment and optimization, the more difficult it is to formulate an optimal adjustment optimization strategy because only limited equipment can be adjusted, and these equipment are likely to not support adjustment (for example, unable to slow down production) or have limited adjustment effects (small energy consumption reduction within a unit time), and at this time, a higher frequency prediction is adapted to discover the trend of excessive energy consumption in advance and intervene as soon as possible. Based on the above situation, the present invention is set: similar to the first conversion relationship and the second conversion relationship mentioned above, the present invention also pre-constructs a third conversion relationship, which is used to describe the relationship between the number of production equipment added in the production overview and the second adjustment period, and this relationship is negatively correlated. After the production overview is formulated and finally confirmed and activated, the scheduling management system counts the total number of production equipment added in the production overview, that is, the above-mentioned equipment quantity, and then converts the second adjustment period corresponding to the equipment quantity based on the above-mentioned third conversion relationship. Finally, the first adjustment period and the second adjustment period are added together to finally obtain the above-mentioned adjustment period.

[0044] The first regulation period obtained by the number of energy types and the second conversion relationship is summed with the second regulation period obtained by the number of production equipment and the third conversion relationship, and the sum obtained is the final regulation period.

[0045] It should be noted that the above-mentioned first conversion relationship, second conversion relationship, and third conversion relationship preset in the present invention can all be expressed in the form of a comparison table or a fitting function. In addition, an analysis model can also be constructed accordingly, and the analysis model can be used to predict and analyze the remaining time, the number of energy types, and the number of devices obtained in real time, so as to obtain the corresponding first prediction period, the first adjustment period, and the second adjustment period, that is, the conversion relationship is packaged as an analysis model. The analysis model is preferably based on an existing deep learning algorithm, such as Transformer, recurrent neural network, etc.

[0046] In some embodiments, the energy consumption plan is determined by at least one of the following methods: converting the planned energy consumption amounts of various energy types into monetary terms, and accumulating the converted monetary values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into calorific values, and accumulating the converted calorific value values ​​to obtain a corresponding energy consumption plan; converting the planned energy consumption amounts of various energy types into carbon emissions, and accumulating the converted carbon emission values ​​to obtain a corresponding energy consumption plan.

[0047] In an embodiment of the present invention, the present invention is configured to use a unified energy consumption plan to represent the total planned energy consumption of all energy forms in the factory. This requires a reasonable method to convert the planned energy consumption of different energy sources to a unified scale, and then perform a sum calculation. Specifically: Monetary conversion to determine the energy consumption plan: Based on the prices of various energy sources on the market, the planned energy consumption of different energy sources is converted into monetary values, and then these monetary values ​​are accumulated to finally obtain the total energy consumption plan. In this way, the factory can plan and manage energy consumption plans from an intuitive perspective of economic cost.

[0048] Determine energy consumption plan by calorific value conversion: Based on the characteristics of different energy sources with different calorific values, the energy consumption plan of various energy sources is uniformly converted into calorific value units based on standard coal. Through this conversion, the energy consumption of different energy sources can be quantified into calorific value values ​​under the same standard, which is convenient for overall consideration and management.

[0049] For example, the calorific value conversion relationship of electric energy is: 1 kWh of electric energy is equivalent to 3600 kilojoules of energy, the calorific value of standard coal is 29307.6 kilojoules / kilogram; the calorific value range of natural gas (35-55 megajoules / cubic meter) and the calorific value range of different types of coal (such as thermal coal 20-30 megajoules / kilogram).

[0050] In some embodiments, based on the actual working parameters of each production equipment and the connection relationship, energy consumption forecast data corresponding to the production overview is predicted according to the prediction strategy, including: calling a prediction model, the prediction model is pre-trained using a training data set; according to the second prediction cycle, controlling the prediction model to predict the actual working parameters of each production equipment and the connection relationship, and obtaining energy consumption forecast data corresponding to the production overview; the energy consumption forecast data refers to the predicted value of the total energy consumption of all production equipment in the production overview during this production cycle.

[0051] In an embodiment of the present invention, the present invention pre-builds and trains a prediction model, which can be pre-embedded in a scheduling management system. The training data set contains a large number of data samples about the actual working parameters of each production equipment (such as equipment operating speed, working intensity, energy consumption values ​​in different time periods, etc.) and equipment connection relationships (reflecting the upstream and downstream logical associations between equipment in the production process), as well as label data of the actual total energy consumption in the production cycle. By learning these data samples, the prediction model gradually grasps the relationship between the working status of the equipment and the energy consumption, as well as the influence of the equipment connection relationship on the energy consumption.

[0052] According to the second prediction cycle obtained above, the prediction model is controlled to perform multiple predictions. The model uses the actual working parameters of each production equipment and the equipment connection relationship as input information to predict the total energy consumption forecast value of all production equipment in the production overview during this production cycle, that is, to obtain the energy consumption forecast data corresponding to the production overview. The above actual working parameters include the real-time parameters of each production equipment and the historical parameters or historical total energy consumption between the start time and the current time of this production cycle.

[0053] The energy consumption forecast data obtained above can help enterprises understand the energy consumption trend in the production process in advance, provide an important basis for subsequent production scheduling and energy management decisions, and help enterprises to reasonably arrange production tasks, optimize energy use, reduce unnecessary energy waste, and achieve green and energy-saving production.

[0054] like Figure 3 As shown, an embodiment of the present invention also discloses an intelligent scheduling and management system for green energy storage transformation, the system comprising an overview construction module, an energy consumption prediction module, and an output module; the overview construction module is used to generate a production overview based on the user's manual settings in the management interface, the production overview including the connection relationship between each production equipment based on the production process, and the energy consumption parameters of each production equipment; the production overview is connected to each production equipment; the energy consumption prediction module is used to calculate the remaining time between the current moment and the end moment of this production cycle, determine a prediction strategy based on the remaining time, and predict the energy consumption prediction data corresponding to the production overview according to the prediction strategy based on the actual working parameters of each production equipment and the connection relationship; the output module is used to output the energy consumption prediction data and its deviation data from the energy consumption plan for this production cycle formulated by the user to the user.

[0055] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0056] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0057] The embodiment of the present invention further discloses a computer program product, which, when executed by an electronic device, implements any of the above methods.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of methods, systems (devices) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. An intelligent dispatching management method for green energy storage transformation, characterized in that: The method includes the following steps: in a management interface, generating a production overview based on a user's manual settings, wherein the production overview includes a connection relationship between various production equipment based on a production process, and energy consumption parameters of various production equipment; connecting the production overview with various production equipment; calculating the remaining time between the current moment and the end moment of this production cycle, determining a prediction strategy based on the remaining time, and predicting energy consumption prediction data corresponding to the production overview according to the prediction strategy based on actual working parameters of each production equipment and the connection relationship; and outputting the energy consumption prediction data and its deviation data from the energy consumption plan for this production cycle formulated by the user to the user.

2. According to claim 1, a smart dispatching management method for green energy storage transformation is characterized by: The prediction strategy is determined based on the remaining time, including: retrieving a pre-constructed first conversion relationship and a second conversion relationship, matching and calculating the remaining time with the first conversion relationship, and obtaining a corresponding first prediction period; determining the number of energy types for formulating the energy consumption plan, obtaining an adjustment period based on the number of energy types and the second conversion relationship, subtracting the first prediction period from the adjustment period, and obtaining a second prediction period; wherein the remaining time in the first conversion relationship is negatively correlated with the first prediction period, and the number of energy types in the second conversion relationship is positively correlated with the adjustment period; and formulating the prediction strategy based on the second prediction period.

3. According to claim 2, a smart dispatching management method for green energy storage transformation is characterized by: The method also includes: calling a pre-constructed third conversion relationship; then deriving the adjustment period according to the number of energy types and the second conversion relationship, including: deriving the first adjustment period according to the number of energy types and the second conversion relationship; counting the number of production equipment in the production overview, and deriving the second adjustment period according to the number of equipment and the third conversion relationship; wherein the number of equipment in the third conversion relationship is negatively correlated with the second adjustment period; and taking the sum of the first adjustment period and the second adjustment period as the adjustment period.

4. The intelligent dispatching management method for green energy storage transformation according to claim 1 is characterized in that: The energy consumption plan is determined by at least one of the following methods: converting the energy consumption plan amounts of various energy types into monetary terms, and accumulating the converted monetary values ​​to obtain the corresponding energy consumption plan; Convert the planned energy consumption of various energy types into calorific values, and add up the converted calorific values ​​to obtain the corresponding energy consumption plan; The energy consumption plans of various energy types are converted into carbon emissions, and the converted carbon emission values ​​are accumulated to obtain the corresponding energy consumption plan.

5. An intelligent dispatching management method for green energy storage transformation according to any one of claims 1 to 3, characterized in that: Based on the actual working parameters of each production equipment and the connection relationship, energy consumption forecast data corresponding to the production overview is predicted according to the prediction strategy, including: calling a prediction model, the prediction model is fully trained in advance using a training data set; according to the second prediction cycle, controlling the prediction model to predict the actual working parameters of each production equipment and the connection relationship, and obtaining energy consumption forecast data corresponding to the production overview; the energy consumption forecast data refers to the predicted value of the total energy consumption of all production equipment in the production overview during this production cycle.

6. An intelligent dispatching and management system for green energy storage transformation, characterized by: The system includes an overview construction module, an energy consumption prediction module, and an output module; the overview construction module is used to generate a production overview based on the manual settings of the user in the management interface, wherein the production overview includes the connection relationship between various production equipment based on the production process, and the energy consumption parameters of each production equipment; the production overview is connected to each production equipment; the energy consumption prediction module is used to calculate the remaining time between the current moment and the end moment of the current production cycle, determine a prediction strategy based on the remaining time, and predict the energy consumption prediction data corresponding to the production overview according to the prediction strategy based on the actual working parameters of each production equipment and the connection relationship; The output module is used to output the energy consumption forecast data and the deviation data between the energy consumption forecast data and the energy consumption plan for the current production cycle formulated by the user to the user.

7. The intelligent dispatching and management system for green energy storage transformation according to claim 6 is characterized by: The energy consumption prediction module is used to: retrieve the pre-built first conversion relationship and the second conversion relationship, match and calculate the remaining time with the first conversion relationship, and obtain the corresponding first prediction period; Determine the number of energy types for formulating the energy consumption plan, derive an adjustment period based on the number of energy types and a second conversion relationship, subtract the first prediction period from the adjustment period to obtain a second prediction period; wherein the remaining time in the first conversion relationship is negatively correlated with the first prediction period, and the number of energy types in the second conversion relationship is positively correlated with the adjustment period; formulate the prediction strategy based on the second prediction period.

8. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 5.

9. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

10. A computer program product, when the computer program product is executed by an electronic device, characterized in that: This enables the method described in any one of claims 1 to 5 to be implemented.

Images