An intelligent scheduling management system and method for green energy storage transformation

Abstract

The present 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 includes: generating an overall production overview based on manual settings of a user in a management interface; connecting the overall production overview to each production device; calculating the remaining duration between the current moment and the end moment of the current production cycle, determining a prediction strategy according to the remaining duration, and predicting energy consumption prediction data corresponding to the overall production overview based on the actual working parameters and connection relationships of each production device according to the prediction strategy; and outputting the energy consumption prediction data and the deviation data between the energy consumption prediction data and the energy consumption plan of the current production cycle formulated by the user to the user. The present invention 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.

Description

Technical Field

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

[0002] With the acceleration of the industrialization process, factories, as the main places of energy consumption, the effectiveness of their energy management is becoming increasingly crucial for reducing operating costs and achieving sustainable development. Currently, although most factories have recognized 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 means can often only achieve simple collection and statistics of energy consumption data, lacking systematic planning tools. When formulating energy consumption plans, factories mostly rely on manual experience and rough estimation of historical data, and cannot accurately combine complex factors such as the actual production process and equipment operation characteristics of the factory, resulting in poor scientificity and rationality of the plans. During the plan execution process, due to the lack of effective real-time monitoring and dynamic adjustment mechanisms, once sudden situations such as production task changes and equipment failures occur, the energy consumption plan is difficult to adapt, thus causing energy waste or insufficient supply, seriously affecting production efficiency and economic benefits.

[0004] Therefore, how to facilitate the efficient management of the energy consumption situation of factories to achieve low-cost and high-efficiency operation of factories is a technical problem that needs to be solved currently. Summary of the Invention

[0005] In view of this, 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, and the method includes the following steps: in the management interface, generate a production overview based on the manual settings of the user, where the production overview includes the connection relationships between various production devices based on the production process and the energy consumption parameters of each production device; connect the production overview to each production device; calculate the remaining duration between the current moment and the end moment of the current production cycle, determine a prediction strategy based on the remaining duration, 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 device and the connection relationships; output the energy consumption prediction data and the deviation data between it and the energy consumption plan of the current production cycle formulated by the user to the user.

[0007] In some embodiments, determining the prediction strategy based on the remaining duration includes: retrieving a pre-constructed first conversion relationship and a second conversion relationship, performing a matching calculation on the remaining duration and the first conversion relationship to obtain a corresponding first prediction period; determining the number of energy types for which the energy consumption plan is formulated, obtaining an adjustment period based on the number of energy types and the second conversion relationship, and performing a subtraction operation on the first prediction period and the adjustment period to obtain a second prediction period; wherein the remaining duration 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 further includes: also retrieving a pre-constructed third conversion relationship; then, obtaining the adjustment period based on the number of energy types and the second conversion relationship includes: obtaining a first adjustment period based on the number of energy types and the second conversion relationship; counting the number of production devices in the production overview, and obtaining a second adjustment period based on the number of devices and the third conversion relationship; wherein the number of devices in the third conversion relationship is negatively correlated with the second adjustment period; and taking the sum value 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: performing monetization conversion on the energy consumption plan amounts of various energy types, accumulating the converted monetary values to obtain the corresponding energy consumption plan; performing calorific value conversion on the energy consumption plan amounts of various energy types, accumulating the converted calorific value values to obtain the corresponding energy consumption plan; performing carbon emission conversion on the energy consumption plan amounts of various energy types, accumulating the converted carbon emission values to obtain the corresponding energy consumption plan.

[0010] In some embodiments, based on the actual working parameters of each production device and the connection relationship, predicting the energy consumption prediction data corresponding to the production overview according to the prediction strategy includes: retrieving a prediction model that has been fully trained in advance using a training data set; controlling the prediction model to predict the actual working parameters of each production device and the connection relationship according to the second prediction period to obtain the 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 devices in the production overview during the current production cycle.

[0011] The embodiment of the present invention also discloses an intelligent scheduling management system for green energy storage transformation. 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. The production overview includes the connection relationship between production devices based on the production process and the energy consumption parameters of each production device. Connect the production overview to each production device. The energy consumption prediction module is used to calculate the remaining duration between the current moment and the end moment of the current production cycle, determine a prediction strategy based on the remaining duration, and predict energy consumption prediction data corresponding to the production overview based on the actual working parameters of each production device and the connection relationship according to the prediction strategy. The output module is used to output the energy consumption prediction data and the deviation data between the energy consumption prediction data and the energy consumption plan of the current 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, perform a matching calculation on the remaining duration and the first conversion relationship to obtain a corresponding first prediction period; determine the number of energy types for formulating the energy consumption plan, obtain an adjustment period according to the number of energy types and the second conversion relationship, and perform a subtraction operation on the first prediction period and the adjustment period to obtain a second prediction period. Wherein, the remaining duration 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 according to the second prediction period.

[0013] In some embodiments, the energy consumption prediction module is used to: also retrieve the pre-constructed third conversion relationship; then the obtaining the adjustment period according to the number of energy types and the second conversion relationship includes: obtaining a first adjustment period according to the number of energy types and the second conversion relationship; counting the number of production devices in the production overview, and obtaining a second adjustment period according to the number of devices and the third conversion relationship. Wherein, the number of devices in the third conversion relationship is negatively correlated with the second adjustment period; use the sum value 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: perform a monetization conversion on the energy consumption plan amounts of various energy types, accumulate the converted currency values to obtain a corresponding energy consumption plan; perform a calorific value conversion on the energy consumption plan amounts of various energy types, accumulate the converted calorific value values to obtain a corresponding energy consumption plan; perform a carbon emission conversion on the energy consumption plan amounts of various energy types, accumulate the converted carbon emission values to obtain a corresponding energy consumption plan.

[0015] In some embodiments, the energy consumption prediction module is configured to: retrieve a prediction model that has been fully trained in advance using a training data set; control the prediction model to predict the actual working parameters of each production device and the connection relationship according to the second prediction period, 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 devices in the production overview during the current production cycle.

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

[0017] The present invention also discloses a computer storage medium, where the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in any one of the preceding items.

[0018] The present invention also discloses a computer program product, which, when run on an electronic device, enables the implementation of the method as described in any one of the preceding items.

[0019] The beneficial effects of the present invention are as follows: (1) Through the integration of the connection relationship of production devices and energy consumption parameters and energy consumption prediction, 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 the energy consumption prediction data and the energy consumption plan deviation data provides clear production energy consumption feedback for users, helps users adjust production strategies in a timely manner, improves the refinement degree of production management, optimizes the production process, and further enhances 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 following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic flowchart of an intelligent scheduling 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 its editing process disclosed in an embodiment of the present invention.

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

[0025] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

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

[0027] As Figure 1 shown, an embodiment of the present invention discloses an intelligent scheduling management method for green energy storage transformation. The method includes the following steps: S10, in the management interface, generate an overall production overview based on the manual settings of the user. The overall production overview includes the connection relationships between various production devices based on the production process, and the energy consumption parameters of each production device; connect the overall production overview to each production device.

[0028] Specifically, as Figure 2 shown, in the management interface, the user can manually add one by one the production devices involved in production according to the actual production situation, and connect these devices one by one according to the production process; then, input the corresponding energy consumption parameters of each production device. The energy consumption parameters include the energy consumption of the production device in different operating states, such as power, power consumption per unit time, etc. The overall production overview generated in this way covers multiple key information of the production process. The connection relationships between various production devices show the architecture of the entire production system, clarifying the upstream and downstream logical relationships between the devices. For example, in a manufacturing production process, the raw material processing device is connected to the semi-finished product assembly device, and then to the finished product packaging device, etc.

[0029] After the settings are completed, connect the overall production overview to each production device, so that the scheduling management system can obtain the operation data of all production devices in the overall production overview in real time and perform analysis for various purposes.

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

[0031] Specifically, first calculate the remaining duration between the current moment and the end moment of this production cycle. The present invention is configured to determine an appropriate prediction strategy based on this remaining duration to support the achievement probability of the energy consumption plan formulated by the user. Then, perform a prediction based on the actual working parameters of each production device (such as the real-time operating speed and working intensity of the device) and their connection relationships. In this way, according to the selected prediction strategy, the energy consumption prediction data corresponding to the overall production overview is predicted, that is, the total energy consumption of the entire production system within this production cycle is predicted. Obviously, the total energy consumption consists of the known actual energy consumption and the predicted energy consumption within the remaining duration.

[0032] Among them, the connection relationship between production devices can be used to analyze the working status of the corresponding devices, and thus determine a more accurate energy consumption. For example, the semi-finished product is manufactured by device A upstream, and device B downstream transports the semi-finished product through a conveyor belt. When device A pauses manufacturing, the operating load of device B gradually decreases, and at this time, the energy consumption of device B in the next period will decrease relatively significantly.

[0033] S30. Output the energy consumption prediction data and the deviation data between it and the energy consumption plan of this production cycle formulated by the user to the user.

[0034] Specifically, display the predicted energy consumption prediction data to the user so that the user can understand the expected energy consumption situation. At the same time, calculate the deviation data between the energy consumption prediction data and the energy consumption plan of this production cycle formulated by the user. For example, if the energy consumption plan of this production cycle formulated by the user is 1000 degrees of electricity, and the predicted energy consumption data is 1200 degrees of electricity, then the deviation data is 200 degrees of electricity. Output these two data to the user so that the user can adjust and optimize the production according to this information to improve the achievement probability of the energy consumption plan.

[0035] Through the integration of the connection relationship of production devices and energy consumption parameters and energy consumption prediction, 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. Secondly, the output of the energy consumption prediction data and the energy consumption plan deviation data provides clear production energy consumption feedback for the user, helps the user adjust the production strategy in a timely manner, improve the refinement degree of production management, optimize the production process, and thus improve the overall production efficiency and economic benefits of the enterprise.

[0036] In some embodiments, the prediction strategy determined according to the remaining duration includes: retrieving a pre-constructed first conversion relationship and a second conversion relationship, performing a matching calculation on the remaining duration and the first conversion relationship to obtain a corresponding first prediction period; determining the number of energy types for formulating the energy consumption plan, obtaining an adjustment period according to the number of energy types and the second conversion relationship, and performing a subtraction operation on the first prediction period and the adjustment period to obtain a second prediction period; wherein, the remaining duration 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; formulating the prediction strategy according to the second prediction period.

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

[0038] Among them, the first conversion relationship is used to describe the corresponding relationship between the remaining duration and the first prediction period. This corresponding relationship shows a negative correlation, which means that as the remaining duration increases (i.e., the current moment is closer to the start moment of this production cycle), the first prediction period correspondingly shortens, so that higher-frequency energy consumption predictions can be made in the early stage of this production cycle to timely detect the trend of energy consumption exceeding the standard, and then timely adjust and optimize the production state of production equipment to improve the achievement probability of the energy consumption plan; while as the remaining duration decreases (i.e., the current moment is closer to the end moment of this production cycle), the first prediction period correspondingly lengthens, because when entering the middle and late stages of this production cycle, the actual effect of adjusting and optimizing the production state of production equipment to improve the achievement probability of the energy consumption plan gradually becomes lower. In other words, it is very difficult to achieve a large reduction in energy consumption with the remaining adjustment time. At this time, lower-frequency energy consumption predictions are made, so as to reduce the data processing load. The first conversion relationship is, for example: it is set that for every 1-hour reduction in the remaining duration, the first prediction period increases by 0.2 hours.

[0039] In the actual production process of a factory, multiple energy types may be involved, such as electricity, natural gas, coal, etc. Users 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 also vary greatly. Therefore, the present invention further determines an adjustment period for appropriately reducing the above first prediction period according to the number of energy types involved in the above energy plan. Specifically: First, count the number of energy types involved in the above energy consumption plan formulated. 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 the number of energy types, the longer the corresponding adjustment period is set, so that the first prediction period can be shortened to a greater extent, that is, energy consumption prediction is performed at a higher frequency; the fewer the number of energy types, the shorter the corresponding adjustment period is set, so that the first prediction period can be shortened to a smaller extent, that is, energy consumption prediction is performed at a lower frequency. The second conversion relationship is, for example: for each additional energy type, the adjustment period increases by 0.1 hour.

[0040] Perform a subtraction operation on the first prediction period obtained through the first conversion relationship and the adjustment period obtained according to the number of energy types and the second conversion relationship, so as 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, namely the remaining duration and the number of energy types. Compared with the first prediction period obtained only based on the remaining duration, it better meets the energy consumption prediction requirements in complex production scenarios, can provide a more reasonable time period setting for predicting energy consumption data according to the selected prediction strategy subsequently, and makes the achievement probability of the energy consumption plan higher.

[0042] In some embodiments, the method further includes: also invoking a pre-constructed third conversion relationship; then the step of obtaining the adjustment period according to the number of energy types and the second conversion relationship includes: 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; taking the sum value of the first adjustment period and the second adjustment period as the adjustment period.

[0043] In an embodiment of the present invention, the above-mentioned appropriate adjustment period is determined by considering the number of energy types. This embodiment further determines the appropriate adjustment period by considering the total number of production devices added in the production overview, which is the total number of devices available for adjustment and optimization. When the number of production devices in the production overview, i.e., the number of objects targeted for adjustment and optimization, is larger, it is easier to formulate an optimal adjustment and optimization strategy with a smaller adjustment amplitude for more devices. This can minimize the degree of interference with production equipment while reducing energy consumption. At this time, a lower-frequency prediction is adapted. When the number of production devices in the production overview, i.e., the number of objects targeted for adjustment and optimization, is smaller, since only a limited number of devices can be adjusted, and these devices may not support adjustment (e.g., unable to reduce production speed) or the adjustment effect is limited (the reduction in energy consumption per unit time is small), it is more difficult to formulate an optimal adjustment and optimization strategy at this time. At this time, a higher-frequency prediction is adapted to detect the trend of excessive energy consumption in advance and intervene as early as possible. Based on the above situation, the present invention sets: Similarly to the above-mentioned first conversion relationship and second conversion relationship, the present invention also pre-constructs a third conversion relationship, which is used to describe the relationship between the number of production devices added in the production overview and the second adjustment period, and this relationship is negatively correlated. After the production overview is formulated, finalized, and activated, the scheduling management system counts the total number of production devices added in the production overview, i.e., the above-mentioned number of devices, and then calculates the corresponding second adjustment period 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 adjustment period obtained through the number of energy types and the second conversion relationship is added to the second adjustment period obtained through the number of production devices and the third conversion relationship. The sum value obtained is the final adjustment 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 represented in the form of a look-up table or a fitting function. In addition, an analysis model can be correspondingly constructed, and the analysis model is used to perform predictive analysis on the remaining duration, the number of energy types, and the number of devices obtained in real time, so as to obtain the corresponding first prediction period, first adjustment period, and second adjustment period, that is, the conversion relationship is packaged as an analysis model. The analysis model is preferably based on existing deep learning algorithms, 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: monetarily convert the energy consumption plan amounts of various energy types, accumulate the converted monetary values to obtain the corresponding energy consumption plan; calorifically convert the energy consumption plan amounts of various energy types, accumulate the converted calorific values to obtain the corresponding energy consumption plan; carbon emission convert the energy consumption plan amounts of various energy types, accumulate the converted carbon emission values to obtain the corresponding energy consumption plan.

[0047] In the embodiments of the present invention, the present invention is configured to use a unified energy consumption plan to represent the total energy consumption plan amount of all energy forms in the factory. This requires converting the energy consumption plan amounts of different energies into a unified scale in a reasonable manner and then performing a summation calculation. Specifically: Determining the energy consumption plan through monetization conversion: Based on the prices of various energies in the market, convert the energy consumption plan amounts of different energies into monetary values, and then accumulate these monetary values to finally obtain the total energy consumption plan. In this way, the factory can plan and manage the energy consumption plan from the intuitive perspective of economic costs.

[0048] Determining the energy consumption plan through calorific value conversion: Based on the characteristics that different energies have different calorific values, uniformly convert the energy consumption plan amounts of various energies into calorific value units based on standard coal. Through this conversion, the energy consumption of different energies can be quantified into calorific 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 kilowatt-hour of electric energy is equivalent to 3600 kilojoules of energy, and the calorific value of standard coal is taken as 29307.6 kilojoules / kilogram; the calorific value range of natural gas (35 - 55 megajoules / cubic meter) and the calorific value ranges of different coal varieties (such as 20 - 30 megajoules / kilogram for steam coal).

[0050] In some embodiments, based on the actual working parameters of each production device and the connection relationship, energy consumption prediction data corresponding to the production overview is predicted according to the prediction strategy, including: retrieving a prediction model that has been fully trained in advance using a training data set; controlling the prediction model to predict the actual working parameters of each production device and the connection relationship according to the second prediction period to 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 devices in the production overview during the current production cycle.

[0051] In an embodiment of the present invention, a prediction model is pre-constructed and trained, and the prediction model can be pre-embedded into the scheduling management system. The training data set includes a large number of data samples regarding the actual working parameters of each production device (such as device running speed, working intensity, energy consumption values at different time periods, etc.) and the device connection relationships (reflecting the upstream and downstream logical associations between devices in the production process), as well as the labeled data of the total actual energy consumption during the production cycle. By learning these data samples, the prediction model gradually masters the relationship between the device working state and energy consumption and the influence law of the device connection relationship on energy consumption.

[0052] According to the second prediction period obtained above, control the prediction model to perform multiple prediction tasks. The model takes the actual working parameters of each production device and the device connection relationship as input information, and predicts the predicted value of the total energy consumption of all production devices in the production overview during this production cycle, that is, obtains the energy consumption prediction data corresponding to the production overview. The above actual working parameters include the real-time parameters of each production device and the historical parameters or historical total energy consumption between the start time and the current time of this production cycle.

[0053] The obtained energy consumption prediction data 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, help enterprises reasonably arrange production tasks, optimize energy use, reduce unnecessary energy waste, and achieve green and energy-saving production.

[0054] As Figure 3 shown, an embodiment of the present invention also discloses an intelligent scheduling management system for green energy storage transformation. 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 in the management interface based on the manual settings of the user. The production overview includes the connection relationships between each production device based on the production process and the energy consumption parameters of each production device; connect the production overview to each production device; the energy consumption prediction module is used to calculate the remaining duration between the current time and the end time of this production cycle, determine the prediction strategy according to the remaining duration, and predict the energy consumption prediction data corresponding to the production overview based on the actual working parameters of each production device and the connection relationship according to the prediction strategy; the output module is used to output the energy consumption prediction data and the deviation data between it and the energy consumption plan of this production cycle formulated by the user to the user.

[0055] An embodiment of the present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and the processor executes the computer program to implement the method as described in the foregoing embodiments.

[0056] An embodiment of the present invention also discloses a computer storage medium, which stores a computer program. The computer program is executed by a processor to implement the method described in the foregoing embodiment.

[0057] An embodiment of the present invention also discloses a computer program product. When the computer program product is run on an electronic device, it enables the implementation of the method described in any of the foregoing.

[0058] The present invention is described with reference to the 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 flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0059] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0060] As described above, it is only a preferred embodiment of the present invention and is not used to limit the protection scope of the present invention.

Claims

1. An intelligent scheduling management method for green energy storage transformation, characterized in that: The method includes the following steps: In the management interface, generate an overall production overview based on the user's manual settings. The overall production overview includes the connection relationships between various production devices based on the production process, and the energy consumption parameters of each production device; connect the overall production overview to each production device; Calculate the remaining duration between the current moment and the end moment of the current production cycle, determine a prediction strategy based on the remaining duration, and predict the energy consumption prediction data corresponding to the overall production overview according to the prediction strategy based on the actual working parameters of each production device and the connection relationships; Output the energy consumption prediction data and the deviation data between it and the energy consumption plan of the current production cycle formulated by the user to the user; The determining the prediction strategy according to the remaining duration includes: Retrieve the pre-constructed first conversion relationship and second conversion relationship, perform a matching calculation between the remaining duration and the first conversion relationship, and obtain the corresponding first prediction period; Determine the number of energy types for which the energy consumption plan is formulated, obtain an adjustment period according to the number of energy types and the second conversion relationship, and perform a subtraction operation between the first prediction period and the adjustment period to obtain a second prediction period; wherein, the remaining duration 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; the energy types include electricity, natural gas, and coal; Formulate the prediction strategy according to the second prediction period; The method further includes: also invoking the pre-constructed third conversion relationship; then the obtaining the adjustment period according to the number of energy types and the second conversion relationship includes: Obtain a first adjustment period according to the number of energy types and the second conversion relationship; Count the number of production devices in the overall production overview, that is, the total number of devices that can be used for adjustment and optimization, and obtain a second adjustment period according to the number of devices and the third conversion relationship; wherein, the number of devices in the third conversion relationship is negatively correlated with the second adjustment period; Take the sum value of the first adjustment period and the second adjustment period as the adjustment period.

2. The intelligent scheduling management method for green energy storage transformation according to claim 1, wherein: The energy consumption plan is determined by at least one of the following methods: Perform a monetization conversion on the energy consumption plan amounts of various energy types, accumulate the converted currency values, and obtain the corresponding energy consumption plan; Perform a calorific value conversion on the energy consumption plan amounts of various energy types, accumulate the converted calorific value values, and obtain the corresponding energy consumption plan; Perform a carbon emission conversion on the energy consumption plan amounts of various energy types, accumulate the converted carbon emission values, and obtain the corresponding energy consumption plan.

3. The intelligent scheduling management method for green energy storage transformation according to claim 1 or 2, characterized in that: Predicting the energy consumption prediction data corresponding to the overall production overview according to the prediction strategy based on the actual working parameters of each production device and the connection relationships includes: Retrieve a prediction model, which has been fully trained in advance using a training data set; According to the second prediction period, control the prediction model to predict the actual working parameters of each production device 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 devices in the production overview during the current production cycle.

4. An intelligent scheduling management system for green energy storage transformation, characterized in that: 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 user's manual settings in the management interface. The production overview includes the connection relationship between each production device based on the production process and the energy consumption parameters of each production device; connect the production overview to each production device. The energy consumption prediction module is used to calculate the remaining duration between the current moment and the end moment of the current production cycle, determine a prediction strategy according to the remaining duration, and predict the energy consumption prediction data corresponding to the production overview based on the actual working parameters of each production device and the connection relationship according to the prediction strategy. The output module is used to output the energy consumption prediction data and the deviation data between it and the energy consumption plan of the current production cycle formulated by the user to the user. The energy consumption prediction module is used for: Retrieve the pre-constructed first conversion relationship and second conversion relationship, perform matching calculations on the remaining duration and the first conversion relationship, and obtain the corresponding first prediction period. Determine the number of energy types for which the energy consumption plan is formulated, obtain an adjustment period according to the number of energy types and the second conversion relationship, and perform a subtraction operation on the first prediction period and the adjustment period to obtain a second prediction period; wherein, the remaining duration 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; the energy types include electricity, natural gas, and coal. Formulate the prediction strategy according to the second prediction period. The energy consumption prediction module is used for: also call the pre-constructed third conversion relationship; then the obtaining the adjustment period according to the number of energy types and the second conversion relationship includes: Obtain a first adjustment period according to the number of energy types and the second conversion relationship. Count the number of production devices in the production overview, that is, the total number of devices that can be used for adjustment and optimization, and obtain a second adjustment period according to the number of devices and the third conversion relationship; wherein, the number of devices in the third conversion relationship is negatively correlated with the second adjustment period. Use the sum value of the first adjustment period and the second adjustment period as the adjustment period.

5. 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, characterized in that: the processor executes the computer program to implement the method according to any one of claims 1-3.

6. A computer storage medium, the computer-readable storage medium stores a computer program, characterized in that: The computer program is executed by the processor to implement the method according to any one of claims 1-3.

7. A computer program product, when the computer program product is run by an electronic device, is characterized in that: Enable the implementation of the method according to any one of claims 1-3.

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