A method and system for intelligent energy management of cloud-based platforms

Abstract

This application relates to the field of artificial intelligence technology, providing a method and system for intelligent energy consumption management using a cloud-based cutting platform. The method includes: acquiring customer steel plate cutting demand information through the cloud-based cutting platform; extracting elements and deploying cutting schemes from the customer steel plate cutting demand information to obtain steel plate cutting application scheme information; acquiring cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model to obtain energy consumption calculation and prediction information; adjusting the energy consumption calculation and prediction information based on data error factors to obtain closed-loop energy consumption calculation and prediction information; and performing energy utilization management based on the closed-loop energy consumption calculation and prediction information. This method can achieve precise management of energy consumption, improve energy consumption management efficiency, and thus ensure the rational and scientific utilization of energy.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an intelligent energy management method and system for cloud-based platforms. Background Technology

[0002] Steel plate cutting is a fundamental step in machinery manufacturing. With the development of artificial intelligence, to address serious issues such as excessive steel plate scrap, unstable orders, low equipment utilization, and capital tied up in inventory, an "Internet + Steel Plate Cutting Collaborative Manufacturing Sharing Platform," also known as a cloud cutting platform, has been proposed. Small and medium-sized metal processing enterprises can place orders on the platform, achieving personalized customization and obtaining high-precision, low-cost cut plates. Steel plate cutting involves various processing methods, and the cutting process is inextricably linked to energy consumption. Therefore, precise management of cutting energy consumption is necessary to ensure the rational and scientific use of energy.

[0003] However, existing steel plate cutting technology suffers from low energy consumption management efficiency, leading to energy waste. Summary of the Invention

[0004] Therefore, it is necessary to provide an intelligent energy management method and system for cloud-based platforms that can accurately manage energy consumption, improve energy consumption management efficiency, and thus ensure the rational and scientific use of energy, in order to address the aforementioned technical problems.

[0005] A method for intelligent energy management for a cloud-based steel plate cutting platform includes: acquiring customer steel plate cutting demand information through the cloud-based platform; extracting elements from the customer steel plate cutting demand information to obtain demand element information; deploying a cutting scheme based on the demand element information to obtain steel plate cutting application scheme information; acquiring cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model for prediction and calculation to obtain energy consumption calculation and prediction information; setting a data error factor based on historical energy consumption data processing experience; adjusting the energy consumption calculation and prediction information based on the data error factor to obtain energy consumption calculation and prediction closed-loop information; and managing energy utilization based on the energy consumption calculation and prediction closed-loop information.

[0006] An intelligent energy management system for a cloud-based steel plate cutting platform includes: a cutting demand information acquisition module for acquiring customer steel plate cutting demand information through the cloud-based platform; a demand element extraction module for extracting elements from the customer steel plate cutting demand information to obtain demand element information; a cutting scheme deployment module for deploying a cutting scheme based on the demand element information to obtain steel plate cutting application scheme information; a cutting parameter acquisition module for acquiring cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; a model prediction and calculation module for uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model for prediction and calculation to obtain energy consumption calculation and prediction information; an error factor setting module for setting data error factors based on historical energy consumption data processing experience; and an energy utilization management module for adjusting the energy consumption calculation and prediction information based on the data error factors to obtain energy consumption calculation and prediction closed-loop information, and performing energy utilization management based on the energy consumption calculation and prediction closed-loop information.

[0007] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0008] We obtain customer steel plate cutting needs information through the cloud cutting platform;

[0009] Extract elements from the customer's steel plate cutting requirements to obtain requirement element information;

[0010] Based on the aforementioned demand element information, a cutting scheme is deployed to obtain steel plate cutting application scheme information;

[0011] Based on the steel plate cutting application scheme information, obtain the cutting deployment equipment information and cutting application parameter information;

[0012] The cutting deployment equipment information and cutting application parameter information are uploaded to the energy consumption prediction and analysis model for prediction and calculation to obtain energy consumption calculation and prediction information.

[0013] Based on historical energy consumption data processing experience, a data error factor is set;

[0014] The energy consumption accounting and prediction information is adjusted based on the data error factor to obtain closed-loop information for energy consumption accounting and prediction, and energy utilization management is carried out based on the closed-loop information for energy consumption accounting and prediction.

[0015] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0016] We obtain customer steel plate cutting needs information through the cloud cutting platform;

[0017] Extract elements from the customer's steel plate cutting requirements to obtain requirement element information;

[0018] Based on the aforementioned demand element information, a cutting scheme is deployed to obtain steel plate cutting application scheme information;

[0019] Based on the steel plate cutting application scheme information, obtain the cutting deployment equipment information and cutting application parameter information;

[0020] The cutting deployment equipment information and cutting application parameter information are uploaded to the energy consumption prediction and analysis model for prediction and calculation to obtain energy consumption calculation and prediction information.

[0021] Based on historical energy consumption data processing experience, a data error factor is set;

[0022] The energy consumption accounting and prediction information is adjusted based on the data error factor to obtain closed-loop information for energy consumption accounting and prediction, and energy utilization management is carried out based on the closed-loop information for energy consumption accounting and prediction.

[0023] The aforementioned intelligent energy management method and system for cloud cutting platforms solves the technical problem of low energy utilization management efficiency in existing steel plate cutting technologies, which leads to energy waste. It achieves the technical effect of improving the accuracy of energy consumption accounting and prediction information by deploying steel plate cutting schemes for energy consumption prediction and analysis, thereby enabling precise management of energy utilization and improving the efficiency of energy utilization management, thus ensuring the rational and scientific use of energy.

[0024] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating an intelligent energy management method for a cloud-based platform in one embodiment.

[0026] Figure 2 This is a schematic diagram of a process for obtaining steel plate cutting application solution information in an energy consumption intelligent management method for a cloud cutting platform, as shown in one embodiment.

[0027] Figure 3 This is a structural block diagram of an energy consumption intelligent management system for a cloud-based platform, as shown in one embodiment.

[0028] Figure 4 This is an internal structural diagram of a computer device in one embodiment;

[0029] Explanation of reference numerals in the attached diagram: Module 11 for obtaining cutting demand information, Module 12 for extracting demand elements, Module 13 for deploying cutting schemes, Module 14 for obtaining cutting parameters, Module 15 for model prediction and calculation, Module 16 for setting error factors, and Module 17 for energy utilization management. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] like Figure 1 As shown, this application provides an intelligent energy management method for a cloud-based platform, the method comprising:

[0032] Step S100: Obtain customer steel plate cutting requirements information through the cloud cutting platform;

[0033] Step S200: Extract elements from the customer's steel plate cutting requirements information to obtain requirement element information;

[0034] Specifically, steel plate cutting is a fundamental step in machinery manufacturing. With the development of artificial intelligence, to address serious issues such as excessive steel plate scrap, unstable orders, low equipment utilization, and capital tied up in inventory, an "Internet + Steel Plate Cutting Collaborative Manufacturing Sharing Platform," also known as a cloud cutting platform, has been proposed. Small and medium-sized metal processing enterprises can place orders on this platform to achieve personalized customization and obtain high-precision, low-cost cut plates. Steel plate cutting involves various processing methods, and the cutting process is inextricably linked to energy consumption. Therefore, precise management of cutting energy consumption is necessary to ensure the rational and scientific use of energy.

[0035] First, customer steel plate cutting requirements are obtained from orders placed through a cloud-based cutting platform. This platform, based on the cloud platform (Internet+), is a smart manufacturing model for steel plate cutting. It tightly integrates next-generation information technologies such as the Internet, robotics, big data, and cloud computing with advanced manufacturing, thereby disrupting the traditional decentralized cutting methods in various factories. This significantly improves processing efficiency, reduces resource waste, and effectively achieves information, technology, capacity, and order sharing, enabling precise allocation and efficient connection of resources across regions and industries. The customer steel plate cutting requirements are then extracted to obtain requirement element information, which includes steel plate type, thickness, shape, and cutting design drawings, thus enabling personalized customization of steel plate cutting.

[0036] Step S300: Deploy a cutting scheme based on the aforementioned demand element information to obtain steel plate cutting application scheme information;

[0037] In one embodiment, such as Figure 2 As shown, in obtaining the steel plate cutting application solution information, step S300 of this application further includes:

[0038] Step S310: Based on the evaluation of the demand element information by the steel plate cutting expert group, a set of steel plate cutting application solutions is obtained;

[0039] Step S320: Construct a knowledge graph for steel plate cutting;

[0040] In one embodiment, the construction of the steel plate cutting knowledge graph, step S320 of this application further includes:

[0041] Step S321: Obtain the set of steel plate cutting parameters, which includes cutting method, cutting process, cutting time, and cutting equipment;

[0042] Step S322: Extract cutting attributes from the steel plate cutting index set to obtain a steel plate cutting attribute set;

[0043] Step S323: Obtain the set of steel plate cutting attribute values ​​based on the set of steel plate cutting attributes;

[0044] Step S324: Based on the set of steel plate cutting attributes and the set of steel plate cutting attribute values, construct and obtain the knowledge graph of steel plate cutting.

[0045] In one embodiment, the step S323 of this application further includes obtaining the steel plate cutting attribute value set based on the steel plate cutting attribute set:

[0046] Step S3231: Extract cutting attributes from the steel plate cutting attribute set to obtain cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information;

[0047] Step S3232: Based on the cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information, obtain the cutting method knowledge node information, cutting process knowledge node information, cutting time knowledge node information, and cutting equipment knowledge node information, respectively.

[0048] Step S3233: Assign feature values ​​to each node information in the knowledge node information of cutting method, knowledge node information of cutting process, knowledge node information of cutting time, and knowledge node information of cutting equipment, and obtain feature values ​​of cutting method knowledge node, cutting process knowledge node, cutting time knowledge node, and cutting equipment knowledge node respectively.

[0049] Step S3234: Based on the feature values ​​of the knowledge nodes of the cutting method, the cutting process, the cutting time, and the cutting equipment, obtain the set of steel plate cutting attribute values.

[0050] Specifically, the deployment of steel plate cutting solutions based on the aforementioned demand element information involves an evaluation of this information by a steel plate cutting expert group. This evaluation employs an expert assessment method, a crucial comprehensive evaluation approach that leverages the experience of experts with diverse professional knowledge and utilizes intuitive thinking to evaluate complex, unstructured problems. This assessment yields a set of steel plate cutting application solutions corresponding to customer needs. To evaluate each cutting solution, a steel plate cutting knowledge graph is constructed. This knowledge graph integrates steel plate cutting knowledge from different sources, enhancing the connections between data and enabling a more intuitive analysis of the steel plate cutting solutions.

[0051] The process of constructing a knowledge graph for steel plate cutting involves first obtaining a set of steel plate cutting indicators, which are classification indicators of steel plate cutting scheme knowledge, including cutting methods, cutting processes, cutting time, and cutting equipment. Next, cutting attributes are extracted from this set of indicators to obtain a set of steel plate cutting attributes corresponding to each indicator. For example, the cutting method indicator attributes include manual steel plate cutting, semi-automatic cutting, and CNC cutting machine cutting; the cutting process indicator attributes include water jet cutting, ordinary shearing, flame cutting, plasma cutting, and laser cutting; and the cutting equipment indicator attributes include CNC flame cutting machine, CNC gasoline cutting machine, plasma cutting machine, and laser cutting machine.

[0052] Based on the steel plate cutting attribute set, steel plate cutting attribute values ​​are assigned. First, cutting attributes are extracted from the steel plate cutting attribute set to obtain the indicator attribute information corresponding to each indicator in the steel plate cutting attribute set, including cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information. Then, each attribute information in the cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information is used as a knowledge graph node. Each attribute information corresponds to a node, thereby obtaining corresponding knowledge node information for cutting method, cutting process, cutting time, and cutting equipment, which are the knowledge component nodes of the steel plate cutting knowledge graph, similar to a tree structure.

[0053] Feature values ​​are assigned to each node in the knowledge node information of cutting method, cutting process, cutting time, and cutting equipment. The method of feature value assignment is not limited and can be based on the complexity or difficulty of the node attributes, thereby obtaining feature values ​​for each of the following knowledge nodes: cutting method, cutting process, cutting time, and cutting equipment. Based on these feature values, a set of steel plate cutting attribute values ​​corresponding to each cutting attribute is obtained. Finally, based on the set of steel plate cutting attributes and the set of steel plate cutting attribute values, a steel plate cutting knowledge graph is constructed for data filtering, analysis, and decision-making regarding steel plate cutting schemes, thereby improving the accuracy and rationality of cutting scheme selection.

[0054] Step S330: Evaluate each cutting scheme in the steel plate cutting application scheme set using the steel plate cutting knowledge graph to obtain a set of cutting scheme evaluation feature values;

[0055] Step S340: Based on the set of evaluation features of the cutting scheme, perform scheme screening on the set of steel plate cutting application schemes to obtain the information of the steel plate cutting application schemes.

[0056] In one embodiment, the application step S340 further includes:

[0057] Step S341: Identify the application status of each cutting device in the steel plate cutting equipment set to obtain the application attribute information of the steel plate cutting equipment;

[0058] Step S342: Generate application tag information for the cutting equipment based on the application attribute information of the steel plate cutting equipment;

[0059] Step S343: Based on the application tag information of the cutting equipment, construct a management ledger for the steel plate cutting equipment;

[0060] Step S344: Based on the management ledger of the steel plate cutting equipment, correct the information of the steel plate cutting application scheme.

[0061] Specifically, the steel plate cutting knowledge graph is used to evaluate each cutting scheme in the steel plate cutting application scheme set. The feature values ​​of each cutting scheme in the steel plate cutting application scheme set are added together according to the feature values ​​corresponding to each attribute to obtain the set of evaluation feature values ​​for each scheme. Based on the set of evaluation feature values, the steel plate cutting application scheme set is filtered. For example, the scheme with the highest evaluation feature value is selected as the steel plate cutting application scheme information, i.e., the optimal steel plate cutting scheme. To ensure the accuracy of the cutting scheme application, the application status of each cutting device in the steel plate cutting equipment set is identified to obtain the application attribute information of the steel plate cutting equipment. The application attribute information of the steel plate cutting equipment is the application status of the steel plate cutting equipment, such as normal operation, scrap / damage, and maintenance status.

[0062] Based on the application attribute information of the steel plate cutting equipment, application tag information is generated, that is, the equipment is tagged according to its application status. Based on this application tag information, a management ledger for the steel plate cutting equipment is constructed. This ledger stores and marks application information for the cutting equipment, facilitating equipment maintenance and management, including equipment name, model, and application status. Based on this management ledger, the steel plate cutting application plan information is corrected. For example, if the cutting equipment is in a damaged or obsolete state, the type of cutting equipment in the application plan needs to be changed or the application cutting time adjusted. By constructing the cutting equipment ledger, full lifecycle management of the equipment is achieved, enabling timely correction of the steel plate cutting application plan and improving its accuracy.

[0063] Step S400: Based on the steel plate cutting application scheme information, obtain the cutting deployment equipment information and the cutting application parameter information;

[0064] Specifically, based on the determined steel plate cutting application scheme information, the information on the cutting deployment equipment used in the scheme is obtained, namely the cutting equipment model and type information, and the cutting application parameter information, namely the cutting method, cutting process, cutting time and other parameters, and then the energy consumption prediction analysis of steel plate cutting is carried out.

[0065] Step S500: Upload the cutting deployment equipment information and cutting application parameter information to the energy consumption prediction and analysis model for prediction and calculation, and obtain energy consumption calculation and prediction information;

[0066] In one embodiment, the step S500 of this application further includes obtaining energy consumption accounting and prediction information:

[0067] Step S510: Build an energy consumption prediction and analysis model, which includes an input layer, an energy consumption prediction layer, an accounting and analysis layer, and an output layer;

[0068] Step S520: Input the cutting deployment equipment information and cutting application parameter information into the energy consumption prediction layer through the input layer to obtain cutting energy consumption prediction information;

[0069] Step S530: Perform energy consumption accounting on the cutting energy consumption prediction information based on the accounting analysis layer to obtain energy consumption accounting prediction information;

[0070] Step S540: Output the energy consumption calculation and prediction information as the model output result through the output layer.

[0071] Specifically, the cutting deployment equipment information and cutting application parameter information are uploaded to the energy consumption prediction and analysis model for prediction and calculation. First, an energy consumption prediction and analysis model is built. This model is used to predict energy consumption during the steel plate cutting process. The model's functional layers include an input layer, an energy consumption prediction layer, a calculation and analysis layer, and an output layer. The cutting deployment equipment information and cutting application parameter information are input into the energy consumption prediction layer through the input layer. The energy consumption prediction layer is used to predict the energy consumption of the selected steel plate cutting application scheme. This prediction can be obtained through training with historical data, and then outputs cutting energy consumption prediction information. This cutting energy consumption prediction information is a prediction of energy usage, including the use of electricity, water, gas, and other energy sources during the cutting process.

[0072] Then, based on the calculation and analysis layer, energy consumption calculation is performed on the cutting energy consumption prediction information. This involves calculating the total process consumption and cost based on the predicted energy consumption, and statistically obtaining the energy consumption prediction information. This energy consumption prediction information is then output as the model output result through the output layer. By building an energy consumption prediction and analysis model for energy consumption prediction, the accuracy and efficiency of the energy consumption prediction information output are improved. This facilitates timely budgeting and adjustment of energy consumption by steel plate cutting suppliers, thereby improving the accuracy of energy cost planning and management.

[0073] Step S600: Based on historical energy consumption data processing experience, set the data error factor;

[0074] Step S700: Adjust the energy consumption accounting prediction information based on the data error factor to obtain energy consumption accounting prediction closed-loop information, and perform energy utilization management based on the energy consumption accounting prediction closed-loop information.

[0075] In one embodiment, the step S700 of this application further includes the following for energy utilization management based on the energy consumption accounting and prediction closed-loop information:

[0076] Step S710: Monitor the steel plate cutting process in real time through the cloud cutting monitoring module to obtain real-time cutting monitoring information;

[0077] Step S720: Analyze the energy consumption of the real-time cutting monitoring information to obtain the energy loss rate of steel plate cutting;

[0078] Step S730: Generate a cutting energy consumption control factor based on the difference between the steel plate cutting energy loss degree and the energy consumption accounting prediction closed-loop information;

[0079] Step S740: Dynamically adjust and manage the energy utilization of steel plates based on the cutting energy consumption control factor.

[0080] Specifically, in actual steel plate cutting energy consumption, excessive energy consumption can occur due to operational and equipment factors, resulting in prediction errors. Based on historical energy consumption data processing experience, a data error factor is set. This data error factor is determined by the cutting supplier's historical experience. The energy consumption calculation and prediction information is adjusted based on this data error factor to obtain error-adjusted closed-loop energy consumption calculation and prediction information. Energy utilization management is then based on this closed-loop information. A cloud-based monitoring module, an embedded functional module within the cloud-based cutting platform, monitors the steel plate cutting process in real time, obtaining real-time cutting monitoring information.

[0081] Energy consumption analysis is performed on the real-time monitoring information of the cutting process to obtain the energy loss rate of steel plate cutting. Based on the difference between the energy loss rate and the energy consumption calculation and prediction closed-loop information (i.e., the difference between the actual energy consumption and the predicted energy consumption), a cutting energy consumption control factor is generated. This factor represents the degree of energy management control. Based on this factor, the energy utilization of the steel plate is dynamically adjusted and managed. For example, if the actual energy consumption is too high, a high degree of energy consumption control is required to check for any illegal electricity use or operational problems, thereby saving energy. This achieves precise management of energy consumption, improves energy consumption management efficiency, ensures the rational and scientific use of energy, and ultimately reduces the overall energy operating cost.

[0082] In one embodiment, such as Figure 3 As shown, an intelligent energy management system for a cloud-based cutting platform is provided, comprising: a cutting demand information acquisition module 11, a demand element extraction module 12, a cutting scheme deployment module 13, a cutting parameter acquisition module 14, a model prediction and calculation module 15, an error factor setting module 16, and an energy utilization management module 17, wherein:

[0083] The cutting requirement information acquisition module 11 is used to acquire customer steel plate cutting requirement information through the cloud cutting platform;

[0084] The demand element extraction module 12 is used to extract elements from the customer's steel plate cutting demand information to obtain demand element information.

[0085] The cutting scheme deployment module 13 is used to deploy the cutting scheme based on the demand element information and obtain steel plate cutting application scheme information.

[0086] The cutting parameter acquisition module 14 is used to acquire cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information;

[0087] The model prediction and calculation module 15 is used to upload the cutting deployment equipment information and cutting application parameter information to the energy consumption prediction and analysis model for prediction and calculation, and obtain energy consumption calculation and prediction information.

[0088] Error factor setting module 16 is used to set the data error factor based on historical energy consumption data processing experience;

[0089] The energy utilization management module 17 is used to adjust the energy consumption accounting prediction information based on the data error factor, obtain the energy consumption accounting prediction closed-loop information, and perform energy utilization management based on the energy consumption accounting prediction closed-loop information.

[0090] In one embodiment, the cutting scheme deployment module further includes:

[0091] The element information evaluation unit is used to evaluate the required element information based on the steel plate cutting expert group to obtain a set of steel plate cutting application solutions.

[0092] Knowledge graph construction unit, used to construct a knowledge graph of steel plate cutting;

[0093] The cutting scheme evaluation unit is used to evaluate each cutting scheme in the steel plate cutting application scheme set through the steel plate cutting knowledge graph, and obtain a set of cutting scheme evaluation feature values.

[0094] The cutting application scheme acquisition unit is used to screen the steel plate cutting application scheme set based on the cutting scheme evaluation feature value set to obtain the steel plate cutting application scheme information.

[0095] In one embodiment, the knowledge graph construction unit further includes:

[0096] The cutting index acquisition unit is used to obtain a set of steel plate cutting indexes, which includes cutting method, cutting process, cutting time, and cutting equipment.

[0097] A cutting attribute extraction unit is used to extract cutting attributes from the steel plate cutting index set to obtain a steel plate cutting attribute set.

[0098] The cutting attribute value acquisition unit is used to obtain a set of steel plate cutting attribute values ​​based on the steel plate cutting attribute set.

[0099] The knowledge graph acquisition unit is used to construct and obtain the steel plate cutting knowledge graph based on the steel plate cutting attribute set and the steel plate cutting attribute value set.

[0100] In one embodiment, the cutting attribute value obtaining unit further includes:

[0101] The cutting attribute extraction unit is used to extract cutting attributes from the steel plate cutting attribute set to obtain cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information.

[0102] The knowledge node acquisition unit is used to obtain cutting method knowledge node information, cutting process knowledge node information, cutting time knowledge node information, and cutting equipment knowledge node information respectively based on the cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information.

[0103] The node feature value acquisition unit is used to assign feature values ​​to each node information in the cutting method knowledge node information, cutting process knowledge node information, cutting time knowledge node information, and cutting equipment knowledge node information, and obtain the cutting method knowledge node feature value, cutting process knowledge node feature value, cutting time knowledge node feature value, and cutting equipment knowledge node feature value respectively.

[0104] The steel plate cutting attribute value acquisition unit is used to obtain the set of steel plate cutting attribute values ​​based on the knowledge node feature values ​​of the cutting method, the knowledge node feature values ​​of the cutting process, the knowledge node feature values ​​of the cutting time, and the knowledge node feature values ​​of the cutting equipment.

[0105] In one embodiment, the system further includes:

[0106] The application status identification unit is used to identify the application status of each cutting device in the steel plate cutting equipment set and obtain the application attribute information of the steel plate cutting equipment.

[0107] The application tag generation unit is used to generate application tag information for the cutting equipment based on the application attribute information of the steel plate cutting equipment.

[0108] The equipment management ledger construction unit is used to construct a steel plate cutting equipment management ledger based on the application tag information of the cutting equipment.

[0109] The application scheme correction unit is used to correct the steel plate cutting application scheme information based on the steel plate cutting equipment management ledger.

[0110] In one embodiment, the model prediction accounting module further includes:

[0111] The model building unit is used to build an energy consumption prediction and analysis model, which includes an input layer, an energy consumption prediction layer, an accounting and analysis layer, and an output layer.

[0112] The energy consumption prediction unit is used to input the cutting deployment equipment information and cutting application parameter information into the energy consumption prediction layer through the input layer to obtain cutting energy consumption prediction information;

[0113] The calculation and analysis unit is used to perform energy consumption calculation on the cutting energy consumption prediction information based on the calculation and analysis layer, and obtain energy consumption calculation and prediction information.

[0114] The model output unit is used to output the energy consumption calculation and prediction information as the model output result through the output layer.

[0115] In one embodiment, the energy utilization management module further includes:

[0116] The real-time cutting monitoring unit is used to monitor the steel plate cutting process in real time through the cloud cutting supervision module and obtain real-time cutting monitoring information.

[0117] An energy consumption analysis unit is used to perform energy consumption analysis on the real-time cutting monitoring information to obtain the energy loss rate of steel plate cutting.

[0118] The regulation factor generation unit is used to generate a cutting energy consumption regulation factor based on the difference between the steel plate cutting energy consumption loss degree and the energy consumption accounting prediction closed-loop information.

[0119] The dynamic adjustment management unit is used to dynamically adjust and manage the energy utilization of steel plates based on the cutting energy consumption control factor.

[0120] For a specific embodiment of an intelligent energy management system for a cloud slicing platform, please refer to the embodiment of an intelligent energy management method for a cloud slicing platform described above, which will not be repeated here. Each module in the aforementioned intelligent energy management device for a cloud slicing platform can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0121] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores news data and data such as time decay factors. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent energy management method for a cloud platform.

[0122] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring customer steel plate cutting demand information through a cloud cutting platform; extracting elements from the customer steel plate cutting demand information to obtain demand element information; deploying a cutting scheme based on the demand element information to obtain steel plate cutting application scheme information; acquiring cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model for prediction and accounting to obtain energy consumption accounting prediction information; setting a data error factor based on historical energy consumption data processing experience; adjusting the energy consumption accounting prediction information based on the data error factor to obtain energy consumption accounting prediction closed-loop information, and performing energy utilization management based on the energy consumption accounting prediction closed-loop information.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program performs the following steps: acquiring customer steel plate cutting demand information through a cloud cutting platform; extracting elements from the customer steel plate cutting demand information to obtain demand element information; deploying a cutting scheme based on the demand element information to obtain steel plate cutting application scheme information; acquiring cutting deployment equipment information and cutting application parameter information according to the steel plate cutting application scheme information; uploading the cutting deployment equipment information and cutting application parameter information to an energy consumption prediction and analysis model for prediction and accounting to obtain energy consumption accounting prediction information; setting a data error factor based on historical energy consumption data processing experience; adjusting the energy consumption accounting prediction information based on the data error factor to obtain energy consumption accounting prediction closed-loop information; and performing energy utilization management based on the energy consumption accounting prediction closed-loop information. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0125] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for intelligent energy management of a cloud-based platform, characterized in that, The method includes: We obtain customer steel plate cutting needs information through the cloud cutting platform; Extract elements from the customer's steel plate cutting requirements to obtain requirement element information; Based on the aforementioned demand element information, a cutting scheme is deployed to obtain steel plate cutting application scheme information; Based on the steel plate cutting application scheme information, obtain the cutting deployment equipment information and cutting application parameter information; The cutting deployment equipment information and cutting application parameter information are uploaded to the energy consumption prediction and analysis model for prediction and calculation to obtain energy consumption calculation and prediction information. Based on historical energy consumption data processing experience, a data error factor is set; The energy consumption accounting and prediction information is adjusted based on the data error factor to obtain closed-loop information for energy consumption accounting and prediction, and energy utilization management is carried out based on the closed-loop information for energy consumption accounting and prediction. The information obtained regarding the steel plate cutting application scheme includes: Based on the evaluation of the required elements by the steel plate cutting expert group, a set of steel plate cutting application solutions was obtained. Constructing a knowledge graph for steel plate cutting; The steel plate cutting knowledge graph is used to evaluate each cutting scheme in the steel plate cutting application scheme set to obtain a set of cutting scheme evaluation feature values. Based on the set of evaluation feature values ​​of the cutting scheme, the set of steel plate cutting application schemes is screened to obtain information on the steel plate cutting application schemes. The construction of the steel plate cutting knowledge graph includes: Obtain a set of steel plate cutting parameters, which includes cutting method, cutting process, cutting time, and cutting equipment; The steel plate cutting index set is subjected to cutting attribute extraction to obtain the steel plate cutting attribute set; Based on the set of steel plate cutting attributes, obtain the set of steel plate cutting attribute values; Based on the set of steel plate cutting attributes and the set of steel plate cutting attribute values, a knowledge graph of steel plate cutting is constructed. The application status of each cutting device in the steel plate cutting equipment set is identified to obtain the application attribute information of the steel plate cutting equipment. Based on the application attribute information of the steel plate cutting equipment, generate application tag information for the cutting equipment; Based on the application tag information of the cutting equipment, a management ledger for steel plate cutting equipment is constructed; Based on the management ledger of the steel plate cutting equipment, the information of the steel plate cutting application scheme is corrected.

2. The method as described in claim 1, characterized in that, The step of obtaining the steel plate cutting attribute value set based on the steel plate cutting attribute set includes: The steel plate cutting attribute set is subjected to cutting attribute extraction to obtain cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information; Based on the cutting method attribute information, cutting process attribute information, cutting time attribute information, and cutting equipment attribute information, respectively, knowledge node information of cutting method, knowledge node information of cutting process, knowledge node information of cutting time, and knowledge node information of cutting equipment are obtained. Feature values ​​are assigned to each node in the knowledge node information of cutting method, cutting process, cutting time, and cutting equipment, respectively, to obtain the feature values ​​of the knowledge node information of cutting method, cutting process, cutting time, and cutting equipment. Based on the feature values ​​of the knowledge nodes of cutting method, cutting process, cutting time, and cutting equipment, the set of steel plate cutting attribute values ​​is obtained.

3. The method as described in claim 1, characterized in that, The acquisition of energy consumption accounting and prediction information includes: An energy consumption prediction and analysis model is constructed, which includes an input layer, an energy consumption prediction layer, an accounting and analysis layer, and an output layer. The cutting deployment equipment information and cutting application parameter information are input into the energy consumption prediction layer through the input layer to obtain cutting energy consumption prediction information; Based on the aforementioned accounting and analysis layer, energy consumption accounting is performed on the cutting energy consumption prediction information to obtain energy consumption accounting prediction information; The energy consumption calculation and prediction information is output as the model output result through the output layer.

4. The method as described in claim 1, characterized in that, The energy utilization management based on the energy consumption accounting and prediction closed-loop information includes: The cloud-based monitoring module monitors the steel plate cutting process in real time, obtaining real-time cutting monitoring information. Energy consumption analysis is performed on the real-time cutting monitoring information to obtain the energy loss rate of steel plate cutting; Based on the difference between the energy loss rate of steel plate cutting and the closed-loop information of energy consumption accounting and prediction, a cutting energy consumption control factor is generated. The energy utilization of steel plates is dynamically adjusted and managed based on the aforementioned cutting energy consumption control factor.

5. An intelligent energy management system for a cloud-based platform, characterized in that, The system is used to perform the method according to any one of claims 1 to 4, the system comprising: The cutting requirement information acquisition module is used to acquire customer steel plate cutting requirement information through the cloud cutting platform; The demand element extraction module is used to extract elements from the customer's steel plate cutting demand information to obtain demand element information; The cutting scheme deployment module is used to deploy the cutting scheme based on the required element information and obtain steel plate cutting application scheme information. The cutting parameter acquisition module is used to acquire cutting deployment equipment information and cutting application parameter information based on the steel plate cutting application scheme information; The model prediction and calculation module is used to upload the cutting deployment equipment information and cutting application parameter information to the energy consumption prediction and analysis model for prediction and calculation, and obtain energy consumption calculation and prediction information. The error factor setting module is used to set the data error factor based on historical energy consumption data processing experience; The energy utilization management module is used to adjust the energy consumption accounting prediction information based on the data error factor to obtain closed-loop information for energy consumption accounting prediction, and to perform energy utilization management based on the closed-loop information for energy consumption accounting prediction.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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