A production line energy-saving strategy automatic generation method and system

Abstract

The application provides a production line energy-saving strategy automatic generation method and system, the method comprises the following steps: collecting energy data information of the production line in real time, generating a production energy consumption database by using a graph database management system; obtaining preset production line energy-saving cases, energy consumption characteristic information of the production line, energy-saving potential information of the production line and standard energy consumption data, and combining the energy consumption data information of the production line to construct an energy-saving database; extracting characteristic indexes of the production line to be improved from the production energy consumption database to generate a technical portrait of the production line; extracting characteristic indexes of the production line energy-saving scheme from the energy-saving database to generate a case technical portrait; preprocessing the technical portrait, and performing case matching by using a similarity matching model; and generating an energy-saving strategy by using an energy-saving strategy generation algorithm according to the case matching result and in combination with the energy-saving database. The application can automatically generate an energy-saving strategy matched with the actual situation of the production line by analyzing the energy data information of the production line.

Description

Technical Field

[0001] This invention relates to the field of production line management technology, and more specifically to a method and system for automatically generating energy-saving strategies for production lines. Background Technology

[0002] In recent years, with the increasing scarcity of global resources and the intensification of environmental problems, all sectors have placed greater emphasis on environmental protection and the full utilization of resources to achieve sustainable development. As a crucial player in social development, enterprises are receiving increasing attention for their role in achieving sustainable development. Energy conservation and emission reduction are key to the sustainable development of enterprises.

[0003] Energy conservation and emission reduction are crucial for the sustainable development of enterprises. First, they help companies reduce energy costs. Second, for large enterprises, energy costs often constitute a significant proportion of their total costs. Therefore, implementing energy conservation and emission reduction measures can effectively reduce energy consumption, thereby lowering production costs and improving profitability.

[0004] For enterprises to achieve energy conservation and emission reduction, the key lies in optimizing energy management in the production process. As the most important component of enterprise production, the production line requires optimized energy management to effectively improve the enterprise's sustainable development level. However, due to the generally large scale, high energy consumption, and extensive management characteristics of enterprise production line energy management, current technical methods for formulating enterprise energy-saving transformation strategies are mostly based on manual experience, requiring manual intervention and adjustments, and later adjustments and optimizations based on actual energy consumption. This approach typically requires significant costs and time and is unlikely to achieve optimal results. Furthermore, the experience gained in energy-saving transformation of production lines is specific and lacks generalizability. Summary of the Invention

[0005] To address the above problems, the present invention aims to provide a method and system for automatically generating energy-saving strategies for production lines, which can automatically generate energy-saving strategies that match the actual operating status of the production line by analyzing the energy data information of the production line.

[0006] To achieve the above objectives, this invention provides the following technical solution: a method for automatically generating energy-saving strategies for production lines, comprising:

[0007] Real-time collection of energy data from the production line; and generation of a production energy consumption database using a graph database management system.

[0008] Obtain preset production line energy-saving cases, production line energy consumption characteristics, production line energy-saving potential information and standard energy consumption data, and combine the production line energy consumption data information to construct an energy-saving knowledge base using knowledge graph methods;

[0009] Extract characteristic indicators of the production line to be improved from the production energy consumption database to generate a technical profile of the production line; extract characteristic indicators of energy-saving solutions for the production line from the energy-saving knowledge base to generate a case technical profile.

[0010] The technical profiles of the production line and the technical profiles of the cases are preprocessed, and a similarity matching model is used for case matching.

[0011] Based on the case matching results, an energy-saving strategy is generated using an energy-saving strategy generation algorithm in conjunction with an energy-saving knowledge base.

[0012] Furthermore, the method also includes:

[0013] The effectiveness of energy-saving strategies is judged based on real-time energy information of the production lines to be improved in the production energy consumption database.

[0014] Predict energy information for production lines that need improvement and dynamically adjust energy-saving strategies based on the prediction results.

[0015] Furthermore, the method also includes dynamically updating the energy-saving knowledge base, the energy-saving strategy production algorithm, and the production energy consumption database.

[0016] Furthermore, the real-time acquisition of energy data from the production line, and the generation of a production energy consumption database using a graph database management system, includes:

[0017] Real-time collection of production line process parameters, energy consumption data, and equipment information; using a graph database management system to collect the acquired production line process parameters, energy consumption data, and equipment information and establish set relationships to generate structured data;

[0018] Data preprocessing techniques are used to clean, denoise, and normalize structured data, and the processed data is used to build a production energy consumption database.

[0019] Furthermore, the energy consumption data from the production line is combined with knowledge graph methods to construct an energy-saving knowledge base, including:

[0020] Production line energy consumption data and standard energy consumption data are set as point sets, while production line energy-saving schemes, production line energy consumption characteristics, and production line energy-saving potential information are set as edge sets. By connecting these various types of data, a production line / equipment data knowledge graph is generated to construct an energy-saving knowledge base.

[0021] Furthermore, the preprocessing of the technical profiles of the production line and the technical profiles of the cases, and the use of a similarity matching model for case matching, includes:

[0022] Feature information is extracted from the technical profiles of the production line and case studies, and then converted into vector representations using a standardization method to generate corresponding standard numerical features.

[0023] Standard numerical features are input into a similarity matching model to calculate similarity and determine the production line energy-saving case with the highest similarity.

[0024] Furthermore, the step of generating energy-saving strategies based on case matching results and in conjunction with an energy-saving knowledge base using an energy-saving strategy generation algorithm includes:

[0025] Based on the most similar production line energy-saving cases, standard energy consumption data in the energy-saving knowledge base and production line / equipment data knowledge graph, the system automatically generates energy-saving strategies according to categories by matching content with preset templates.

[0026] Energy-saving strategies include indicator analysis, strategy description, and technical contingency plans.

[0027] Furthermore, the prediction of energy information for the production line to be improved, and the dynamic adjustment of energy-saving strategies based on the prediction results, include:

[0028] Machine learning algorithms are used to predict energy consumption data and equipment operating status information of the production line to be improved, and energy-saving strategies are dynamically adjusted based on the prediction results.

[0029] Furthermore, the dynamic updating of the energy-saving knowledge base, energy-saving strategy production algorithm, and production energy consumption database includes:

[0030] An incremental learning algorithm is used to incrementally update the energy-saving knowledge base;

[0031] An adaptive learning algorithm is used to automatically adjust the energy-saving strategy generation algorithm based on changes in energy data information of the production line;

[0032] Online learning algorithms are used to dynamically update the process characteristics, energy consumption data, and equipment operating parameters of the production line.

[0033] Accordingly, the present invention also discloses an automatic generation system for energy-saving strategies of production lines, comprising:

[0034] The energy consumption database generation unit is used to collect energy data information from the production line in real time and generate a production energy consumption database using the graph database management system.

[0035] The energy-saving knowledge base generation unit is used to acquire preset production line energy-saving cases, production line energy consumption characteristic information, production line energy-saving potential information and standard energy consumption data, and to construct an energy-saving knowledge base by combining the production line energy consumption data information with the knowledge graph method.

[0036] The technical profile generation unit is used to extract the characteristic indicators of the production line to be improved from the production energy consumption database and generate the technical profile of the production line; and to extract the characteristic indicators of the energy-saving solution of the production line from the energy-saving knowledge base and generate the case technical profile.

[0037] The similarity matching unit is used to preprocess the technical profiles of the production line and the technical profiles of the cases, and to perform case matching using the similarity matching model;

[0038] The energy-saving strategy generation unit is used to generate energy-saving strategies based on the case matching results and in conjunction with the energy-saving knowledge base using the energy-saving strategy generation algorithm.

[0039] The strategy application monitoring unit is used to predict energy information of the production line to be improved and dynamically adjust energy-saving strategies based on the prediction results.

[0040] The knowledge base update unit is used to dynamically update the energy-saving knowledge base, energy-saving strategy production algorithms, and production energy consumption database.

[0041] Compared with existing technologies, the advantages of this invention are as follows: This invention discloses a method and system for automatically generating energy-saving strategies for production lines. It constructs a production energy consumption database and an energy-saving knowledge base by collecting real-time production line data, related production line indicator data, and energy-saving technology solutions. Using the production energy consumption database and energy-saving knowledge base, it performs production line similarity matching to determine suitable energy-saving cases for the production line to be improved, and generates corresponding energy-saving strategies based on the actual situation of the production line. This invention can automatically generate energy-saving strategies by combining production line energy data, energy-saving policy indicators, energy-saving knowledge strategy maps, and energy-saving prediction data, thereby solving the problem in existing technologies where strategy generation cannot accurately match the actual situation of the production line.

[0042] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention;

[0045] Figure 2 This is a system structure diagram of a specific embodiment of the present invention.

[0046] In the diagram, 1 is the energy consumption database generation unit; 2 is the energy-saving knowledge base generation unit; 3 is the technology profile generation unit; 4 is the similarity matching unit; 5 is the energy-saving strategy generation unit; 6 is the strategy application monitoring unit; and 7 is the knowledge base update unit. Detailed Implementation

[0047] Example 1:

[0048] like Figure 1 As shown in the figure, this embodiment provides a method for automatically generating energy-saving strategies for production lines, including the following steps:

[0049] S1: Collect energy data from the production line in real time and generate a production energy consumption database using a graph database management system.

[0050] In a specific implementation, firstly, production line process parameters, energy consumption data, and equipment information are collected in real time. A graph database management system is used to aggregate the acquired production line process parameters, energy consumption data, and equipment information and establish set relationships to generate structured data. Then, data preprocessing techniques are used to clean, denoise, and normalize the structured data, and the processed data is used to construct a production energy consumption database.

[0051] S2: Obtain preset production line energy-saving cases, production line energy consumption characteristics, production line energy-saving potential information and standard energy consumption data, and construct an energy-saving knowledge base using the knowledge graph method in combination with the production line energy consumption data information.

[0052] In a specific implementation, production line energy consumption data and standard energy consumption data are set as point sets, and production line energy-saving schemes, production line energy consumption characteristic information, and production line energy-saving potential information are set as edge sets. By connecting various types of data, a production line / equipment data knowledge graph is generated to construct an energy-saving knowledge base.

[0053] S3: Extract the characteristic indicators of the production line to be improved from the production energy consumption database to generate a technical profile of the production line; extract the characteristic indicators of the energy-saving solution of the production line from the energy-saving knowledge base to generate a case technical profile.

[0054] S4: Preprocess the technical profiles of the production line and the technical profiles of the cases, and use a similarity matching model to match cases.

[0055] In a specific implementation, feature information is first extracted from the technical profiles of the production line and the technical profiles of the case studies, and then converted into vector representations using a standardization method to generate corresponding standard numerical features. Next, the standard numerical features are input into a similarity matching model to calculate similarity, and the production line energy-saving case with the highest similarity is determined based on the similarity value.

[0056] S5: Based on the case matching results, use the energy-saving strategy generation algorithm to generate energy-saving strategies in conjunction with the energy-saving knowledge base.

[0057] In a specific implementation, based on the production line energy-saving case with the highest similarity, standard energy consumption data in the energy-saving knowledge base, and the production line / equipment data knowledge graph, energy-saving strategies are automatically generated by category according to the content matching of preset templates; the energy-saving strategies include indicator analysis, strategy description, and technical plans.

[0058] As an example, once the energy-saving strategy is generated, this method will monitor the execution of the energy-saving strategy as needed.

[0059] First, the effectiveness of energy-saving strategies is assessed based on real-time energy information from the production line to be improved in the production energy consumption database. Then, the energy information of the production line to be improved is predicted, and the energy-saving strategies are dynamically adjusted based on the prediction results. Specifically, machine learning algorithms can be used to predict the energy consumption data and equipment operating status information of the production line to be improved, and the energy-saving strategies can be dynamically adjusted based on the prediction results.

[0060] As an example, this method can dynamically update the energy-saving knowledge base, energy-saving strategy production algorithm, and production energy consumption database during execution. Specifically: it uses an incremental learning algorithm to incrementally update the energy-saving knowledge base; it uses an adaptive learning algorithm to automatically adjust the energy-saving strategy generation algorithm based on changes in the production line's energy data; and it uses an online learning algorithm to dynamically update the production line's process characteristics, energy consumption data, and equipment operating parameters.

[0061] Example 2:

[0062] Based on Example 1, such as Figure 2 As shown, the present invention also discloses an automatic generation system for energy-saving strategies of production lines, including: an energy consumption database generation unit 1, an energy-saving knowledge base generation unit 2, a technology profile generation unit 3, a similarity matching unit 4, an energy-saving strategy generation unit 5, a strategy application monitoring unit 6, and a knowledge base update unit 7.

[0063] Energy consumption database generation unit 1 is used to collect energy data information from the production line in real time and generate a production energy consumption database using a graph database management system.

[0064] Energy-saving knowledge base generation unit 2 is used to acquire preset production line energy-saving cases, production line energy consumption characteristic information, production line energy-saving potential information and standard energy consumption data, and to construct an energy-saving knowledge base by combining the production line energy consumption data information with the knowledge graph method.

[0065] Technical profile generation unit 3 is used to extract characteristic indicators of the production line to be improved from the production energy consumption database and generate a technical profile of the production line; and to extract characteristic indicators of energy-saving solutions for the production line from the energy-saving knowledge base and generate a case technical profile.

[0066] Similarity matching unit 4 is used to preprocess the technical profiles of the production line and the technical profiles of the cases, and to perform case matching using the similarity matching model.

[0067] Energy-saving strategy generation unit 5 is used to generate energy-saving strategies based on case matching results and in conjunction with an energy-saving knowledge base using an energy-saving strategy generation algorithm.

[0068] The strategy application monitoring unit 6 is used to predict the energy information of the production line to be improved and dynamically adjust the energy-saving strategy based on the prediction results.

[0069] The knowledge base update unit 7 is used to dynamically update the energy-saving knowledge base, energy-saving strategy production algorithm, and production energy consumption database.

[0070] In summary, this invention can automatically generate energy-saving strategies by combining production line energy data, energy-saving policy indicators, energy-saving knowledge strategy maps, and energy-saving prediction data, thereby solving the problem in existing technologies where strategy generation cannot accurately match the actual situation of the production line.

[0071] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0072] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0073] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0074] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0076] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0077] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0078] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] The above provides a detailed description of the automatic generation method and system for energy-saving strategies in production lines provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A method for automatically generating energy-saving strategies for production lines, characterized in that, include: Real-time collection of energy data from the production line; and generation of a production energy consumption database using a graph database management system. Obtain preset production line energy-saving cases, production line energy consumption characteristics, production line energy-saving potential information and standard energy consumption data, and combine the production line energy consumption data information to construct an energy-saving knowledge base using knowledge graph methods; Extract characteristic indicators of the production line to be improved from the production energy consumption database to generate a technical profile of the production line; Extract characteristic indicators of energy-saving solutions for production lines from the energy-saving knowledge base to generate case technology profiles; The technical profiles of the production line and the technical profiles of the cases are preprocessed, and a similarity matching model is used for case matching. Based on the case matching results, an energy-saving strategy is generated using an energy-saving strategy generation algorithm in conjunction with an energy-saving knowledge base.

2. The method for automatically generating energy-saving strategies for production lines according to claim 1, characterized in that, Also includes: The effectiveness of energy-saving strategies is judged based on real-time energy information of the production lines to be improved in the production energy consumption database. Predict energy information for production lines that need improvement and dynamically adjust energy-saving strategies based on the prediction results.

3. The method for automatically generating energy-saving strategies for production lines according to claim 2, characterized in that, Also includes: The energy-saving knowledge base, energy-saving strategy production algorithms, and production energy consumption database are dynamically updated.

4. The method for automatically generating energy-saving strategies for production lines according to claim 3, characterized in that, The real-time acquisition of energy data from the production line, and the generation of a production energy consumption database using a graph database management system, includes: Real-time collection of production line process parameters, energy consumption data, and equipment information; using a graph database management system to collect the acquired production line process parameters, energy consumption data, and equipment information and establish set relationships to generate structured data; Data preprocessing techniques are used to clean, denoise, and normalize structured data, and the processed data is used to build a production energy consumption database.

5. The method for automatically generating energy-saving strategies for production lines according to claim 4, characterized in that, The energy consumption data from the production line is used to construct an energy-saving knowledge base using a knowledge graph approach, including: Production line energy consumption data and standard energy consumption data are set as point sets, while production line energy-saving schemes, production line energy consumption characteristics, and production line energy-saving potential information are set as edge sets. By connecting these various types of data, a production line / equipment data knowledge graph is generated to construct an energy-saving knowledge base.

6. The method for automatically generating energy-saving strategies for production lines according to claim 5, characterized in that, The preprocessing of the technical profiles of the production line and the technical profiles of cases, and the use of a similarity matching model for case matching, includes: Feature information is extracted from the technical profiles of the production line and case studies, and then converted into vector representations using a standardization method to generate corresponding standard numerical features. Standard numerical features are input into a similarity matching model to calculate similarity and determine the production line energy-saving case with the highest similarity.

7. The method for automatically generating energy-saving strategies for production lines according to claim 6, characterized in that, The step of generating energy-saving strategies based on case matching results and an energy-saving knowledge base using an energy-saving strategy generation algorithm includes: Based on the most similar production line energy-saving cases, standard energy consumption data in the energy-saving knowledge base and production line / equipment data knowledge graph, the system automatically generates energy-saving strategies according to categories by matching content with preset templates. Energy-saving strategies include indicator analysis, strategy description, and technical contingency plans.

8. The method for automatically generating energy-saving strategies for production lines according to claim 7, characterized in that, The prediction of energy information for the production line to be improved, and the dynamic adjustment of energy-saving strategies based on the prediction results, include: Machine learning algorithms are used to predict energy consumption data and equipment operating status information of the production line to be improved, and energy-saving strategies are dynamically adjusted based on the prediction results.

9. The method for automatically generating energy-saving strategies for production lines according to claim 3, characterized in that, The dynamic updating of the energy-saving knowledge base, energy-saving strategy production algorithm, and production energy consumption database includes: An incremental learning algorithm is used to incrementally update the energy-saving knowledge base; An adaptive learning algorithm is used to automatically adjust the energy-saving strategy generation algorithm based on changes in energy data information of the production line; Online learning algorithms are used to dynamically update the process characteristics, energy consumption data, and equipment operating parameters of the production line.

10. An automatic energy-saving strategy generation system for a production line, characterized in that, include: The energy consumption database generation unit is used to collect energy data information from the production line in real time and generate a production energy consumption database using the graph database management system. The energy-saving knowledge base generation unit is used to acquire preset production line energy-saving cases, production line energy consumption characteristic information, production line energy-saving potential information and standard energy consumption data, and to construct an energy-saving knowledge base by combining the production line energy consumption data information with the knowledge graph method. The technical profile generation unit is used to extract characteristic indicators of the production line to be improved from the production energy consumption database and generate a technical profile of the production line. Extract characteristic indicators of energy-saving solutions for production lines from the energy-saving knowledge base to generate case technology profiles; The similarity matching unit is used to preprocess the technical profiles of the production line and the technical profiles of the cases, and to perform case matching using the similarity matching model; The energy-saving strategy generation unit is used to generate energy-saving strategies based on the case matching results and in conjunction with the energy-saving knowledge base using the energy-saving strategy generation algorithm. The strategy application monitoring unit is used to predict energy information of the production line to be improved and dynamically adjust energy-saving strategies based on the prediction results. The knowledge base update unit is used to dynamically update the energy-saving knowledge base, energy-saving strategy production algorithms, and production energy consumption database.

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