Energy System Data Management Method, Device, Equipment and Storage Medium Based on Blood Relationship Analysis

Abstract

The present application discloses a method, apparatus, device and storage medium for energy system data management based on blood relationship analysis, relating to the technical field of data processing. The method for energy system data management based on blood relationship analysis includes: constructing a data blood relationship triple based on the node mapping relationship between each energy model node of the energy structure tree model; determining the participating tuples and non-participating tuples of energy conversion according to the data blood relationship triple; determining the energy conversion weight value between the first model node and the second model node according to the participating tuples and non-participating tuples; and cleaning redundant data in the first model node and the second model node according to the energy conversion weight value. Since the blood relationship analysis of energy data is realized by constructing the energy structure tree model and the data blood relationship triple, the data transparency in the energy data conversion process of the energy system is improved, making the management of the energy data conversion and storage process more convenient.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly to an energy system data management method, device, equipment and storage medium based on lineage analysis. Background Art

[0002] As an indispensable part of society, energy management has a huge impact on economic development, from daily life such as lighting and heating to energy consumption of transportation tools. Therefore, how to reasonably manage and plan the data of the energy system has become a top priority.

[0003] Currently, as an important means of data management, data lineage analysis can provide a historical record of data source conversion, and then help the energy system conduct attribution analysis, impact analysis, usage analysis and link sorting, which is convenient for quickly locating the source of data errors and data problems, and is of great significance to data governance. However, due to the multi-source and diversification of energy data for a long time, the content of the database is complex and diverse, resulting in the problem of difficult data parsing. Summary of the Invention

[0004] The main purpose of the present application is to provide an energy system data management method, device, equipment and storage medium based on lineage analysis, aiming to solve the technical problem that the multi-source and diversification make the content of the database complex and diverse, resulting in difficult data parsing.

[0005] To achieve the above object, the present application proposes an energy system data management method based on lineage analysis, and the energy system data management method based on lineage analysis includes:

[0006] Constructing a data lineage triple based on the node mapping relationship between each energy model node of the energy structure tree model;

[0007] Determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triple;

[0008] Determining the energy conversion weight between the first model node and the second model node according to the participating tuples and the non-participating tuples; the first model node is the precursor node in a section of node mapping relationship, and the second model node is the successor node in a section of node mapping relationship;

[0009] Cleaning the redundant data in the first model node and the second model node according to the energy conversion weight.

[0010] In an embodiment, the step of constructing a data lineage triple based on the node mapping relationship between each energy model node of the energy structure tree model includes:

[0011] Generate energy model nodes based on the conversion hierarchy of energy data;

[0012] Determine the node mapping relationship between energy model nodes according to the association relationship between the energy data and the energy conversion factor;

[0013] Construct an energy conversion data table between the first model node and the second model node based on the node mapping relationship;

[0014] Construct data triples according to the energy conversion data table.

[0015] In one embodiment, the step of determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triples includes:

[0016] Determine the energy conversion participating data and energy conversion non-participating data in the energy conversion data table based on the data triples;

[0017] Determine the participating tuples of energy conversion in the data triples based on the energy conversion participating data;

[0018] Determine the non-participating tuples of energy conversion in the data triples based on the energy conversion non-participating data.

[0019] In one embodiment, the step of determining the node mapping relationship between energy model nodes according to the association relationship between the energy data and the energy conversion factor includes:

[0020] Determine the association relationship between the energy model nodes based on the association relationship between the energy data;

[0021] Construct an initial energy structure tree model based on the association relationship between the energy model nodes;

[0022] Traverse the initial energy structure tree model to obtain energy model node information;

[0023] Reconstruct the initial energy structure tree model based on the energy model node information to obtain an energy structure tree model;

[0024] Determine the node mapping relationship between energy model nodes based on the association relationship between the energy model nodes in the reconstructed energy structure tree model.

[0025] In one embodiment, the step of determining the energy conversion weight between the first model node and the second model node according to the participating tuples and the non-participating tuples includes:

[0026] Assign conversion weights to each of the participating tuples and each of the non-participating tuples according to the energy conversion type;

[0027] Determine the conversion participation rate of energy data based on the participation tuples and the non - participation tuples;

[0028] Perform weighted processing based on the conversion weight and the conversion participation rate to determine the energy conversion weight value between the first model node and the second model node.

[0029] In one embodiment, the step of cleaning redundant data in the first model node and the second model node according to the energy conversion weight value includes:

[0030] Set a redundant cleaning threshold for the redundant data cleaning process of the energy system based on the energy conversion weight value;

[0031] Determine the redundant data in the first model node and the second model node based on the redundant cleaning threshold and the conversion weight values of the data lineage triples;

[0032] Clean the redundant data.

[0033] In addition, to achieve the above - mentioned purpose, the present application also proposes an energy system data management device based on lineage analysis, and the energy system data management device based on lineage analysis includes:

[0034] A lineage analysis module, configured to construct data lineage triples based on the node mapping relationship between the energy model nodes of the energy structure tree model;

[0035] A tuple management module, configured to determine the participation tuples and non - participation tuples of energy conversion according to the data lineage triples;

[0036] A weight value determination module, configured to determine the energy conversion weight value between the first model node and the second model node according to the participation tuples and the non - participation tuples; the first model node is the precursor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship;

[0037] A data management module, configured to clean the redundant data in the first model node and the second model node according to the energy conversion weight value.

[0038] In addition, to achieve the above - mentioned purpose, the present application also proposes an energy system data management device based on lineage analysis, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the energy system data management method based on lineage analysis as described above.

[0039] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the energy system data management method based on blood relationship analysis described above are implemented.

[0040] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the energy system data management method based on blood relationship analysis described above are implemented.

[0041] One or more technical solutions proposed by the present application have at least the following technical effects:

[0042] The present application constructs a data blood relationship triple based on the node mapping relationship between each energy model node of the energy structure tree model; determines the participating tuples and non-participating tuples of energy conversion according to the data blood relationship triple; determines the energy conversion weight value between the first model node and the second model node according to the participating tuples and non-participating tuples; and cleans the redundant data in the first model node and the second model node according to the energy conversion weight value. Since the blood relationship analysis of energy data is realized by constructing the energy structure tree model and the data blood relationship triple, the data transparency in the energy data conversion process of the energy system is improved, making the management of the energy data conversion and storage process more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the energy system data management method based on blood relationship analysis of the present application;

[0046] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the energy system data management method based on blood relationship analysis of the present application;

[0047] Figure 3 It is a schematic flowchart provided for Embodiment 3 of the energy system data management method based on blood relationship analysis of the present application;

[0048] Figure 4 This is a schematic diagram of the module structure of the energy system data management device based on blood relationship analysis in the embodiments of the present application;

[0049] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the energy system data management method based on blood relationship analysis in the embodiments of the present application.

[0050] The implementation, functional features, and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific embodiments.

[0053] The main solution of the embodiments of the present application is as follows: constructing a data blood relationship triple based on the node mapping relationship between each energy model node of the energy structure tree model; determining the participating tuples and non-participating tuples of energy conversion according to the data blood relationship triple; determining the energy conversion weight value between the first model node and the second model node according to the participating tuples and non-participating tuples, where the first model node is the precursor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship; cleaning the redundant data in the first model node and the second model node according to the energy conversion weight value.

[0054] Currently, due to the multi-source and diversification of energy data in the prior art, it usually has the characteristics of large scale and complexity, which increases the difficulty of blood relationship analysis, makes the content of the database complex and diverse, and leads to the problem of difficult data parsing.

[0055] The present application provides a solution. Since the node mapping relationship between each energy model node is described by constructing a data blood relationship triple, the data correlation and traceability in the energy data conversion process are enhanced. Based on the data blood relationship triple, it is possible to accurately identify which energy data participates in the energy conversion process, and then determine the weight value in the energy conversion, realizing the cleaning of redundant data and avoiding confusion in subsequent calculations and analyses.

[0056] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a computer, a server, a cloud data center, etc., or an electronic device or a virtual device that can implement the above functions. Hereinafter, the energy system data management device based on blood relationship analysis (hereinafter referred to as the management device) will be used as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the embodiments of the present application provide a method for managing energy system data based on blood relationship analysis. Referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for managing energy system data based on blood relationship analysis in the present application.

[0058] In this embodiment, the method for managing energy system data based on blood relationship analysis includes steps S10 to S40:

[0059] Step S10, constructing a data blood relationship triple based on the node mapping relationship between the energy model nodes of the energy structure tree model.

[0060] It should be noted that the energy structure tree model is a tree-structured data model. In the energy structure tree model, it can include energy model nodes and edges connecting two energy model nodes. In any energy model node of the energy structure tree model, it can include at least one type of energy; for the edge connecting two energy model nodes, that is, the node mapping relationship between the two energy model nodes, it can be regarded as the process of energy conversion / transmission.

[0061] It can be understood that in the energy system, there can be several energy partitions, and each energy partition can be regarded as an energy regulation area of the energy system. For each energy regulation area, it can include at least one type of energy, and the energy regulation area including the at least one type of energy can be regarded as an energy model node.

[0062] In the embodiments of the present application, a data blood relationship triple T = (R in , R out , σ) can be constructed based on the conversion relationship of each energy data between the energy model nodes. Among them, R in represents a type of energy data in the first model node, R out represents a type of energy data in the second model node, and σ represents the conversion rule of energy data (that is, the node mapping relationship).

[0063] It should be noted that the above node mapping relationship can correspond to basic relational operations (such as selection, projection, join, set union, intersection, and difference operations, etc.) or energy conversion relationships.

[0064] Step S20, determining the participating tuples and non-participating tuples of energy conversion according to the data blood relationship triple.

[0065] It should be noted that in the energy system of the embodiments of the present application, the above-mentioned participating tuples are the data lineage triples that directly participate in energy conversion and energy transfer, and the non-participating tuples are the data lineage triples that do not participate or indirectly participate in energy conversion and energy transfer.

[0066] It can be understood that by determining the participating tuples and non-participating tuples in the energy conversion process, the energy conversion process can be monitored and managed more accurately, while improving the data transparency in the energy data conversion process of the energy system, thereby realizing the optimization of the energy conversion process.

[0067] Step S30, determining the energy conversion weight between the first model node and the second model node according to the participating tuple and the non-participating tuple; the first model node is the predecessor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship;

[0068] Step S40, cleaning the redundant data in the first model node and the second model node according to the energy conversion weight.

[0069] It should be noted that in a segment of node mapping relationship, there may be a predecessor node and a successor node, where the predecessor node can be regarded as the R of the data lineage triple in , and the successor node can be regarded as the R of the data lineage triple out . The above energy conversion weight can be used to represent the energy conversion ratio of an energy model node to another energy model node. This energy conversion weight can be used to reflect the energy loss, conversion efficiency, and conversion relationship between different energy forms in the energy conversion process. Based on the energy conversion weight, redundant data can be screened out from the energy data of the first model node, and then the redundant data can be deleted or merged to achieve the cleaning of the redundant data. In the screening process, comprehensive evaluations can also be made considering data quality, data integrity, etc., and the embodiments of the present application do not limit this.

[0070] In the embodiments of the present application, data lineage triples are constructed based on the node mapping relationship between the energy model nodes of the energy structure tree model; the participating tuples and non-participating tuples of energy conversion are determined according to the data lineage triples; the energy conversion weight between the first model node and the second model node is determined according to the participating tuples and non-participating tuples; the redundant data in the first model node and the second model node is cleaned according to the energy conversion weight. Since the blood relationship analysis of energy data is realized by constructing the energy structure tree model and data lineage triples, the data transparency in the energy data conversion process of the energy system is improved, making the management of the energy data conversion and storage process more convenient.

[0071] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as that in the above-mentioned first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , in the embodiment of the present application, the steps of constructing a data lineage triple based on the node mapping relationship between each energy model node of the energy structure tree model include:

[0072] Step S11, generating energy model nodes based on the conversion hierarchy of energy data.

[0073] It should be noted that in the energy system of the embodiment of the present application, there may be several energy partitions, and each energy partition may correspond to a conversion hierarchy of energy data. Corresponding to different conversion hierarchies of energy data, there may be different association relationships between two energy partitions. Specifically, in an energy structure tree model, there may be a primary energy level, a secondary energy level, and a tertiary energy level. Among them, the energy in the primary energy level can be energy that can be directly obtained from nature, such as solar energy, wind energy, etc.; while the energy in the secondary energy level can be energy obtained by converting the energy in the primary energy level, such as electrical energy obtained by converting solar energy, electrical energy obtained by converting wind energy, etc.; the energy in the tertiary energy level can be energy obtained by converting the energy in the secondary energy level, such as chemical energy obtained by converting electrical energy, etc., and the embodiment of the present application does not limit this.

[0074] It should be explained that for energy partitions corresponding to different conversion levels, their energy model nodes may correspond to different energy conversion factors and association relationships.

[0075] Step S12, determining the node mapping relationship between energy model nodes according to the association relationship between the energy data and the energy conversion factor.

[0076] It can be understood that there are different loss ratios and conversion efficiency ratios between energy data at different energy conversion levels, that is, the association relationship between energy data. This association relationship can be obtained by analyzing and statistically processing historical conversion data, or can be quantified based on a machine learning model, and the embodiment of the present application does not limit this. The above-mentioned energy conversion factor is the efficiency or ratio of energy conversion obtained after quantifying the association relationship.

[0077] It should be noted that by establishing the node mapping relationship between energy model nodes, the accuracy of the energy structure tree model of the energy system is improved.

[0078] Step S13, constructing an energy conversion data table between the first model node and the second model node based on the node mapping relationship;

[0079] Step S14: Construct data triples according to the energy conversion data table.

[0080] It should be noted that based on the node mapping relationship, an energy conversion data table can be constructed. Through this energy conversion data table, the energy data participating in energy conversion and the energy data not participating in energy conversion can be determined. At the same time, the loss amount during the energy conversion process can be clarified, realizing a clear data display of the energy conversion process. Based on the energy conversion data table, data triples of energy conversion can be constructed.

[0081] In some embodiments of the embodiments of the present application, the step of determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triples includes: determining the energy conversion participating data and energy conversion non-participating data in the energy conversion data table based on the data triples; determining the participating tuples of energy conversion in the data triples based on the energy conversion participating data; determining the non-participating tuples of energy conversion in the data triples based on the energy conversion non-participating data.

[0082] It should be noted that by determining the energy conversion participating data and energy conversion non-participating data traced by the energy conversion data table based on the data triples, the participating tuples and non-participating tuples in the energy conversion value can be determined, which helps to better understand the structure and performance characteristics of the energy system and is convenient for the optimization and management of the energy system.

[0083] In some embodiments of the embodiments of the present application, the step of determining the node mapping relationship between energy model nodes according to the association relationship between the energy data and the energy conversion factor includes: determining the association relationship between the energy model nodes based on the association relationship between the energy data; constructing an initial energy structure tree model based on the association relationship between the energy model nodes; traversing the initial energy structure tree model to obtain energy model node information; reconstructing the initial energy structure tree model based on the energy model node information to obtain an energy structure tree model; determining the node mapping relationship between energy model nodes based on the association relationship between the energy model nodes in the reconstructed energy structure tree model.

[0084] It should be noted that several types of energy data can be included in the energy model nodes. Based on these types of energy data in different energy model nodes and the association relationships between these energy data in different energy nodes, the association relationships between the energy model nodes can be determined. Furthermore, an initial energy structure tree model can be constructed. By traversing the initial energy structure tree model, energy model node information such as the types of energy data, the amount of energy data, and the energy data loss amount included in each energy model node can be obtained. By performing operations such as normalization, merging, splitting, and rearrangement on the energy model node information in the energy model nodes, the reconstruction of the energy connection and transmission model can be realized, and the reconstructed energy structure tree model can be obtained.

[0085] It can be understood that through the reconstructed energy structure tree model and the association relationships between the energy model nodes, the node mapping relationships between the energy model nodes can be further determined.

[0086] In some implementation manners of the embodiments of the present application, the step of cleaning redundant data in the first model node and the second model node according to the energy conversion weight includes: setting a redundancy cleaning threshold for the redundant data cleaning process of the energy system based on the energy conversion weight; determining the redundant data in the first model node and the second model node based on the redundancy cleaning threshold and the conversion weights of each data lineage triple; cleaning the redundant data.

[0087] It should be noted that through the energy conversion weight, the management device can determine which energy data is valuable in the energy conversion process, thereby realizing the cleaning of redundant data. Specifically, the energy system can set a redundancy cleaning threshold, and through this redundancy cleaning threshold, it can be distinguished which data is redundant.

[0088] In the embodiments of the present application, energy model nodes are generated based on the conversion levels of energy data; the node mapping relationships between the energy model nodes are determined according to the association relationships between the energy data and the energy conversion factors; an energy conversion data table between the first model node and the second model node is constructed based on the node mapping relationships; and data triples are constructed according to the energy conversion data table. Since the management of energy data is realized by constructing data triples, the difficulty of energy data analysis is reduced, and the convenience of energy data analysis is improved.

[0089] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar content as that in the above-mentioned first embodiment and / or the second embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3, the step of determining the energy conversion weight between the first model node and the second model node according to the participating tuple and the non-participating tuple includes:

[0090] Step S100, assigning conversion weights to each of the participating tuples and each of the non-participating tuples according to the energy conversion type;

[0091] Step S200, determining the conversion participation rate of the energy data based on the participating tuple and the non-participating tuple;

[0092] Step S300, performing a weighting process based on the conversion weight and the conversion participation rate to determine the energy conversion weight between the first model node and the second model node.

[0093] It should be noted that for different energy conversion types, there can be different energy conversion weights. For example, the energy conversion weight for converting solar energy into electrical energy can be a, and the energy conversion weight for converting electrical energy into chemical energy can be b, etc. In practical applications, a conversion weight table for characterizing the energy conversion weights corresponding to different energy conversion types can be preset, and the initial conversion weight ω corresponding to the energy conversion type can be determined through this conversion weight table.

[0094] It should be explained that for the participating tuple and the non-participating tuple, their corresponding actual conversion weights can be different. Among them, for the same energy conversion type, if the input energy data R in is a participating tuple, its corresponding actual conversion weight can be ω; if the input energy data R in is a participating tuple, its corresponding actual conversion weight can be mω. Among them, the value of m ranges from 0.4 to 0.8, and its specific value can be limited according to the requirements in practical applications. The embodiments of the present application do not limit this.

[0095] It should be noted that since the corresponding equipment aging degrees and reaction effects of different energy partitions are different, there are different energy aging partitions, and the conversion participation rates of the energy data are different. This conversion participation rate can be obtained by monitoring the equipment aging degree and the like in the energy partition. The embodiments of the present application do not limit this.

[0096] It should be explained that by weighting based on the conversion weight and the conversion participation rate, the energy conversion weight between the first model node and the second model node can be determined. Specifically, it can be as follows:

[0097] M = σω1 + μn;

[0098] Among them, M is the energy conversion weight, ω1 is the actual conversion weight of the data lineage triple, n is the conversion participation rate corresponding to the first model node, σ is the weighting ratio corresponding to the actual conversion weight, μ is the weighting ratio corresponding to the conversion participation rate, and the sum of μ and σ is 1.

[0099] In the embodiment of the present application, conversion weights are assigned to each of the participating tuples and each of the non-participating tuples according to the energy conversion type; the conversion participation rate of the energy conversion data is determined based on the participating tuples and the non-participating tuples; and weighted processing is performed based on the conversion weights and the conversion participation rate to determine the energy conversion weight between the first model node and the second model node. Since the redundant data is cleaned by calculating the energy conversion weight, the accuracy of energy data management is improved.

[0100] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the energy system data management method based on lineage analysis of the present application. Any simple transformation in more forms based on this technical concept is within the protection scope of the present application.

[0101] The present application also provides an energy system data management device based on lineage analysis. Please refer to Figure 4 , the energy system data management device based on lineage analysis includes:

[0102] The lineage analysis module 10 is used to construct data lineage triples based on the node mapping relationship between each energy model node of the energy structure tree model;

[0103] The tuple management module 20 is used to determine the participating tuples and non-participating tuples of energy conversion according to the data lineage triples;

[0104] The weight determination module 30 is used to determine the energy conversion weight between the first model node and the second model node according to the participating tuples and the non-participating tuples; the first model node is the predecessor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship;

[0105] The data management module 40 is used to clean the redundant data in the first model node and the second model node according to the energy conversion weight.

[0106] The energy system data management device based on blood relationship analysis provided by the present application adopts the energy system data management method based on blood relationship analysis in the above embodiment, which can solve the technical problems of multi-source and diversification, making the content of the database complex and diverse, resulting in difficult data parsing. Compared with the prior art, the beneficial effects of the energy system data management device based on blood relationship analysis provided by the present application are the same as those of the energy system data management method based on blood relationship analysis provided by the above embodiment, and other technical features in the energy system data management device based on blood relationship analysis are the same as the features disclosed in the method of the above embodiment, which will not be elaborated here.

[0107] The present application provides an energy system data management device based on blood relationship analysis. The energy system data management device based on blood relationship analysis includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the energy system data management method based on blood relationship analysis in the first embodiment above.

[0108] Next, refer to Figure 5 , which shows a schematic structural diagram of an energy system data management device suitable for implementing the embodiments of the present application. The energy system data management device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The shown energy system data management device is merely an example and should not impose any limitations on the functions and usage scopes of the embodiments of the present application.

[0109] As Figure 5As shown, the energy system data management device based on lineage analysis may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the energy system data management device based on lineage analysis are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the energy system data management device based on lineage analysis to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an energy system data management device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be implemented or had alternatively.

[0110] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0111] The energy system data management device based on blood relationship analysis provided by this application adopts the energy system data management method based on blood relationship analysis in the above embodiment, which can solve the technical problems of multi-source and diversification, making the database content complex and diverse, resulting in difficult data parsing. Compared with the prior art, the beneficial effects of the energy system data management device based on blood relationship analysis provided by this application are the same as those of the energy system data management method based on blood relationship analysis provided by the above embodiment, and other technical features in the energy system data management device based on blood relationship analysis are the same as those disclosed in the method of the previous embodiment, which will not be elaborated here.

[0112] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0113] As mentioned above, only the specific embodiments of this application are described, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0114] This application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the energy system data management method based on blood relationship analysis in the above embodiment.

[0115] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0116] The above computer-readable storage medium may be included in the energy system data management device based on lineage analysis; or it may exist independently without being assembled into the energy system data management device based on lineage analysis.

[0117] The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the energy system data management device based on lineage analysis, the energy system data management device based on lineage analysis is caused to:

[0118] Construct data lineage triples based on the node mapping relationship between the energy model nodes of the energy structure tree model;

[0119] Determine the participating tuples and non-participating tuples of energy conversion according to the data lineage triples;

[0120] Determine the energy conversion weight value between the first model node and the second model node according to the participating tuples and the non-participating tuples; the first model node is the predecessor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship;

[0121] Clean up the redundant data in the first model node and the second model node according to the energy conversion weight value.

[0122] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0124] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0125] The readable storage medium provided in this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned energy system data management method based on blood relationship analysis, and can solve the technical problems of multi-source and diversification, making the database content complex and diverse, resulting in difficult data parsing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the energy system data management method based on blood relationship analysis provided in the above embodiments, and will not be elaborated here.

[0126] The present application also provides a computer program product, including a computer program, which implements the steps of the energy system data management method based on blood relationship analysis as described above when executed by a processor.

[0127] The computer program product provided by the present application can solve the technical problems of multi-source and diversification, which make the content of the database complex and diverse, resulting in difficult data parsing. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the energy system data management method based on blood relationship analysis provided in the above embodiments, and will not be elaborated here.

[0128] The above are only some embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural transformation made by using the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for managing energy system data based on blood relationship analysis, characterized in that, The method includes: Constructing a data lineage triple based on the node mapping relationship between each energy model node of the energy structure tree model; Determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triple; Determining the energy conversion weight between the first model node and the second model node according to the participating tuples and the non-participating tuples; the first model node is the precursor node in a segment of node mapping relationship, and the second model node is the successor node in a segment of node mapping relationship; Cleaning redundant data in the first model node and the second model node according to the energy conversion weight; The step of constructing a data lineage triple based on the node mapping relationship between each energy model node of the energy structure tree model includes: Generating energy model nodes based on the conversion levels of energy data; the conversion levels include: primary energy level, secondary energy level, and tertiary energy level; Determining the node mapping relationship between energy model nodes according to the correlation relationship between the energy data and the energy conversion factor; Constructing an energy conversion data table between the first model node and the second model node based on the node mapping relationship; Constructing a data lineage triple according to the energy conversion data table; The step of determining the node mapping relationship between energy model nodes according to the correlation relationship between the energy data and the energy conversion factor includes: Determining the correlation relationship between the energy model nodes based on the correlation relationship between the energy data; Constructing an initial energy structure tree model based on the correlation relationship between the energy model nodes; Traversing the initial energy structure tree model to obtain energy model node information; Reconstructing the initial energy structure tree model based on the energy model node information to obtain an energy structure tree model; Determining the node mapping relationship between energy model nodes based on the correlation relationship between the energy model nodes in the reconstructed energy structure tree model; The step of determining the energy conversion weight between the first model node and the second model node according to the participating tuples and the non-participating tuples includes: Assigning conversion weights to each of the participating tuples and each of the non-participating tuples according to the energy conversion type; Determining the conversion participation rate of energy data based on the participating tuples and the non-participating tuples; Performing a weighted process based on the conversion weight and the conversion participation rate to determine the energy conversion weight between the first model node and the second model node.

2. The method for managing energy system data based on blood relationship analysis according to claim 1, wherein, The step of determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triple includes: Determining the energy conversion participating data and energy conversion non-participating data in the energy conversion data table based on the data lineage triple; Determining the participating tuples of energy conversion in the data lineage triple based on the energy conversion participating data; Determining the non-participating tuples of energy conversion in the data lineage triple based on the energy conversion non-participating data.

3. The method for managing energy system data based on blood relationship analysis according to claim 1, wherein, The step of cleaning redundant data in the first model node and the second model node according to the energy conversion weight includes: Set a redundancy cleaning threshold for the redundant data cleaning process of the energy system based on the energy conversion weight value; Determine the redundant data in the first model node and the second model node based on the redundancy cleaning threshold and the conversion weights of the respective data lineage triples; Clean the redundant data.

4. An energy system data management device based on blood relationship analysis, characterized in that, The energy system data management device based on lineage analysis includes: A lineage analysis module for constructing data lineage triples based on the node mapping relationships between the energy model nodes of the energy structure tree model; A tuple management module for determining the participating tuples and non-participating tuples in the energy conversion according to the data lineage triples; A weight determination module for determining the energy conversion weight value between the first model node and the second model node according to the participating tuples and the non-participating tuples; the first model node is the precursor node in a segment of node mapping relationships, and the second model node is the successor node in a segment of node mapping relationships; A data management module for cleaning the redundant data in the first model node and the second model node according to the energy conversion weight value; The constructing of data lineage triples based on the node mapping relationships between the energy model nodes of the energy structure tree model includes: Generating energy model nodes based on the conversion levels of energy data; the conversion levels include: primary energy level, secondary energy level, and tertiary energy level; Determining the node mapping relationships between the energy model nodes according to the association relationships between the energy data and the energy conversion factors; Constructing an energy conversion data table between the first model node and the second model node based on the node mapping relationships; Constructing data lineage triples according to the energy conversion data table; The determining of the node mapping relationships between the energy model nodes according to the association relationships between the energy data and the energy conversion factors includes: Determining the association relationships between the energy model nodes based on the association relationships between the energy data; Constructing an initial energy structure tree model based on the association relationships between the energy model nodes; Traversing the initial energy structure tree model to obtain energy model node information; Reconstructing the initial energy structure tree model based on the energy model node information to obtain an energy structure tree model; Determining the node mapping relationships between the energy model nodes based on the association relationships between the energy model nodes in the reconstructed energy structure tree model; The determining of the energy conversion weight value between the first model node and the second model node according to the participating tuples and the non-participating tuples includes: Assigning conversion weights to the respective participating tuples and non-participating tuples according to the energy conversion type; Determining the conversion participation rate of the energy data based on the participating tuples and the non-participating tuples; Performing a weighting process based on the conversion weights and the conversion participation rate to determine the energy conversion weight value between the first model node and the second model node.

5. An energy system data management device based on blood relationship analysis, characterized in that, The device includes: a memory, a processor, and an energy system data management program based on lineage analysis stored on the memory and executable on the processor, the energy system data management program based on lineage analysis being configured to implement the steps of the energy system data management method based on lineage analysis according to any one of claims 1 to 3.

6. A storage medium, characterized in that, An energy system data management program based on lineage analysis is stored on the storage medium, and when the energy system data management program based on lineage analysis is executed by a processor, the steps of the energy system data management method based on lineage analysis according to any one of claims 1 to 3 are implemented.

7. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the energy system data management method based on lineage analysis according to any one of claims 1 to 3 are implemented.

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