Energy system data management method, device and equipment based on consanguinity analysis and storage medium

Abstract

The invention discloses an energy system data management method, device and equipment based on consanguinity analysis and a storage medium, and relates to the technical field of data processing.The energy system data management method based on consanguinity analysis comprises the steps that a data consanguinity triple is constructed based on the node mapping relation between energy model nodes of an energy structure tree model; determining a participating tuple and a non-participating tuple of energy conversion according to the data consanguinity triple; determining an energy conversion weight between the first model node and the second model node according to the participating tuple and the non-participating tuple; and cleaning redundant data in the first model node and the second model node according to the energy conversion weight. The blood relationship analysis of the energy data is realized by constructing the energy structure tree model and the data blood relationship triple, so that the data transparency in the energy data conversion process of the energy system is improved, and the energy data conversion and storage processes are more convenient to manage.

Description

Technical Field

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

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

[0003] At present, data lineage analysis, as an important means of data management, can provide a historical record of data source conversion, thereby helping the energy system to conduct attribution analysis, impact analysis, usage analysis and link combing, making it easier to quickly locate the source of data errors and data problems, which is of great significance to data governance. However, due to the long-term multi-source and diversified nature of energy data, the database content is complex and diverse, leading to the problem of difficult data analysis. Summary of the invention

[0004] The main purpose of this application is to provide an energy system data management method, device, equipment and storage medium based on lineage analysis, aiming to solve the technical problems of multi-source and diversity, which make the database content complex and diverse and lead to difficulty in data analysis.

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

[0006] Construct data lineage triples based on the node mapping relationship between energy model nodes in the energy structure tree model;

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

[0008] Determine an energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship;

[0009] Redundant data in the first model node and the second model node are cleaned up according to the energy conversion weight.

[0010] In one embodiment, the step of constructing data lineage triples based on the node mapping relationship between energy model nodes in the energy structure tree model includes:

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

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

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

[0014] A data triplet is constructed according to the energy conversion data table.

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

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

[0017] Determining a participant tuple of energy conversion in the data triplet based on the energy conversion participant data;

[0018] A non-participating tuple of energy conversion is determined in the data triple based on the non-participating data of energy conversion.

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

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

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

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

[0024] A node mapping relationship between energy model nodes is determined 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 tuple and the non-participating tuple includes:

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

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

[0028] A weighted processing is performed 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.

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

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

[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 of each of the data lineage triples;

[0032] The redundant data is cleaned up.

[0033] In addition, to achieve the above 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 is used to construct data lineage triples based on the node mapping relationship between the energy model nodes in the energy structure tree model;

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

[0036] A weight determination module, used to determine the energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship;

[0037] A data management module is used to clean up redundant data in the first model node and the second model node according to the energy conversion weight.

[0038] In addition, to achieve the above-mentioned objectives, the present application also proposes an energy system data management device based on lineage analysis, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being 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-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the energy system data management method based on bloodline analysis as described above are implemented.

[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the energy system data management method based on bloodline analysis as described above.

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

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

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 A flow chart of the first embodiment of the energy system data management method based on lineage analysis provided in this application;

[0046] Figure 2 A flow chart of the second embodiment of the energy system data management method based on lineage analysis provided in this application;

[0047] Figure 3 A flow chart of the energy system data management method based on lineage analysis provided in Example 3 of the present application;

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

[0049] Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the energy system data management method based on lineage analysis in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[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] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: constructing data lineage triples based on the node mapping relationship between each energy model node in the energy structure tree model; determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triples; determining the energy conversion weight between the first model node and the second model node according to the participating tuples and non-participating tuples; the first model node is the predecessor node in a node mapping relationship, and the second model node is the successor node in a node mapping relationship; and cleaning up the redundant data in the first model node and the second model node according to the energy conversion weight.

[0054] Due to the multi-source and diversified nature of energy data, current existing technologies usually have the characteristics of large-scale and complexity, which increases the difficulty of lineage analysis, makes the database content complex and diverse, and leads to difficulties in data analysis.

[0055] This application provides a solution that describes the node mapping relationship between each energy model node by constructing a data lineage triplet, thereby enhancing the data relevance and traceability in the energy data conversion process. Based on the data lineage triplet, it is possible to accurately identify which energy data is involved in the energy conversion process, and then determine the weight in the energy conversion, thereby cleaning up redundant data and avoiding confusion in subsequent calculations and analysis.

[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, server, cloud data center, etc., or an electronic device, virtual device, etc. that can realize the above functions. The following takes the energy system data management device based on lineage analysis (hereinafter referred to as the management device) as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the embodiment of the present application provides an energy system data management method based on lineage analysis, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the energy system data management method based on lineage analysis of the present application.

[0058] In this embodiment, the energy system data management method based on lineage analysis includes steps S10 to S40:

[0059] Step S10: constructing data lineage triples based on the node mapping relationship between the energy model nodes in the energy structure tree model.

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

[0061] It is understandable that in an energy system, several energy partitions may be included, wherein each energy partition may be regarded as an energy control area of ​​the energy system. For each energy control area, at least one type of energy may be included, and the energy control area including the at least one type may be regarded as an energy model node.

[0062] In the embodiment of the present application, a data lineage triplet T=(R in ,R out ,σ). 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 (ie, the node mapping relationship).

[0063] It should be noted that the above-mentioned node mapping relationship may correspond to a basic relational operation (such as selection, projection, connection, set intersection and difference operation, etc.) or an energy conversion relationship.

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

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

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

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

[0069] It should be noted that a node mapping relationship may include a predecessor node and a successor node, where the predecessor node can be regarded as the R of the data lineage triple. in , the successor node can be regarded as the R of the data lineage triplet out . The above-mentioned energy conversion weight can be used to characterize the energy conversion ratio of one energy model node to another energy model node, and the 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 in the energy data of the first model node, and then the redundant data can be deleted or merged to achieve the cleaning of redundant data. In the screening process, a comprehensive evaluation can also be taken into account, such as data quality and data integrity, which is not limited in this embodiment of the present application.

[0070] The embodiment of the present application constructs a data lineage triplet based on the node mapping relationship between each energy model node of the energy structure tree model; determines the participating tuple and non-participating tuple of energy conversion according to the data lineage triplet; determines the energy conversion weight between the first model node and the second model node according to the participating tuple and the non-participating tuple; and cleans up the redundant data in the first model node and the second model node according to the energy conversion weight. Since the lineage analysis of energy data is realized by constructing the energy structure tree model and the data lineage triplet, the data transparency in the energy data conversion process of the energy system is improved, making the management of the conversion and storage process of energy data more convenient.

[0071] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 In the embodiment of the present application, the step of constructing a data lineage triple based on the node mapping relationship between the energy model nodes of the energy structure tree model includes:

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

[0073] It should be noted that in the energy system of the embodiment of the present application, several energy partitions may be included, each of which may correspond to a conversion level of energy data. Corresponding to different conversion levels of energy data, there may be different associations between two energy partitions. Specifically, in an energy structure tree model, a primary energy level, a secondary energy level, and a tertiary energy level may be included, wherein the energy in the primary energy level may be energy that can be directly obtained from nature, such as solar energy, wind energy, etc.; and the energy in the secondary energy level may be energy obtained by converting energy in the primary energy level, such as electrical energy obtained by converting solar energy, electrical energy obtained by converting wind energy, etc.; and the energy in the tertiary energy level may be energy obtained by converting 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 impose any restrictions on this.

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

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

[0076] It is understandable that there are different loss ratios and conversion efficiency ratios between energy data at different energy conversion levels, that is, the correlation between energy data, and this correlation can be obtained by analyzing and counting historical conversion data, or it can be quantified based on a machine learning model, which is not limited in this embodiment of the present application. The above energy conversion factor is the efficiency or ratio of energy conversion obtained after quantifying the correlation.

[0077] It should be noted that by establishing a 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, constructing a data triplet according to the energy conversion data table.

[0080] It should be noted that an energy conversion data table can be constructed based on the node mapping relationship. Through the energy conversion data table, the energy data involved in energy conversion and the energy data not involved in energy conversion can be determined, and the loss amount in the energy conversion process can be clarified to achieve a clear data display of the energy conversion process. Based on the energy conversion data table, a data triple 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 based on the data lineage triples includes: determining the energy conversion participating data and the 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 data triples, the participating tuples and non-participating tuples of the energy conversion median can be determined, which helps to better understand the structure and performance characteristics of the energy system and facilitate the optimization and management of the energy system.

[0083] In some implementations of the embodiments of the present application, the step of determining the node mapping relationship between the energy model nodes based on 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; and determining the node mapping relationship between the 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 the energy model node may include several types of energy data. Based on these types of energy data in different energy model nodes and the correlation between these energy data in different energy nodes, the correlation between energy model nodes can be determined. Then, an initial energy structure tree model is constructed. By traversing the initial energy structure tree model, energy model node information such as energy data type, energy data volume, energy data loss volume, etc. included in each energy model node can be obtained. By normalizing, merging, splitting, and rearranging the energy model node information in the energy model node, the energy input model can be reconstructed to obtain the reconstructed energy structure tree model.

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

[0086] In some implementations of the embodiments of the present application, the step of cleaning up the redundant data in the first model node and the second model node according to the energy conversion weight includes: setting a redundant 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 redundant cleaning threshold and the conversion weight of each of the data lineage triples; and cleaning up 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, and then clean up the redundant data. Specifically, the energy system can set a redundant cleaning threshold, through which it can distinguish which data is redundant.

[0088] The embodiment of the present application generates energy model nodes by conversion levels based on energy data; determines the node mapping relationship between energy model nodes according to the association relationship between energy data and energy conversion factors; constructs an energy conversion data table between a first model node and a second model node based on the node mapping relationship; and constructs a data triplet according to the energy conversion data table. Since energy data is managed 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 contents as those of the above-mentioned first embodiment and / or the second embodiment can refer to the above introduction, and will not be repeated later. 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 energy conversion types;

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

[0092] Step S300: performing weighted processing 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 different energy conversion types may correspond to different energy conversion weights, such as the energy conversion weight of solar energy to electrical energy may be a, the energy conversion weight of electrical energy to chemical energy may be b, etc. In practical applications, a conversion weight table may be preset to characterize the energy conversion weights corresponding to different energy conversion types, and the initial conversion weight ω corresponding to the energy conversion type may be determined through the conversion weight table.

[0094] It should be explained that for participating tuples and non-participating tuples, the corresponding actual conversion weights may be different. in is a participating tuple, then its corresponding actual conversion weight can be ω; if the input energy data R in is a participating tuple, then its corresponding actual conversion weight may be mω. Wherein, the value of m is 0.4-0.8, and its specific value may be limited according to the requirements in practical applications, and the embodiment of the present application does not impose any limitation on this.

[0095] It should be noted that, since different energy partitions have different corresponding equipment aging degrees and reaction effects, the conversion participation rates of energy data in different energy aging partitions are different. The conversion participation rate can be obtained based on monitoring the aging degree of equipment in the energy partition, etc., and the embodiments of the present application are not limited to this.

[0096] It should be explained that the energy conversion weight between the first model node and the second model node can be determined by weighting based on the conversion weight and the conversion participation rate. 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 triplet, n is the conversion participation rate corresponding to the first model node, σ is the weighted ratio corresponding to the actual conversion weight, μ is the weighted ratio corresponding to the conversion participation rate, and the sum of μ and σ is 1.

[0099] The embodiment of the present application allocates conversion weights to each participating tuple and each non-participating tuple according to the energy conversion type; determines the conversion participation rate of energy conversion data based on the participating tuple and the non-participating tuple; and performs weighted processing 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. Since the redundant data is cleaned up 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 used to understand the present application and do not constitute a limitation on the energy system data management method based on bloodline analysis of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0101] This 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 blood relationship analysis includes:

[0102] A lineage analysis module 10, for constructing data lineage triples based on the node mapping relationship between the energy model nodes of the energy structure tree model;

[0103] A tuple management module 20, for determining a participating tuple and a non-participating tuple of energy conversion according to the data lineage triplet;

[0104] A weight determination module 30, for determining an energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship;

[0105] The data management module 40 is used to clean up 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 lineage analysis provided by the present application adopts the energy system data management method based on lineage analysis in the above-mentioned embodiment, which can solve the technical problem that multi-source and diversification make the database content complex and diverse, resulting in difficulty in data analysis. Compared with the prior art, the beneficial effects of the energy system data management device based on lineage analysis provided by the present application are the same as the beneficial effects of the energy system data management method based on lineage analysis provided by the above-mentioned embodiment, and the other technical features of the energy system data management device based on lineage analysis are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0107] The present application provides an energy system data management device based on lineage analysis, and the energy system data management device based on lineage 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 so that the at least one processor can execute the energy system data management method based on lineage analysis in the above-mentioned embodiment one.

[0108] Reference below Figure 5 , which shows a schematic diagram of the structure of an energy system data management device based on lineage analysis suitable for implementing the embodiment of the present application. The energy system data management device based on lineage analysis in the embodiment 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 Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The energy system data management device based on bloodline analysis shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

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

[0110] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. 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 includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0111] The energy system data management device based on lineage analysis provided by the present application adopts the energy system data management method based on lineage analysis in the above-mentioned embodiment, which can solve the technical problems of multi-source and diversification, which make the database content complex and diverse, and lead to difficulty in data analysis. Compared with the prior art, the beneficial effects of the energy system data management device based on lineage analysis provided by the present application are the same as the beneficial effects of the energy system data management method based on lineage analysis provided by the above-mentioned embodiment, and the other technical features of the energy system data management device based on lineage analysis are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0112] It should be understood that the various parts 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 any one or more embodiments or examples in a suitable manner.

[0113] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0114] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the energy system data management method based on lineage analysis in the above-mentioned embodiment.

[0115] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. 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-mentioned 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 computer-readable storage medium carries one or more programs. When the 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:

[0118] Construct data lineage triples based on the node mapping relationship between energy model nodes in 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 an energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship;

[0121] Redundant data in the first model node and the second model node are cleaned up according to the energy conversion weight.

[0122] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0125] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned energy system data management method based on lineage analysis, and can solve the technical problems of multi-source and diversification, which make the database content complex and diverse, and lead to difficulties in data analysis. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the energy system data management method based on lineage analysis provided by the above-mentioned embodiment, and will not be repeated here.

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

[0127] The computer program product provided by this application can solve the technical problem that multi-source and diversification make the database content complex and diverse, leading to difficulty in data analysis. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the energy system data management method based on blood relationship analysis provided by the above embodiment, which will not be repeated here.

[0128] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for energy system data management based on lineage analysis, characterized in that: The method comprises: Construct data lineage triples based on the node mapping relationship between energy model nodes in the energy structure tree model; Determine the participating tuples and non-participating tuples of energy conversion according to the data lineage triples; Determine an energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship; Redundant data in the first model node and the second model node are cleaned up according to the energy conversion weight.

2. The energy system data management method based on lineage analysis according to claim 1, characterized in that: The step of constructing data lineage triples based on the node mapping relationship between the energy model nodes in the energy structure tree model includes: Generate energy model nodes based on the conversion hierarchy of energy data; Determine a node mapping relationship between energy model nodes according to the association 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; A data triplet is constructed according to the energy conversion data table.

3. The energy system data management method based on blood relationship analysis according to claim 2 is characterized in that: The step of determining the participating tuples and non-participating tuples of energy conversion according to the data lineage triples comprises: Determine the energy conversion participating data and the energy conversion non-participating data in the energy conversion data table based on the data triples; Determining a participant tuple of energy conversion in the data triplet based on the energy conversion participant data; A non-participating tuple of energy conversion is determined in the data triple based on the non-participating data of energy conversion.

4. The energy system data management method based on lineage analysis according to claim 2 is characterized in that: The step of determining the node mapping relationship between the 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; A node mapping relationship between energy model nodes is determined based on the association relationship between the energy model nodes in the reconstructed energy structure tree model.

5. The energy system data management method based on lineage analysis according to claim 1 is characterized in that: 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: Allocating conversion weights to each of the participating tuples and each of the non-participating tuples according to energy conversion types; Determine a conversion participation rate of energy data based on the participating tuple and the non-participating tuple; A weighted processing is performed 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.

6. The energy system data management method based on blood relationship analysis according to claim 4 is characterized in that: The step of cleaning up redundant data in the first model node and the second model node according to the energy conversion weight includes: Setting a redundant cleaning threshold for a redundant data cleaning process of an energy system based on the energy conversion weight; Determine the redundant data in the first model node and the second model node based on the redundant cleaning threshold and the conversion weight of each of the data lineage triples; The redundant data is cleaned up.

7. An energy system data management device based on lineage analysis, characterized in that: The energy system data management device based on blood relationship analysis includes: A lineage analysis module is used to construct data lineage triples based on the node mapping relationship between the energy model nodes in the energy structure tree model; A tuple management module, used to determine the participating tuples and non-participating tuples of energy conversion according to the data lineage triples; A weight determination module, used to determine the energy conversion weight between a first model node and a second model node according to the participating tuple and the non-participating tuple; the first model node is a predecessor node in a node mapping relationship, and the second model node is a successor node in a node mapping relationship; A data management module is used to clean up redundant data in the first model node and the second model node according to the energy conversion weight.

8. An energy system data management device based on lineage analysis, characterized in that: The device includes: a memory, a processor, and an energy system data management program based on lineage analysis stored in the memory and executable on the processor, wherein the energy system data management program based on lineage analysis is configured to implement the steps of the energy system data management method based on lineage analysis as described in any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium stores an energy system data management program based on lineage analysis, and when the energy system data management program based on lineage analysis is executed by the processor, the steps of the energy system data management method based on lineage analysis as described in any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises 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 as described in any one of claims 1 to 7 are implemented.

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