Energy-carbon entropy quantification data model under user-side energy microgrid data optimization structure

Abstract

This invention discloses an energy-carbon entropy quantification data model within a user-side energy microgrid data optimization structure. Based on embedded energy-carbon consumption data, an energy-carbon consumption quantification data entropy algorithm with a universal data format and data link architecture is constructed. This model addresses the lack of objectivity in the underlying data used for energy-carbon consumption data analysis and optimization within user-side microgrid data platforms. By introducing multiple quantifiable data sub-items and their associated data entropy algorithms, this model reduces subjectivity and uncertainty during data analysis, improving the objectivity and accuracy of data processing. This model is applicable not only to the applicant's data platform but also to various user-side energy analysis and optimization data platforms.

Description

Technical Field

[0001] The present invention relates to the technical field of user-side energy structure optimization, and in particular to a quantitative data model suitable for various data-driven user-side energy consumption structure quantitative optimization data platforms, which can be widely used for objective quantitative processing of user-side energy optimization. Background Art

[0002] With the development of data information and intelligent technologies, theoretical research on user-side energy optimization and various application-side data platforms are rapidly developing. Existing user-side energy microgrid data optimization platforms offer many advantages over traditional manual analysis-based approaches in processing massive amounts of energy consumption data, as well as energy consumption and carbon emissions data influenced by multiple factors. However, they also have several drawbacks, particularly when processing cross-platform or cross-event data. This often leads to significant subjectivity and uncertainty, which in turn affects the objectivity, stability, and reliability of data analysis.

[0003] Taking the "Leneng Factory" data platform with its own brand built by the applicant as an example, the Leneng Factory digital platform and application construction is an end-to-end integrated data platform for user-side energy carbon digital construction and digital operation. It comprehensively improves the user-side energy carbon digitalization under the full coverage architecture of source, grid, load and storage, and uses the core digitalization of energy carbon informationization and operation digital architecture as support to output end-to-end energy carbon operation digital empowerment efficiency.

[0004] At the application level, similar to existing user-side energy-carbon optimization data platforms whose data objectives include reducing carbon emissions, energy consumption, emissions, and energy costs, the Leneng Factory digital platform uses the scientific aggregation of user-side energy loads as a foundation for data analysis, such as energy-carbon optimization and emission reduction and efficiency improvement for user-side energy microgrids. This platform typically subjectively sets indicators based on technical communication with energy users. Although a multi-layered review process is implemented, this subjectivity can easily impact the effectiveness of the data solutions output by the data platform (e.g., stability, reliability, and consistency). This can easily lead to the same technical operator outputting vastly different data solutions for different energy users. In particular, factors such as the transfer or departure of data analysts can result in two sets of data solutions with inconsistent data for the same user, necessitating significant debugging costs to adjust and integrate these divergent solutions. The fundamental issue lies in the lack of objective, quantifiable, globally referenceable, and stable and consistent data indicator models in these user-side energy optimization data platforms. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a data model used as a basic reference or examination data indicator in the quantitative optimization of the user-side energy consumption structure, as an energy-carbon entropy quantitative data model under the user-side energy microgrid optimization platform.

[0006] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0007] The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure is suitable for various data-driven user-side energy consumption and carbon consumption intelligent and information-based data analysis and processing platforms to conduct comprehensive quantitative optimization of the user-side energy consumption structure. The data model is based on the embedded energy-carbon consumption data to construct an energy-carbon consumption quantifiable data entropy algorithm with a universal data format and data link architecture, which makes up for the lack of objectivity of the underlying data in the energy-carbon consumption data analysis and optimization in the user-side user microgrid data platform, and reduces the subjectivity and uncertainty of different data platforms when analyzing and processing the same energy-carbon consumption event or the same data platform when analyzing and processing different energy-carbon consumption events.

[0008] As a preferred technical solution of the present invention, the energy-carbon entropy quantification data model is composed of multiple groups of interrelated data processing dimensions to perform independent or cross-data process verification on energy-carbon consumption data, and is constructed into a systematic internal correlation data model.

[0009] As a preferred technical solution of the present invention, it includes a core model of energy-carbon consumption data entropy as the basis. The data space of this core model is specifically constructed as follows: based on the energy consumption and carbon consumption data on the user side in a given system (or a given data platform), an energy consumption vector and a carbon consumption vector are respectively constructed. The components of the two vectors respectively represent the energy consumption and carbon consumption of each node in the given system (the node can be an energy form, energy type, time node, operation node, space node or other links). Then, in general, the introduction and construction of energy-carbon data indicators, especially in the face of objective data quantification problems that reflect the internal structure of energy-carbon consumption, requires the introduction of advanced mathematical analysis tools. Therefore, The above energy consumption vector and carbon consumption vector (these two vectors come from user-side data in a given system or a given data platform, and are themselves data vectors associated with specific energy and carbon consumption events) are imported from their own data space into the geometric space of higher mathematics. Under such a data concept transformation, the properties of the two vectors in the mathematical geometric space are further considered, including the length of the vector in the geometric space, the angle between the vector and the zero-point data axis of each dimension in the geometric space, the projection and vector components of the vector in each dimension in the geometric space, the spatial direction of the vector, and the angle relationship between the two vectors, etc. The set of the above vector geometric space properties serves as the basic data space of the core model.

[0010] As a preferred technical solution of the present invention, in the data space of the core model, the orthogonal projection values ​​of the energy consumption vector and the carbon consumption vector in each dimension directly correspond to the specific nodes and the vector components above them used when the energy consumption vector and the carbon consumption vector were initially constructed.

[0011] As a preferred technical solution of the present invention, based on actual user-side energy-carbon consumption events and their data association relationships, basic and quantifiable energy-carbon consumption core data indicator sub-items are derived from the data space of the above-constructed core model, and the core model of the energy-carbon consumption data entropy is thus constructed.

[0012] As a preferred technical solution of the present invention, specifically, the core model of the energy-carbon consumption data entropy includes at least the following data sub-items:

[0013] The first sub-item, in the data space of the core model above, considers the lengths of the energy consumption vector and the carbon consumption vector in the mathematical geometric space. Their respective lengths can generally represent the corresponding energy consumption quantitative data or carbon consumption quantitative data behind their own vectors, which constitutes an important and underlying quantifiable data entropy data guidance direction;

[0014] The second sub-item, based on the data considerations of the first sub-item above, further considers the ratio of the length of the carbon consumption vector to the length of the energy consumption vector in the geometric data space of the core model, forming a coefficient indicator. The value of this indicator corresponds to the amount of carbon consumption per unit energy consumption. Therefore, this coefficient indicator constitutes a quantitative entropy value data that can effectively characterize the energy consumption structure;

[0015] The third sub-item considers the general idea of ​​carbon emission reduction. The most basic method is to reduce energy consumption globally. However, in contrast, the optimization of energy-carbon consumption structure usually corresponds to achieving emission reduction effects equivalent to global energy consumption through local energy consumption reduction. Based on this, and reflecting it on the energy vector and carbon vector constructed above, the idea of ​​global energy consumption reduction in traditional methods actually assumes that the components of the energy consumption vector and the components of the carbon consumption vector have a consistent correspondence in terms of data principle. In other words, the energy consumption vector and the carbon consumption vector have a consistent correspondence in each dimension in the geometric space. The projections on them have a consistent correspondence respectively. On the contrary, the essence of the optimization of the energy-carbon consumption structure is to assume that the components of the two vectors are not globally consistent, and based on this inconsistency, find the component projection with a higher carbon consumption rate, thereby optimizing the energy-carbon consumption structure; based on this, a third sub-item can be constructed to characterize the potential for energy-carbon consumption optimization, which corresponds to an indicator of the directional consistency of the energy vector and the carbon vector in the data space of the core model. Naturally, the level of this indicator represents the optimization potential of the energy-carbon consumption structure. The greater the deviation between the two vectors, the greater the optimization space of the energy-carbon consumption structure.

[0016] As a preferred technical solution of the present invention, the core model of the energy-carbon consumption data entropy independently and optionally includes one or more items of the three groups of data sub-items.

[0017] As a preferred technical solution of the present invention, one or more groups of different extended data sub-items are subsequently introduced on the basis of the core model of the energy-carbon consumption data entropy to construct one or more different energy-carbon consumption data entropy extended models.

[0018] As a preferred technical solution of the present invention, the extended data sub-items include but are not limited to: constructing an intrinsic correlation structure data sub-item of energy-carbon consumption based on the inner product numerical index of the energy consumption vector and the carbon consumption vector in the geometric data space, and this data sub-item corresponds to the conjugate linkage of energy-carbon consumption and its projected components; constructing an intrinsic causal structure data sub-item of energy-carbon consumption based on the extended tensor product and / or direct product of the energy consumption vector and the carbon consumption vector.

[0019] As an optimal technical solution of the present invention, the data processing dimension includes at least the energy consumption data dimension and the carbon consumption data dimension on the user side at the bottom layer; the energy consumption data dimension is divided into two ladder dimensions, namely: a sub-ladder dimension with a multi-part energy price model and a sub-ladder dimension with only a single energy price model, wherein the sub-ladder dimension with a multi-part energy price model includes but is not limited to the electric energy consumption data dimension; the carbon consumption data dimension adopts a direct carbon emission data model or a carbon emission equivalent data model, wherein the carbon emission equivalent data model includes but is not limited to equivalent tree planting equivalent or equivalent coal equivalent or equivalent electricity equivalent or other equivalent equivalent; all data models under all the above dimensions have a data structure that can be quantified, and the energy consumption and carbon consumption data can be directly represented using scalar numerical data, which is also a basis for constructing a quantifiable data entropy model for energy-carbon consumption.

[0020] The beneficial effects of adopting the above technical solution are:

[0021] This paper addresses data objectivity issues in existing product applications and has developed a new energy-carbon entropy quantification data model within a user-side energy microgrid data optimization structure. By introducing multiple quantifiable data sub-items and their associated data entropy algorithms, this model reduces subjectivity and uncertainty during data analysis, improving the objectivity and accuracy of data processing. It is applicable not only to the applicant's data platform but also to a variety of user-side energy analysis and optimization data platforms.

[0022] The data model constructed by the present invention can effectively process and optimize the energy consumption and carbon emission data on the user side by constructing a systematic intrinsic correlation data model. In terms of data processing ideas, this application maps the various components of the energy consumption vector and the carbon consumption vector into a mathematical geometric space, and then quantifies and optimizes the energy-carbon consumption structure by analyzing the geometric properties of these vectors (such as length, angle, projection, etc.), and performs specific characterization and construction of the data sub-items of the model based on mathematical geometric space analysis tools on such a data processing route, thereby enabling this model to more deeply explore the intrinsic structure and correlation of energy consumption and carbon emission data, providing a more scientific and accurate method for data optimization; at the same time, the systematic intrinsic correlation data model in the model enables comprehensive consideration of multiple dimensions and levels of energy consumption and carbon emissions, and realizes the comprehensiveness and systematicity of data processing.

[0023] The specific beneficial effects of the present invention are detailed in the following embodiments. DETAILED DESCRIPTION

[0024] The following examples illustrate the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application. The term "and / or" used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0025] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when..." or "upon..." or "in response to determining..." or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted as meaning "upon determination," "in response to determining," or "upon detection of [the described condition or event]," or "in response to detecting [the described condition or event]," depending on the context. In addition, in the description of the specification of this application and the appended claims, the terms "first," "second," "third," etc. are used merely to distinguish descriptions and are not to be understood as indicating or implying relative importance.

[0027] Example 1, Technical Overview

[0028] The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure constructed by the present invention is applicable to various data-driven user-side energy consumption and carbon consumption intelligent and information-based data analysis and processing platforms, and performs comprehensive quantitative optimization of the user-side energy consumption structure. Taking the "Leneng Factory" data platform with its own brand constructed by the applicant as an example, the Leneng Factory digital platform and application construction is an end-to-end integrated data platform for user-side energy-carbon digital construction and digital operation. Under the full coverage architecture of source, grid, load and storage, the user-side energy-carbon digitalization is comprehensively improved, and the core digitalization of energy-carbon information and operation digital architecture is used as support to output end-to-end energy-carbon operation digital empowerment efficiency.

[0029] Similar to existing user-side energy-carbon optimization data platforms whose data objectives include reducing carbon emissions, energy consumption, emissions, and energy costs, the Leneng Factory digital platform uses the scientific aggregation of user-side energy loads as a foundation for data analysis, such as energy-carbon optimization and emission reduction and efficiency improvement for user-side energy microgrids. This platform typically subjectively sets indicators based on technical communication with energy users. Although a multi-layered review process is implemented, this subjective factor can easily impact the effectiveness of the data solutions output by the data platform (e.g., stability, reliability, and consistency). This can easily lead to the same technical operator outputting vastly different data solutions for different energy users. In particular, factors such as the transfer or departure of data analysts can result in two sets of data solutions with inconsistent data for the same user, necessitating significant debugging costs to adjust and integrate these divergent data solutions. The fundamental problem lies in the lack of objective, quantifiable, globally referenceable, and stable and consistent data indicator models in these user-side energy optimization data platforms.

[0030] Example 2: Model Core

[0031] The energy-carbon consumption data entropy core model takes the basic, global and generally concerned energy-carbon consumption data items as the core data focus and data objects of model construction, and embeds an energy-carbon consumption quantifiable data entropy algorithm with a universal data format and data link architecture based on these energy-carbon consumption data items to make up for the lack of objectivity of the underlying data in the energy-carbon consumption data analysis and optimization in the user microgrid data platform facing the user side, and reduce the subjectivity and uncertainty of different data platforms when analyzing and processing the same energy-carbon consumption event or the same data platform when analyzing different energy-carbon consumption events.

[0032] Here, the basic data processing idea is to derive basic and quantifiable energy-carbon consumption core data indicator sub-items from the user-side energy-carbon consumption data space based on actual user-side energy-carbon consumption events and their data correlation relationships, and thereby construct the core model of the energy-carbon consumption data entropy.

[0033] The key is to derive core energy-carbon consumption indicators from the existing user-side energy-carbon consumption data space. After numerous attempts, the key data processing method lies in combining existing user-side energy-carbon consumption data with the geometric data space of advanced mathematics. This allows for the intuitive and rational introduction of mathematical analysis tools to construct and process stable and reliable quantitative data indicators.

[0034] Under this data thinking and technical route, the corresponding specific data process is to construct energy consumption and carbon consumption into two data vectors respectively. Assume that the energy consumption vector E is Where ei represents the energy consumption of the i-th node in the system; the carbon consumption vector C is Where ci represents the carbon consumption of the i-th node in the system.

[0035] Considering the given user-side energy and carbon consumption data, the nodes used to delineate the various components e1, e2, …, en or c1, c2, …, cn in the aforementioned energy and carbon consumption vectors can be different energy types, time nodes, operation nodes, spatial nodes, or other links, depending on the actual user-side energy and carbon consumption data. In fact, the energy and carbon consumption data handed over from user units is often highly non-standardized. Therefore, the construction of energy and carbon consumption vectors, or the dimensionality of the data space in which they reside, exhibits significant variability. While common data organization procedures can be used to assist in optimization, manual guidance and verification must still be the key foundation and fundamental data constraint.

[0036] Based on the data of common user-side energy and carbon consumption in actual operations, the energy consumption vector and carbon consumption vector division nodes and their division components mostly present common forms. For example, in the energy consumption vector, the consumption of different types of energy (such as electricity, coal, oil, natural gas, etc.); find the energy consumption data of different equipment or systems such as daily electricity consumption, charging piles, industrial loads, etc. from the user-side energy and carbon consumption data document; the consumption corresponding to the production capacity of new energy such as photovoltaic power generation, wind power generation and coal power consumption is divided into different components, etc.

[0037] Depending on the specific data analysis needs, the energy consumption vector and the carbon consumption vector can be aggregated data vectors. For example, one of the data analysis functions at our Leneng Factory is to quantify the company's electricity and fuel consumption, including energy consumption, carbon consumption, energy efficiency, energy price, and stability. In this case, the energy consumption vector can be aggregated into two components in a two-dimensional data space: e1, electricity consumption, which corresponds to the numerical value of electricity consumption, generally purchased from the traditional power grid; and e2, fuel consumption, which involves the use of gas or other combustible fuels and is summarized in the energy consumption vector. The components of the carbon emissions vector are then constructed in correspondence with the two components of the energy consumption vector.

[0038] Another common component division form of the energy consumption vector and the carbon consumption vector is: constructing the components of the vector based on the user-side energy and carbon consumption operation links, where the number of operation links corresponds to the dimension of the data space.

[0039] For example, the components of the energy consumption vector can correspond to: material procurement energy consumption, transportation energy consumption, production energy consumption (such as energy consumption of workshop production line equipment, monitoring equipment, and computer equipment), production support energy consumption (such as office building energy consumption, heating energy consumption, lighting equipment operation energy consumption, and other building energy consumption), equipment quality inspection energy consumption, and waste disposal energy consumption. Correspondingly, in general, the carbon emission vector is constructed in correspondence with the energy consumption vector. For example, supply chain carbon emissions can include carbon emissions from supply procurement. Production operations may involve the purchase of a large amount of materials, including raw materials, equipment, and parts, and the carbon emissions generated during the production and transportation of these purchases; transportation carbon emissions correspond to carbon emissions generated during product transportation, such as fuel consumption of transportation vehicles; manufacturing carbon emissions correspond to carbon emissions generated during the manufacturing process by equipment used; and waste disposal carbon emissions generally correspond to the waste generated during the production process. The treatment of this waste may involve processes such as incineration and landfilling. The carbon emissions corresponding to these processes serve as the numerical components of the carbon emission vector at the corresponding link.

[0040] In addition, based on policy requirements or user-side demands, some uncommon component forms sometimes appear in the energy consumption vector, such as the energy savings component. This data component also has various forms of data representation, such as whether to introduce a negative sign, using equivalent electricity or whether to use the equivalent representation of the amount of standard coal saved through energy-saving measures; for example, the number of sites serving user power assets is sometimes introduced into the energy consumption vector as an indirect energy consumption component based on user-side rules and requirements; and so on.

[0041] After the energy consumption vector and carbon consumption vector are constructed, the introduction and construction of energy-carbon data indicators are considered based on the technical route of data geometricization and mathematical analysis tools, and the construction of data processing algorithms. In particular, when facing the objective data quantification problem of the internal structure of energy-carbon consumption, it is necessary to introduce advanced mathematical analysis tools. Therefore, the energy consumption vector and carbon consumption vector (these two vectors come from user-side data in a given system or a given data platform and are themselves data vectors associated with specific energy and carbon consumption events) are imported from their own data space into the geometric space of advanced mathematics. Under this data thinking transformation, the properties of the two vectors in the mathematical geometric space are further considered, including the length of the vector in the geometric space, the angle between the vector and the zero-point data axis of each dimension in the geometric space, the projection and vector components of the vector in each dimension in the geometric space, the spatial orientation of the vector, and the angular relationship between the two vectors. The set of these vector geometric space properties serves as the basic data space of the core model. It can be seen that in such a data space, the orthogonal projection values ​​of the energy consumption vector and the carbon consumption vector in each dimension directly correspond to the specific nodes and the vector components above them used when the energy consumption vector and the carbon consumption vector were initially constructed.

[0042] At this point, the specific construction of the data processing algorithm is quite clear. Based on actual user-side energy and carbon consumption events and their data relationships, the data space of the core model constructed above is used to derive fundamental and quantifiable core energy-carbon consumption data indicator sub-items. These sub-items can then be used to construct a model of energy-carbon consumption data entropy. This technical approach allows the construction of a fundamental, globally quantifiable, and referenceable data model, serving as the core model of energy-carbon consumption data entropy. Specifically, this model may include the following data items.

[0043] The first data item is named: CONSUMPINDEX (Index Of Energy-Carbon Consumption), which contains two independent sub-data items, namely CONSUMPINDEX-e and CONSUMPINDEX-c, pointing to energy and carbon respectively; and the mathematical algorithm representations of CONSUMPINDEX-e and CONSUMPINDEX-c are respectively: and

[0044]

[0045] The data connotation of CONSUMPINDEX-e and CONSUMPINDEX-c is: considering the Euclidean norm length of the energy consumption vector and the carbon consumption vector in the mathematical geometric space, their respective Euclidean norm lengths can generally represent the energy consumption quantitative data or carbon consumption quantitative data corresponding to their own vectors. Therefore, CONSUMPINDEX-e and CONSUMPINDEX-c constitute a set of important and underlying quantifiable data entropy data guidance directions.

[0046] The second data item is named PROPORTINDEX-ce (Proportional Index Of Energy-Carbon Consumption). Its data is essentially the ratio of the length of the carbon consumption vector to the length of the energy consumption vector in the geometric data space of the core model, which constitutes a coefficient index. The value of this index corresponds to the amount of carbon consumption per unit energy consumption. Therefore, this coefficient index constitutes a quantitative entropy value data that can effectively characterize the energy consumption structure. Correspondingly, the mathematical algorithm of PROPORTINDEX-ce is represented as:

[0047] As described above, the data essence or data connotation of PROPORTINDEX-ce is a quantitative proportional entropy value, constructed based on the dot product of the carbon consumption vector C and the energy consumption vector E. This quantitative proportional entropy value can be used to measure carbon emissions per unit of energy consumption and can serve as a key efficiency indicator for many relevant energy-carbon analysis data platforms. Generally speaking, higher quantitative proportional entropy values ​​indicate greater carbon emissions for the same energy consumption, reflecting lower carbon emission efficiency of energy use; conversely, lower values ​​indicate that energy use is more effective in producing less carbon emissions. Therefore, the quantitative proportional entropy value we constructed, including its algorithmic process and data measurement model, has excellent quantitative representation properties for evaluating and improving energy consumption structures to reduce carbon footprints.

[0048] The third data item is named: POTENTINDEX-ce (Index of Optimization Potential of Energy-Carbon Consumption Structure); its data idea is: considering the general idea of ​​carbon emission reduction, the most basic method is to carry out global reduction of energy consumption. However, in contrast, the optimization of energy-carbon consumption structure usually corresponds to achieving an emission reduction effect equivalent to global energy consumption through local energy consumption reduction; based on this, and reflecting it on the energy vector and carbon vector constructed above, for the idea of ​​global reduction of energy consumption in traditional methods, in terms of data principle, it is actually assumed that the various components of the energy consumption vector and the various components of the carbon consumption vector have a consistent correspondence, or in other words, the energy consumption vector and the carbon consumption vector are geometrically related. The projections on each dimension in the space have a consistent correspondence. Conversely, the essence of optimizing the energy-carbon consumption structure is to assume that the components of the two vectors are not globally consistent, and based on this inconsistency, find the component projection with a higher carbon consumption rate, thereby optimizing the energy-carbon consumption structure. Based on this, a third sub-item can be constructed to characterize the potential for energy-carbon consumption optimization. In the data space of the core model, it corresponds to an indicator of the directional consistency of the energy vector and the carbon vector. Naturally, the level of this indicator represents the optimization potential of the energy-carbon consumption structure. The greater the deviation between the two vectors, the larger the optimization space for the energy-carbon consumption structure. Based on the above data ideas, there are several different possible mathematical representations of PROPORTINDEX-ce, including a representation based on the angular offset between the energy vector and the carbon vector in the geometric data space, named PROPORTINDEX-ce-al; a representation based on the ratio of the inner product and outer product of the vectors, named PROPORTINDEX-ce-xx; and other possible mathematical algorithms.

[0049] In the first representation form, the mathematical algorithm of PROPORTINDEX-ce-al is represented as: in, From a data algorithm perspective, the PROPORTINDEX-ce-al data sub-item considers the directional consistency of the energy and carbon consumption vectors in geometric space to optimize the energy and carbon consumption structure. This directional consistency is quantified by calculating the angle between the energy and carbon consumption vectors. Using cosine similarity, PROPORTINDEX-ce-al corresponds to the ratio of E·C to ∥E∥∥C∥. Numerically, directional consistency ranges from -1 to 1, with values ​​closer to 1 indicating that the two vectors are closer to the same direction, and smaller values ​​indicating less consistent directions. Furthermore, directional inconsistency indicates potential for optimizing the energy and carbon consumption structure. This optimization can be achieved by adjusting the energy and carbon consumption of certain nodes. This allows for the quantification and evaluation of the energy consumption and carbon emission structure and the identification of optimization potential.

[0050] In the second representation form, the mathematical algorithm code of PROPORTINDEX-ce-xx is: The same, E×C is the vector product of two vectors. In the PROPORTINDEX-ce-xx algorithm, the upper and lower fractions respectively agree or deviate from the consistency of energy-carbon consumption. Their ratio amplifies the optimization potential of the user-side energy-carbon consumption and emission structure to a certain extent. This data amplification effect should provide advantages in specific, detailed energy-carbon structure analysis and optimization.

[0051] It's worth noting that in the PROPORTINDEX-ce-xx algorithm, since the outer product is mathematically defined only for three-dimensional vectors, if the energy consumption vector and the carbon consumption vector are high-dimensional vectors (as mentioned above, the component partitioning nodes of the energy consumption vector and the carbon consumption vector generally exceed three, so both are usually vectors in a high-dimensional data space), then their outer product can be considered the set of all vectorized pairwise element products, whose modulus can be obtained by calculating the square root of the sum of the squares of all these products; this is mathematically equivalent to the modulus or Euclidean norm of the outer product vector in three-dimensional space. Thus, the size of the PROPORTINDEX-ce-xx ratio reflects the correspondence between energy consumption and carbon emissions, as well as the potential for optimizing the energy-carbon consumption structure; a larger ratio means a greater inconsistency between energy consumption and carbon emissions in a specific direction, and a greater potential for optimizing energy-carbon consumption; thus, the size of the possible optimization space is quantitatively characterized.

[0052] Example 3: Model expansion

[0053] The core model mentioned above can be expanded in a series of ways. On the one hand, other equivalent algorithms can be introduced to represent the three data items equivalently (just like PROPORTINDEX-ce-al and PROPORTINDEX-ce-xx in the third data item. Although they represent the same data content and are both objective quantitative data algorithms, they can show different details of the energy-carbon vector relationship and its user-side energy-carbon consumption and optimization in certain directions).

[0054] On the other hand, the model can be naturally expanded by introducing more quantities. The construction concept of these new and expanded quantities is the same as that of the core model mentioned above, that is, based on the actual user-side energy and carbon consumption events and their data association relationships, basic and quantifiable energy-carbon consumption core data indicator sub-items are derived from the energy-carbon vector data space, and new data items are constructed from them to expand the model. For example, based on the numerical indicator of the inner product of the energy consumption vector and the carbon consumption vector in the geometric data space, the intrinsic correlation structure data sub-item of energy and carbon consumption is constructed. This data sub-item corresponds to the conjugate chain of energy and carbon consumption and its projected components; based on the extended tensor product and / or direct product of the energy consumption vector and the carbon consumption vector, the intrinsic causal structure data sub-item of energy and carbon consumption is constructed.

[0055] In addition, the model can be expanded through the secondary intersection of the underlying data items. For example, consider an indicator of the total amount of energy and carbon consumption, without considering rationality for the time being, and let it be represented by the product of the lengths of the energy consumption vector and the carbon consumption vector; also without considering rationality for the time being, on this basis, we can construct a normalization index of energy consumption (for example, by representing the amount of energy consumption under the same carbon emissions), and then we can stack and construct it based on the constructed data indicators, such as setting the normalization index to be:

[0056] The size of this indicator represents the amount of energy consumed per unit of carbon emission, and further characterizes the efficiency of energy utilization. The specific development and construction of these extended models are also important follow-up work after the research and development of the present invention.

[0057] In the above embodiments, the descriptions of each embodiment have different emphases. For portions not described or described in detail in a particular embodiment, reference should be made to the relevant descriptions of other embodiments. In each embodiment, the hardware implementation of the technology can directly utilize existing intelligent devices, including but not limited to industrial computers, personal computers, smartphones, handheld standalone computers, and floor-standing standalone computers. The input device preferably utilizes an on-screen keyboard, the data storage and calculation modules utilize existing memories, calculators, and controllers, the internal communication modules utilize existing communication ports and protocols, and remote communication utilizes existing GPRS networks, the World Wide Web, and the like. Those skilled in the art will clearly understand that, for ease of description and brevity, the division of the above functional units and modules is used only as an example. In actual applications, the above functions can be distributed among different functional units and modules as needed, i.e., the internal structure of the device can be divided into different functional units or modules to perform all or part of the functions described above. The functional units and modules in the embodiments can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. These integrated units can be implemented in either hardware or software functional units. In addition, the specific names of the functional units and modules are merely for the purpose of distinguishing them from one another and are not intended to limit the scope of protection of this application. The specific operating processes of the units and modules in the above-mentioned system can be referenced to the corresponding processes in the aforementioned method embodiments and will not be described in detail here. In the embodiments provided herein, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or omitting or not implementing certain features. The functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically as a separate unit, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in either hardware or software functional units. If the integrated modules / units are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium.

[0058] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure is applicable to various data-driven user-side energy optimization information platforms, and is characterized by: The data model is based on the embedded energy and carbon consumption data to build an energy and carbon consumption quantification data entropy algorithm with a universal data format and data link architecture; Furthermore, the data model is constructed as a systematic internal correlation data model by performing independent or cross-data process verification on energy and carbon consumption data through multiple sets of interrelated data processing dimensions; Specifically, the data link architecture of the data model and its independent or cross-data processes are optionally based on the following data sub-items: ① energy consumption vector and carbon consumption vector; ② CONSUMPINDEX-e and CONSUMPINDEX-c, which are to construct the Euclidean norm of the energy consumption vector and the carbon consumption vector into quantitative data of energy and carbon consumption; ③ PROPORTINDEX-ce, the data is essentially constructed based on the self-dot product of the carbon consumption vector and the energy consumption vector and their quantized proportional entropy value; ④ POTENTINDEX-ce; The data idea is to achieve an emission reduction effect equivalent to global energy consumption through local energy consumption reduction, and based on this inconsistent characteristic, find the component projection with a higher carbon consumption rate, thereby optimizing the energy-carbon consumption structure.

2. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 1 is characterized by: POTENTINDEX-ce optionally contains basic indicators and amplified indicators; The basic indicator is PROPORTINDEX-ce-al, which is based on the angular offset between the energy vector and the carbon vector in the geometric data space. The basic indicator is used to quantify the energy-carbon entropy in a general scenario. The magnification index is PROPORTINDEX-ce-xx; It is characterized based on the ratio of the inner product and outer product of the energy vector and the carbon vector; the amplification index is used to quantify the energy-carbon entropy in a fine-grained processing scenario.

3. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 2 is characterized by: The optimization potential of the basic indicator PROPORTINDEX-ce-al for the energy and carbon consumption structure is based on the directional consistency of the energy consumption vector and the carbon consumption vector in the geometric space; the directional consistency is quantified by calculating the angle between the energy consumption vector and the carbon consumption vector. At the same time, the directional inconsistency indicates the potential for optimizing the energy-carbon consumption structure. The energy-carbon consumption structure is optimized by adjusting the energy-carbon consumption of certain nodes, thereby quantifying the structure of energy consumption and carbon emissions and identifying the optimization potential.

4. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 2 is characterized by: The amplification indicator PROPORTINDEX-ce-xx is characterized based on the ratio of the inner product and outer product of the energy vector and the carbon vector. The upper and lower parts of the ratio respectively fit or deviate from the consistency of energy and carbon consumption. The ratio amplifies the optimization potential of the energy-carbon consumption and emission structure on the user side. This data amplification effect has advantages in fine energy-carbon structure analysis and optimization.

5. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 1 is characterized by: It includes a core model of energy and carbon consumption data entropy as the basis. The data space of this core model is specifically constructed as follows: based on the energy consumption and carbon consumption data on the user side in a given system, an energy consumption vector and a carbon consumption vector are respectively constructed. The components of the two vectors represent the energy consumption and carbon consumption of each node in the given system. The above energy consumption vector and carbon consumption vector are imported from their own data space into the geometric space of higher mathematics. The properties of the two vectors in the mathematical geometric space are further considered, including the length of the vector in the geometric space, the angle between the vector and the zero-point data axis of each dimension in the geometric space, the projection of the vector in each dimension in the geometric space and the vector components, the spatial direction of the vector and the angle relationship between the two vectors. The set of the above vector geometric space properties serves as the basic data space of the core model.

6. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 5 is characterized by: In the data space of the core model, the orthogonal projection values ​​of the energy consumption vector and the carbon consumption vector in each dimension directly correspond to the specific nodes and the vector components above them used when the energy consumption vector and the carbon consumption vector were initially constructed.

7. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 1 is characterized by: The data model is compatible with extended data sub-items, including but not limited to: constructing an intrinsic correlation structure data sub-item of energy and carbon consumption based on the inner product numerical index of the energy consumption vector and the carbon consumption vector in the geometric data space, and this data sub-item corresponds to the conjugate linkage of energy and carbon consumption and its projected components; The intrinsic causal structure data sub-item of energy and carbon consumption is constructed based on the extended tensor product and / or direct product of the energy consumption vector and the carbon consumption vector.

8. The energy-carbon entropy quantification data model under the user-side energy microgrid data optimization structure according to claim 1 is characterized by: The data processing dimension at the bottom layer includes at least the energy consumption data dimension and the carbon consumption data dimension on the user side; the energy consumption data dimension is divided into two ladder dimensions, namely: a sub-ladder dimension with a multi-part energy price model and a sub-ladder dimension with only a single energy price model, wherein the sub-ladder dimension with a multi-part energy price model includes but is not limited to the electric energy consumption data dimension; the carbon consumption data dimension adopts a direct carbon emission data model or a carbon emission equivalent data model, wherein the carbon emission equivalent data model includes but is not limited to equivalent tree planting equivalent or equivalent coal equivalent or equivalent electricity equivalent or other equivalent equivalent; the data under the above-mentioned user-side energy consumption data dimension and carbon consumption data dimension are all quantitatively represented, and the energy consumption and carbon consumption data are directly represented using scalar numerical data.

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