Standardized energy efficiency service model construction method, system, terminal and storage medium

Abstract

The application discloses a standardized energy efficiency service model construction method and system, a terminal and a storage medium, and establishes energy use business data index and collects business data of different energy efficiency business participants based on space-time accessibility; an energy efficiency characteristic data engine is established, and energy efficiency characteristic data is obtained by using the energy efficiency characteristic data engine; an energy efficiency characteristic data division model, an energy efficiency business division model, an energy efficiency service conversion model and an energy efficiency service evaluation model are constructed and used, and an energy efficiency service model is constructed based on a multi-source energy efficiency database, a basic energy efficiency service model library and a high-level energy efficiency service model library by using the energy efficiency service conversion model and the energy efficiency service evaluation model. The application is used for quickly and efficiently carrying out energy efficiency service demand analysis, energy use business data summarization, energy efficiency service data extraction, energy efficiency business content clarification, energy efficiency service business process standardization, energy efficiency service evaluation, energy efficiency service participant service and energy efficiency service quality improvement, and promotes the establishment of a standardized energy efficiency service market system.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency service technology, and more specifically, to a method, system, terminal, and storage medium for constructing standardized energy efficiency service models. Background Technology

[0002] With the application of new technologies in the energy sector, the energy efficiency service market, driven by the demand for energy conservation and consumption reduction, is gradually developing. End-user energy customers are increasingly emphasizing energy efficiency management and have higher and higher requirements for energy efficiency services. Currently, the domestic energy efficiency service industry is in its developmental stage. Numerous energy efficiency service companies are operating independently, resulting in a lack of feasible standardized processes for related energy efficiency businesses. This leads to fragmented operations, low service quality, and a lack of in-depth research into energy efficiency service business models. Even existing platforms that integrate energy efficiency service functions have relatively simple business models, lack universality, and a complete functional architecture. Business standards have not yet been established, necessitating the construction of a standardized energy efficiency service system to improve the quality of energy efficiency services.

[0003] The existing technology (CN114119276A), "A method and system for constructing a business model for energy efficiency services in typical scenarios," analyzes energy efficiency service issues in various typical scenarios, sorts out the energy forms involved in energy efficiency services in various typical scenarios, divides the energy efficiency service business into functions and establishes a business architecture. Based on the business function modules and business architecture of energy efficiency services, a business model for energy efficiency services in typical scenarios is constructed using a unified modeling language. Then, the energy efficiency level is analyzed by combining the index weight analysis method. The existing technology can comprehensively, clearly and intuitively analyze the business process. However, existing technologies lack centralized management of entities participating in energy efficiency services, especially those that are both suppliers and consumers. Management relies solely on the aggregated energy forms used in these services, leading to fragmented energy efficiency operations and a decline in customer experience. Furthermore, the lack of an energy efficiency data engine to locate entity business data when entities submit energy efficiency data requests further degrades service quality. This invention establishes a mechanism for extracting energy consumption data on demand using a business data index, transforming it into energy efficiency feature data to provide energy efficiency services, thus preventing user data leakage. The invention also categorizes public energy efficiency services, reducing customer energy efficiency management costs and promoting the widespread adoption of basic energy efficiency services. Additionally, the invention utilizes a series of big data machine learning algorithms during energy efficiency feature data extraction, improving feature extraction accuracy. More importantly, existing energy efficiency service research does not consider the geographical location and grid location of participating entities. Entities located at different voltage levels and power line terminals experience varying response times to energy efficiency services, and ignoring grid location can lead to power transmission losses. In addition, the main business data, including the master table metering data, sub-table metering data, user energy consumption strategy data, user energy consumption bills, user production plans, and user product energy consumption, are large in volume and have unstable data quality. They are core production and operation data within the enterprise and cannot be directly used as energy efficiency data to support public energy efficiency services. They need to be feature extracted based on the content of energy efficiency service business to become energy efficiency data usable for energy efficiency services. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a standardized energy efficiency service model construction method, system, terminal, and storage medium for rapidly and efficiently conducting energy efficiency service demand analysis, summarizing energy consumption business data, extracting energy efficiency service data, clarifying energy efficiency business content, standardizing energy efficiency service business processes, providing energy efficiency service evaluation, serving energy efficiency service participants, improving energy efficiency service quality, and promoting the establishment of a standardized energy efficiency service market system.

[0005] This invention proposes a method for constructing a standardized energy efficiency service model, comprising: Step 1: Obtain energy efficiency characteristic data according to the energy efficiency business data requirements, and establish a multi-source energy efficiency database to store the energy efficiency characteristic data; Step 2: Classify the energy efficiency characteristic data in the multi-source energy efficiency database, and establish an energy efficiency service model based on the classification results of the energy efficiency characteristic data; wherein, the energy efficiency service model includes: basic energy efficiency service and advanced energy efficiency service; establish a basic energy efficiency service model library to store basic energy efficiency services, and establish an advanced energy efficiency service model library to store advanced energy efficiency services; Step 3: Based on the energy efficiency business service data of the energy efficiency service model, construct an energy efficiency service evaluation model using the energy efficiency business evaluation algorithm, and use the energy efficiency service evaluation model to evaluate basic energy efficiency services and advanced energy efficiency services. Step 4: Based on the energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library, a standardized energy efficiency service system is constructed.

[0006] Step 1 includes: Step 1.1: Based on the energy efficiency business data requirements, establish an energy consumption business data index based on spatiotemporal accessibility, and use the energy consumption business data index to collect business data from different energy efficiency business participants; Step 1.2: Establish an energy efficiency feature data engine. Use the energy efficiency feature data engine to standardize business data and filter it according to industry energy efficiency standard thresholds. After filtering, extract and select energy efficiency features to obtain energy efficiency feature data. The energy efficiency feature data is stored in a multi-source energy efficiency database. Step 1.3: When the energy efficiency characteristic data obtained in Step 1.2 cannot meet the energy efficiency business data requirements published by the energy efficiency business participants, the business data is located in reverse using the energy efficiency characteristic data engine and the energy consumption business data index. Then, the energy efficiency business participants are located again based on the business data, and Steps 1.1 and 1.2 are repeated to obtain the required energy efficiency characteristic data.

[0007] Step 2 includes: Step 2.1: Construct an energy efficiency characteristic data partitioning model based on the data ownership and public availability of the energy efficiency characteristic data from Step 1; Step 2.2: Use the energy efficiency characteristic data partitioning model to partition the energy efficiency characteristic data. Energy efficiency characteristic data with non-private ownership and public ownership is partitioned into public energy efficiency data, and energy efficiency characteristic data with private ownership and non-public ownership is partitioned into private energy efficiency data. Step 2.3: Using the energy efficiency business segmentation model, energy efficiency businesses whose public energy efficiency data can meet the energy efficiency business data requirements are classified as basic energy efficiency services, and energy efficiency businesses whose private energy efficiency data can meet the energy efficiency business data requirements are classified as advanced energy efficiency services. Among them, basic energy efficiency services are stored in the basic energy efficiency service model library, and advanced energy efficiency services are stored in the advanced energy efficiency service model library.

[0008] The construction method includes steps 3 and 4, including: Based on the classification conditions of the energy efficiency service model in step 3, construct the trigger conditions for energy efficiency service conversion, build the energy efficiency service conversion model based on the trigger conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

[0009] The standardized energy efficiency service system also includes an energy efficiency service conversion model.

[0010] Step 1.1 includes: Step 1.1.1: Taking the energy efficiency business data demand as the endpoint, and the list of available business data provided by the energy efficiency business participants to form the starting point set; under the spatiotemporal constraints formed by the endpoint and the starting point set, calculate the spatiotemporal reachability from each starting point to the endpoint. Step 1.1.2: Construct an energy consumption business data index using the path between the starting point and the ending point corresponding to the minimum value among multiple spatiotemporal reachability values.

[0011] Step 1.1.1 includes: Step 1.1.1.1, End Point The starting point is a collection of energy efficiency business data requests issued by energy efficiency business participants. satisfy ,in, and For any two distinct starting points in the starting point set, The number of starting points; each starting point represents a list of proprietary business data that can be supplied. Step 1.1.1.2, using the endpoint The geographical location and power grid location, together with the geographical location and power grid location of each starting point, constitute the spatiotemporal constraints STP. The location of the power grid includes: the voltage level at which the end or starting point of the grid is connected, and the transmission line at which the end or starting point of the grid is connected. Step 1.1.1.3, under the spatiotemporal constraints STP The spatiotemporal reachability from each starting point to the ending point is calculated using the following formula:

[0012] In the formula, Starting point To the finish line Spatiotemporal reachability This is the power grid location correction factor. Starting point Geographical location to destination The time of the geographical location.

[0013] Step 1.2 includes: Step 1.2.1: Based on rule engine technology, establish an energy efficiency feature data engine using data processing rules; and use the energy efficiency feature data engine to decompose, clean, and integrate the collected business data to obtain energy efficiency data. Step 1.2.2 involves standardizing the energy efficiency data and filtering it according to energy efficiency standards and importance indices to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics. Step 1.2.3: Using energy efficiency evaluation indicators as decision points, establish an XGBoost decision tree based on the correlation between standard energy efficiency characteristics and energy efficiency evaluation indicators. Perform feature determination on production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics to obtain energy efficiency characteristic data. Step 1.2.4: Store the energy efficiency characteristic data in the multi-source energy efficiency database.

[0014] Step 1.2.2 includes: Step 1.2.2.1: The original values ​​of the numerical features in the energy efficiency data are linearly transformed using the min-max normalization method. The resulting linearized feature values ​​satisfy the following relationship:

[0015] In the formula, These are the raw values ​​for the numerical type characteristics in the energy efficiency data. The numerical values ​​of the linearized features. It is the minimum value among the original numerical values ​​of the numerical type feature. The maximum value among the original numerical values ​​of the numerical type feature; Step 1.2.2.2: The z-score normalization method is used to normalize the values ​​of the linearized features. The resulting linearly normalized features satisfy the following relationship:

[0016] In the formula, The numerical values ​​of the linearly normalized features. The average value of the linearized feature. The standard deviation of the linearized feature values; Step 1.2.2.3: Perform decimal scaling normalization on the numerical values ​​of the linear normalized features. The resulting normalized feature values ​​satisfy the following relationship:

[0017] In the formula, For the numerical values ​​of the normalized features, It makes The smallest integer.

[0018] Step 1.2.2 also includes: Step 1.2.2.4: Use separability as a criterion to determine the inter-class separability of standard energy efficiency characteristics, whereby standard energy efficiency characteristics participate in relevant classes of energy efficiency business. Unrelated categories that participate in energy efficiency business with standard energy efficiency characteristics log-likelihood ratio The following relationship must be satisfied:

[0019] In the formula, As a standard energy efficiency characteristic, For standard energy efficiency characteristics related categories, This is a category unrelated to standard energy efficiency characteristics; Related classes For irrelevant classes Class separability The following relationship must be satisfied:

[0020] Step 1.2.2.5, using KL divergence KL divergence is used as an indicator of the importance of standard energy efficiency characteristics to energy efficiency operations. The following relationship must be satisfied:

[0021] In the formula, For related classes For irrelevant classes Separability between classes Unrelated class For related classes Separability between classes; Step 1.2.2.6: Filter according to the importance index of standard energy efficiency characteristics for energy efficiency business to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics.

[0022] Step 1.2.3 includes: Step 1.2.3.1: Using safety performance evaluation index, economic performance evaluation index, and sustainable production performance evaluation index as decision points; when splitting the decision point, if the value of the loss function decreases after splitting, then split the decision point. The loss function satisfies the following relationship:

[0023] In the formula, and These represent the left and right subtrees, respectively. For the weights of the child nodes in the decision tree, Represents the sum of the first derivatives of the left child node. Represents the sum of the first derivatives of the right child node. Represents the sum of the first derivatives of the left child node. This represents the sum of the first derivatives of the right child node; This is the loss correction value for the sum of the derivatives of the child nodes; Step 1.2.3.2: Construct the XGBoost decision tree by selecting local features and training data to determine features.

[0024] In the formula, This represents the prediction result of the k-th tree. This represents the cumulative loss of the prediction results. This represents the transformation of the prediction result of the k-th tree. Representing true characteristics, Indicates pre-stored features, This represents the total amount of data in the k-th tree. This indicates the number of trees created. This indicates the optimization result. Indicates the correction amount.

[0025] In step 2.2, the public energy efficiency data includes: total meter readings, energy price data, energy strategies, energy meteorological data, energy-saving technologies, and energy-saving strategies. Private energy efficiency data includes: sub-metering data, user energy consumption strategy data, user energy consumption bills, user production plans, and user product energy consumption.

[0026] In step 2.3, for basic energy efficiency services, the model is built, trained, and optimized according to the process of business analysis, data requirement analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to obtain the basic energy efficiency service model. The basic energy efficiency service model is stored in the basic energy efficiency service model library. The basic energy efficiency service model includes: energy meteorology model for industrial enterprises, electricity substitution model for pharmaceutical enterprises, and regional load forecasting model. For advanced energy efficiency services, the process involves business analysis, data requirements analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to build, train, and optimize models, thereby obtaining advanced energy efficiency service models. These models are stored in an advanced energy efficiency service model library. Among them, advanced energy efficiency service models include: enterprise product energy consumption improvement models, enterprise capacity demand energy efficiency optimization models, and enterprise energy bill optimization models.

[0027] In step 3, the evaluation objects of the energy efficiency service evaluation model include: energy efficiency service providers, energy efficiency service models, energy efficiency service businesses, and energy efficiency service users; each evaluation object contains evaluation tags. Energy efficiency service users submit user evaluations through the energy efficiency service evaluation model. The user evaluations include: service completeness, service effectiveness, service quality, service attitude, and service process. The user evaluations are used as evaluation tags and are respectively bound to each evaluation object.

[0028] Standardized energy efficiency service evaluation includes: energy efficiency business content analysis, energy efficiency business service data collection, service evaluation data feature extraction, energy efficiency business evaluation algorithm configuration, energy efficiency business evaluation rule configuration, and energy efficiency business evaluation model optimization; standardized energy efficiency service evaluation is used as an evaluation tag and is bound to the corresponding evaluation object.

[0029] In step 4, the participants in the energy efficiency business include: energy suppliers, government departments, meteorological service providers, energy customers, and energy efficiency service providers. The standard energy efficiency service business database includes a directory of energy efficiency service products and energy efficiency service providers; among them, energy efficiency service products include: enterprise product energy consumption improvement services, enterprise capacity demand energy efficiency optimization services, enterprise energy bill optimization services, and regional load forecasting services.

[0030] Triggering conditions include: changes in the source of business data, changes in the entities participating in energy efficiency service business, changes in the demand for energy efficiency service business, changes in the development of energy efficiency technology, changes in the content of energy efficiency service business, and changes in the rules of energy efficiency service business.

[0031] When changes in public energy efficiency data render it unable to meet the requirements of the basic energy efficiency service model, the basic energy efficiency service model is removed from the basic energy efficiency service model library and transformed into an advanced energy efficiency service model stored in the advanced energy efficiency service model library. These changes in public energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the public energy efficiency data. When changes in private energy efficiency data render it unable to meet the requirements of the Advanced Energy Efficiency Service Model (AESM), the AESM is removed from the AESM library and converted into a Basic Energy Efficiency Service Model (BESSM) and stored in the BESSM library. Changes in private energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the private energy efficiency data.

[0032] In another aspect, this invention proposes a standardized energy efficiency service model construction system and implements a standardized energy efficiency service model construction method.

[0033] The system includes: a multi-source energy efficiency database construction module, a basic energy efficiency service model library construction module, an energy efficiency service evaluation model construction module, a standard energy efficiency service business library construction module, and a standardized energy efficiency service system construction module; The multi-source energy efficiency database construction module is used to obtain energy efficiency characteristic data according to the energy efficiency business data requirements and establish a multi-source energy efficiency database to store the energy efficiency characteristic data. The basic energy efficiency service model library construction module is used to classify energy efficiency characteristic data in the multi-source energy efficiency database and establish energy efficiency service models based on the classification results. The energy efficiency service models include: basic energy efficiency services and advanced energy efficiency services. The basic energy efficiency service model library construction module includes a basic energy efficiency service model library unit and an advanced energy efficiency service model library unit. The basic energy efficiency service model library unit stores basic energy efficiency services, and the advanced energy efficiency service model library unit stores advanced energy efficiency services. The energy efficiency service evaluation model construction module is used to construct an energy efficiency service evaluation model based on the energy efficiency business service data of the energy efficiency service model and the energy efficiency business evaluation algorithm, and to evaluate the basic energy efficiency service and advanced energy efficiency service using the energy efficiency service evaluation model. The standardized energy efficiency service system construction module is used to construct a standardized energy efficiency service system based on energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library.

[0034] The system also includes: an energy efficiency service conversion model building module; The energy efficiency service conversion model construction module is used to construct the triggering conditions for energy efficiency service conversion based on the classification conditions of the energy efficiency service model, construct the energy efficiency service conversion model based on the triggering conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

[0035] The present invention also proposes a terminal, including a processor and a storage medium; the processor is used to operate according to the instructions to execute the steps of the standardized energy efficiency service model construction method.

[0036] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a standardized energy efficiency service model construction method.

[0037] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a standardized energy efficiency service model construction method. Based on energy efficiency business data requirements and centered on energy efficiency business standardization, it aggregates energy efficiency business participants, collects energy efficiency business demand data to obtain energy efficiency characteristic data, establishes a multi-source energy efficiency database, and provides the energy efficiency characteristic data required for energy efficiency business operations. It distinguishes energy efficiency data permissions, analyzes energy efficiency service needs, and constructs basic energy efficiency services and a basic energy efficiency service model library based on public energy efficiency data to support public energy efficiency services. Based on private energy efficiency data and market-driven energy efficiency improvement needs, it constructs advanced energy efficiency services and an advanced energy efficiency service model library to support market-driven energy efficiency services. Finally, it constructs an energy efficiency service transformation model in which basic and advanced energy efficiency services mutually support and transform each other, maintaining the universality of public energy efficiency services. The system aims to improve the accessibility and effectiveness of market-based energy efficiency services. Based on energy efficiency service data from energy efficiency service models, an energy efficiency service evaluation model is constructed using energy efficiency business evaluation algorithms. This model is then used to evaluate basic and advanced energy efficiency services. Standardized energy efficiency service evaluation and user evaluation channels are established, along with service evaluation tags for participating entities in energy efficiency service businesses, to monitor the quality of energy efficiency service businesses. A standardized energy efficiency service system is constructed based on a multi-source energy efficiency database, a basic energy efficiency service model library, an advanced energy efficiency service model library, an energy efficiency service evaluation model, and a standard energy efficiency service business library. This standard energy efficiency service business library includes standardized energy efficiency service products and energy efficiency service providers. The goal is to promote the construction of a standardized energy efficiency service system, regulate energy efficiency service market activities, and improve the quality of energy efficiency services. Attached Figure Description

[0038] Figure 1 This is a flowchart of a standardized energy efficiency service model construction method proposed in this invention; Figure 2 This is the standardized energy efficiency service model in the embodiments of the present invention; Figure 3 This refers to the energy efficiency characteristic data engine established in the embodiments of the present invention; Figure 4 This is the energy efficiency service conversion model established in the embodiments of the present invention. Detailed Implementation

[0039] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0040] This invention proposes a method for constructing a standardized energy efficiency service model, such as... Figure 1 As shown, it includes steps 1 to 4.

[0041] Step 1: Obtain energy efficiency characteristic data based on energy efficiency business data requirements, and establish a multi-source energy efficiency database to store energy efficiency characteristic data.

[0042] Specifically, step 1 includes: Step 1.1: Based on the energy efficiency business data requirements, establish an energy consumption business data index based on spatiotemporal accessibility, and use the energy consumption business data index to collect business data from different energy efficiency business participants.

[0043] Step 1.1 includes: Step 1.1.1: Taking the energy efficiency business data demand as the endpoint, and the list of available business data provided by the energy efficiency business participants to form the starting point set; under the spatiotemporal constraints formed by the endpoint and the starting point set, calculate the spatiotemporal reachability from each starting point to the endpoint. Step 1.1.2: Construct an energy consumption business data index using the path between the starting point and the ending point corresponding to the minimum value among multiple spatiotemporal reachability values.

[0044] In step 1.1.1, after successful registration, the energy efficiency business participants provide a list of their own business data that can be supplied; the lists of their own business data that can be supplied by different energy efficiency business participants are classified according to the industry classification of energy users, the type of energy use, and the classification of energy efficiency business. In this embodiment, the main participants in energy efficiency business are suppliers that can provide energy efficiency data and demanders that need to use energy efficiency data. Since energy efficiency services require data support from multiple parties, there are entities that are both suppliers and demanders. These entities include, but are not limited to, energy suppliers, government departments, meteorological service providers, energy customers, and energy efficiency service providers.

[0045] Taking energy efficiency services for high-energy-consuming industries as an example, the business data of the main participants in the energy efficiency business are shown in Table 1: Table 1 Business Data of Participants in Energy Efficiency Business

[0046] The list of available proprietary business data provided by different energy efficiency business participants is categorized according to the industry classification of energy users, energy consumption type, and energy efficiency business classification. The list of available proprietary business data is shown in Table 2. Table 2 List of Available Business Data

[0047] Using the energy efficiency business data requirements published by energy efficiency business participants as the endpoint, and a list of available proprietary business data after classification as the starting point set, the spatiotemporal reachability from each starting point to the endpoint is calculated under the spatiotemporal constraints formed by the endpoint and the starting point set. The energy efficiency business data requirements are shown in Table 3. Table 3 Energy Efficiency Business Data Requirements

[0048] Furthermore, step 1.1.1 includes: Step 1.1.1.1, End Point The starting point is a collection of energy efficiency business data requests issued by energy efficiency business participants. satisfy ,in, and For any two distinct starting points in the starting point set, The number of starting points; each starting point represents a list of proprietary business data that can be supplied. Step 1.1.1.2, using the endpoint The geographical location and power grid location, together with the geographical location and power grid location of each starting point, constitute the spatiotemporal constraints STP. The location of the power grid includes: the voltage level at which the end or starting point of the grid is connected, and the transmission line at which the end or starting point of the grid is connected. Step 1.1.1.3, under the spatiotemporal constraints STP The spatiotemporal reachability from each starting point to the ending point is calculated using the following formula:

[0049] In the formula, Starting point To the finish line Spatiotemporal reachability This is the power grid location correction factor. Starting point Geographical location to destination The time of the geographical location.

[0050] In this embodiment, when summarizing energy efficiency service participants and service data, the geographical location of each energy efficiency service participant and the location of the power grid are fully considered. An energy consumption service data index is established based on spatiotemporal accessibility. The energy consumption service data index can select energy efficiency service participants located at different voltage levels and different power supply line terminals based on the minimum spatiotemporal accessibility. This makes the selection of energy efficiency service participants based on evidence, improves the response speed of energy efficiency service participants in obtaining energy efficiency services, and also achieves the effect of reducing power transmission losses and improving operational economy, thus realizing efficient energy efficiency services.

[0051] Step 1.2: Establish an energy efficiency feature data engine. Use the energy efficiency feature data engine to standardize business data and filter it according to industry energy efficiency standard thresholds. After filtering, extract and select energy efficiency features to obtain energy efficiency feature data. The energy efficiency feature data is stored in a multi-source energy efficiency database.

[0052] like Figure 2 An energy efficiency feature data engine is established to standardize business data and filter it according to industry energy efficiency standard thresholds. After filtering, energy efficiency features are extracted and selected to obtain energy efficiency feature data. The energy efficiency feature data is stored in a multi-source energy efficiency database.

[0053] In this embodiment, the multi-source energy efficiency database takes the energy efficiency business data requirements as its starting point, summarizes the energy efficiency business participants, analyzes the business data of the participants, establishes a business data index and an energy efficiency feature data engine, and extracts the business data of the participants through the business data index. The energy efficiency feature data engine decomposes the business data of the participants into the energy efficiency feature data required by the energy efficiency business, thereby providing energy efficiency business data services.

[0054] In addition, the data in the multi-source energy efficiency database comes from the business data of many energy efficiency participants. Participants can independently publish their own business data, which is then transformed and decomposed into energy efficiency characteristic data that can be directly used for energy efficiency business through the energy efficiency characteristic data engine. This maintains data accumulation, ensures data timeliness, and allows energy efficiency participants to obtain added value from data services.

[0055] Specifically, step 1.2 includes: Step 1.2.1: Based on rule engine technology, establish an energy efficiency feature data engine using data processing rules; the data processing rules include: data identification rules, data extraction rules, data parsing rules, data optimization rules, data entry rules, data request rules, data maintenance rules, and data anonymization rules; and use the energy efficiency feature data engine to decompose, clean, and integrate the collected business data to obtain energy efficiency data.

[0056] Furthermore, in step 1.2.1, the energy efficiency data includes: total meter data, sub-meter data, user energy consumption strategy data, user energy bills, user production plans, user product energy consumption, energy price data, energy policies, energy meteorological data, energy-saving technologies, and energy-saving strategies; among them, the total meter data, sub-meter data, user energy consumption strategy data, user energy bills, user production plans, and user product energy consumption all contain numerical data types.

[0057] The main business data includes master table metering data, sub-table metering data, user energy consumption strategy data, user energy consumption bills, user production plans, and user product energy consumption. The data volume is large and the data quality is unstable. It is the core production and operation data of the enterprise and cannot be directly used as energy efficiency data to support public energy efficiency services. It is necessary to extract features based on the energy efficiency service business content to make it usable energy efficiency data for energy efficiency services.

[0058] In this embodiment, an energy efficiency feature data engine is used to extract features from the core production and operation data of an enterprise, thereby obtaining various energy efficiency feature data that can support energy efficiency services, thus avoiding the impact of various adverse factors such as large data volume and unstable data quality.

[0059] Step 1.2.2: Normalize the original values ​​of the numerical type features in the energy efficiency data to obtain normalized features. The values ​​of all normalized features are unified to the same numerical range.

[0060] Furthermore, step 1.2.2 includes: Step 1.2.2.1: The original values ​​of the numerical features in the energy efficiency data are linearly transformed using the min-max normalization method. The resulting linearized feature values ​​satisfy the following relationship:

[0061] In the formula, These are the raw values ​​for the numerical type characteristics in the energy efficiency data. The numerical values ​​of the linearized features. It is the minimum value among the original numerical values ​​of the numerical type feature. The maximum value among the original numerical values ​​of the numerical type feature; Step 1.2.2.2: The z-score normalization method is used to normalize the values ​​of the linearized features. The resulting linearly normalized features satisfy the following relationship:

[0062] In the formula, The numerical values ​​of the linearly normalized features. The average value of the linearized feature. The standard deviation of the linearized feature values; Step 1.2.2.3: Perform decimal scaling normalization on the numerical values ​​of the linear normalized features. The resulting normalized feature values ​​satisfy the following relationship:

[0063] In the formula, For the numerical values ​​of the normalized features, It makes The smallest integer.

[0064] Step 1.2.3: The standardized characteristics are screened according to energy efficiency standards to obtain standard energy efficiency characteristics; among which, energy efficiency standards include: international energy efficiency standards, national energy efficiency standards, industry energy efficiency standards, and enterprise energy efficiency standards; Step 1.2.4: Filter according to the importance index of standard energy efficiency characteristics for energy efficiency business to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics.

[0065] Furthermore, step 1.2.4 includes: Step 1.2.4.1: Use separability as a criterion to determine the inter-class separability of standard energy efficiency characteristics, whereby standard energy efficiency characteristics participate in relevant classes of energy efficiency business. Unrelated categories that participate in energy efficiency business with standard energy efficiency characteristics log-likelihood ratio The following relationship must be satisfied:

[0066] In the formula, As a standard energy efficiency characteristic, For standard energy efficiency characteristics related categories, This is a category unrelated to standard energy efficiency characteristics; Related classes For irrelevant classes Class separability The following relationship must be satisfied:

[0067] Step 1.2.4.2, using KL divergence KL divergence is used as an indicator of the importance of standard energy efficiency characteristics to energy efficiency operations. The following relationship must be satisfied:

[0068] In the formula, For related classes For irrelevant classes Separability between classes Unrelated class For related classes Separability between classes; Step 1.2.4.3: Filter according to the importance index of standard energy efficiency characteristics for energy efficiency business to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics.

[0069] Step 1.2.5: Using energy efficiency evaluation indicators as decision points, establish an XGBoost decision tree based on the correlation between standard energy efficiency characteristics and energy efficiency evaluation indicators. Perform feature determination on the production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics obtained from Step 2.4 to obtain energy efficiency characteristic data.

[0070] Further, in step 1.2.5.1, the safety performance evaluation index, economic performance evaluation index, and sustainable production performance evaluation index are used as decision points; when splitting the decision point, if the value of the loss function decreases after splitting, then the decision point is split, and the loss function satisfies the following relationship:

[0071] In the formula, and These represent the left and right subtrees, respectively. For the weights of the child nodes in the decision tree, Represents the sum of the first derivatives of the left child node. Represents the sum of the first derivatives of the right child node. Represents the sum of the first derivatives of the left child node. This represents the sum of the first derivatives of the right child node; This is the loss correction value for the sum of the derivatives of the child nodes; Step 1.2.5.2: Construct the XGBoost decision tree by selecting local features and training data to determine features.

[0072] In the formula, This represents the prediction result of the k-th tree. This represents the cumulative loss of the prediction results. This represents the transformation of the prediction result of the k-th tree. Representing true characteristics, Indicates pre-stored features, This represents the total amount of data in the k-th tree. This indicates the number of trees created. This indicates the optimization result. Indicates the correction amount.

[0073] Step 1.2.6: Store the energy efficiency characteristic data in the multi-source energy efficiency database.

[0074] Step 1.3: When the energy efficiency characteristic data obtained in Step 1.2 cannot meet the energy efficiency business data requirements published by the energy efficiency business participants, the business data is located in reverse using the energy efficiency characteristic data engine and the energy consumption business data index. Then, the energy efficiency business participants are located again based on the business data, and Steps 1.1 and 1.2 are repeated to obtain the required energy efficiency characteristic data.

[0075] Step 2: Classify the energy efficiency characteristic data in the multi-source energy efficiency database, and establish an energy efficiency service model based on the classification results of the energy efficiency characteristic data; wherein, the energy efficiency service model includes: basic energy efficiency service and advanced energy efficiency service; establish a basic energy efficiency service model library to store basic energy efficiency services, and establish an advanced energy efficiency service model library to store advanced energy efficiency services.

[0076] Specifically, step 2 includes: Step 2.1: Construct an energy efficiency characteristic data partitioning model based on the data ownership and public availability of the energy efficiency characteristic data from Step 1; Step 2.2: Use the energy efficiency characteristic data partitioning model to partition the energy efficiency characteristic data. Energy efficiency characteristic data with non-private ownership and public access is partitioned into public energy efficiency data, and energy efficiency characteristic data with private ownership and non-public access is partitioned into private energy efficiency data.

[0077] In step 2.2, the public energy efficiency data includes: total meter readings, energy price data, energy strategies, energy meteorological data, energy-saving technologies, and energy-saving strategies. Private energy efficiency data includes: sub-metering data, user energy consumption strategy data, user energy consumption bills, user production plans, and user product energy consumption.

[0078] Step 2.3: Using the energy efficiency business segmentation model, energy efficiency businesses whose public energy efficiency data can meet the energy efficiency business data requirements are classified as basic energy efficiency services, and energy efficiency businesses whose private energy efficiency data can meet the energy efficiency business data requirements are classified as advanced energy efficiency services. Among them, basic energy efficiency services are stored in the basic energy efficiency service model library, and advanced energy efficiency services are stored in the advanced energy efficiency service model library.

[0079] In step 2.3, for basic energy efficiency services, the model is built, trained, and optimized according to the process of business analysis, data requirement analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to obtain the basic energy efficiency service model. The basic energy efficiency service model is stored in the basic energy efficiency service model library. The basic energy efficiency service model includes: energy meteorology model for industrial enterprises, electricity substitution model for pharmaceutical enterprises, and regional load forecasting model. For advanced energy efficiency services, the process involves business analysis, data requirements analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to build, train, and optimize models, thereby obtaining advanced energy efficiency service models. These models are stored in an advanced energy efficiency service model library. Among them, advanced energy efficiency service models include: enterprise product energy consumption improvement models, enterprise capacity demand energy efficiency optimization models, and enterprise energy bill optimization models.

[0080] Step 3: Based on the energy efficiency business service data of the energy efficiency service model, construct an energy efficiency service evaluation model using the energy efficiency business evaluation algorithm, and use the energy efficiency service evaluation model to evaluate basic energy efficiency services and advanced energy efficiency services.

[0081] Specifically, in step 3, the evaluation objects of the energy efficiency service evaluation model include: energy efficiency service providers, energy efficiency service models, energy efficiency service businesses, and energy efficiency service users; each evaluation object contains evaluation tags. Energy efficiency service users submit user evaluations through the energy efficiency service evaluation model. The user evaluations include: service completeness, service effectiveness, service quality, service attitude, and service process. The user evaluations are used as evaluation tags and are respectively bound to each evaluation object.

[0082] Standardized energy efficiency service evaluation includes: energy efficiency business content analysis, energy efficiency business service data collection, service evaluation data feature extraction, energy efficiency business evaluation algorithm configuration, energy efficiency business evaluation rule configuration, and energy efficiency business evaluation model optimization; standardized energy efficiency service evaluation is used as an evaluation tag and is bound to the corresponding evaluation object.

[0083] In this embodiment, the evaluation objects of the energy efficiency service evaluation model include: energy efficiency service providers, energy efficiency service models, energy efficiency service businesses, and energy efficiency service users; each evaluation object contains an evaluation tag; energy efficiency service users submit user evaluations through the energy efficiency service evaluation model, and the user evaluations include: service completeness, service effectiveness, service quality, service attitude, and service process; user evaluations are used as evaluation tags and are respectively bound to each evaluation object.

[0084] Furthermore, standardized energy efficiency service evaluation includes: energy efficiency service content analysis, energy efficiency service data collection, service evaluation data feature extraction, energy efficiency service evaluation algorithm configuration, energy efficiency service evaluation rule configuration, and energy efficiency service evaluation model optimization. Standardized energy efficiency service evaluations are used as evaluation tags, each bound to a corresponding evaluation object. The energy efficiency service evaluation model consists of evaluation tags, user evaluations, and standardized energy efficiency service evaluations. It is used to assess the implementation of energy efficiency services, provide users with evaluation channels, offer a standardized evaluation mechanism, facilitate the optimization of the energy efficiency service model, improve the quality of energy efficiency services, and effectively achieve energy efficiency improvements for users.

[0085] The construction method includes steps 3 and 4, including: Based on the classification conditions of the energy efficiency service model in step 3, construct the trigger conditions for energy efficiency service conversion, build the energy efficiency service conversion model based on the trigger conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

[0086] Therefore, the standardized energy efficiency service system also includes an energy efficiency service conversion model.

[0087] Triggering conditions include: changes in the source of business data, changes in the entities participating in energy efficiency service business, changes in the demand for energy efficiency service business, changes in the development of energy efficiency technology, changes in the content of energy efficiency service business, and changes in the rules of energy efficiency service business.

[0088] When changes in public energy efficiency data render it unable to meet the requirements of the basic energy efficiency service model, the basic energy efficiency service model is removed from the basic energy efficiency service model library and transformed into an advanced energy efficiency service model stored in the advanced energy efficiency service model library. These changes in public energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the public energy efficiency data. When changes in private energy efficiency data render it unable to meet the requirements of the Advanced Energy Efficiency Service Model (AESM), the AESM is removed from the AESM library and converted into a Basic Energy Efficiency Service Model (BESSM) and stored in the BESSM library. Changes in private energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the private energy efficiency data.

[0089] In the embodiments, such as Figure 3 An energy efficiency service conversion model is established to realize the conversion between basic energy efficiency services and advanced energy efficiency services, so as to cope with the impact of technological development, policy adjustments and the energy efficiency service market on the energy efficiency service model and maintain the effectiveness of the energy efficiency service model.

[0090] The evaluation of changes in the energy efficiency service transformation model includes, but is not limited to, the source of business data, the scope of business services, the support capabilities of integrated services, energy efficiency technologies, the development level of energy efficiency services, and energy efficiency business rules.

[0091] Step 4: Based on the energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library, a standardized energy efficiency service system is constructed.

[0092] Specifically, in step 4, the participants in the energy efficiency business include: energy suppliers, government departments, meteorological service providers, energy customers, and energy efficiency service providers. The standard energy efficiency service business database includes a directory of energy efficiency service products and energy efficiency service providers; among them, energy efficiency service products include: enterprise product energy consumption improvement services, enterprise capacity demand energy efficiency optimization services, enterprise energy bill optimization services, and regional load forecasting services.

[0093] Figure 4 In China, the standardized energy efficiency service system built on the standardized energy efficiency service model includes: energy efficiency business entities, energy consumption business data index, energy efficiency feature data engine, multi-source energy efficiency database, reverse positioning of energy efficiency feature data, energy efficiency feature data partitioning model, energy efficiency business partitioning model, energy efficiency service conversion model, energy efficiency service evaluation model, and standardized energy efficiency service business library.

[0094] The energy efficiency business entities include, but are not limited to: energy suppliers, government smart departments, meteorological service providers, energy customers, and energy efficiency service providers that join the standardized energy efficiency service system after registration. These entities submit a list of their own available business data. The energy consumption business data index uses the data list submitted by the business entities to establish a business data catalog, extracting energy supply data, energy policy data, energy meteorological data, production energy consumption data, planned energy consumption data, and energy saving calculation data, and establishing a business data query catalog. The energy efficiency feature data engine can retrieve data through the energy consumption business data index, extract the energy consumption data required for energy efficiency business, perform feature extraction, and obtain energy efficiency feature data. The energy efficiency characteristic data partitioning model divides energy efficiency characteristic data into public and private energy efficiency data stored in a multi-source energy efficiency database. The standardized energy efficiency service business library is a collection of energy efficiency service businesses. The energy efficiency business partitioning model divides advanced energy efficiency service businesses based on both public and private energy efficiency data in the multi-source energy efficiency database. The energy efficiency data required for these services comes from the multi-source energy efficiency database. When data is insufficient, the energy efficiency characteristic data engine and energy consumption business data index can be used to reverse locate the business data and supplement it. The business collection in the standardized energy efficiency service business library can provide energy efficiency services to energy efficiency entities, who can access these services through [the database]. The energy efficiency service evaluation model is used to conduct user evaluations.

[0095] In another aspect, this invention proposes a standardized energy efficiency service model construction system and implements a standardized energy efficiency service model construction method.

[0096] The system includes: a multi-source energy efficiency database construction module, a basic energy efficiency service model library construction module, an energy efficiency service evaluation model construction module, a standard energy efficiency service business library construction module, and a standardized energy efficiency service system construction module; The multi-source energy efficiency database construction module is used to obtain energy efficiency characteristic data according to the energy efficiency business data requirements and establish a multi-source energy efficiency database to store the energy efficiency characteristic data. The basic energy efficiency service model library construction module is used to classify energy efficiency characteristic data in the multi-source energy efficiency database and establish energy efficiency service models based on the classification results. The energy efficiency service models include: basic energy efficiency services and advanced energy efficiency services. The basic energy efficiency service model library construction module includes a basic energy efficiency service model library unit and an advanced energy efficiency service model library unit. The basic energy efficiency service model library unit stores basic energy efficiency services, and the advanced energy efficiency service model library unit stores advanced energy efficiency services. The energy efficiency service evaluation model construction module is used to construct an energy efficiency service evaluation model based on the energy efficiency business service data of the energy efficiency service model and the energy efficiency business evaluation algorithm, and to evaluate the basic energy efficiency service and advanced energy efficiency service using the energy efficiency service evaluation model. The standardized energy efficiency service system construction module is used to construct a standardized energy efficiency service system based on energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library.

[0097] The system also includes: an energy efficiency service conversion model building module; The energy efficiency service conversion model construction module is used to construct the triggering conditions for energy efficiency service conversion based on the classification conditions of the energy efficiency service model, construct the energy efficiency service conversion model based on the triggering conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

[0098] The present invention also proposes a terminal, including a processor and a storage medium; the processor is used to operate according to the instructions to execute the steps of the standardized energy efficiency service model construction method.

[0099] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a standardized energy efficiency service model construction method.

[0100] The beneficial effects of this invention are compared with those of the prior art: By analyzing the content of energy efficiency service business, tracing the source of energy efficiency service data, summarizing the participants in energy efficiency business, establishing an index of main energy efficiency business data, establishing a main business data mining model, and providing the business process of converting energy consumption data into energy efficiency data for energy efficiency business data services; Based on the z-score algorithm specification for energy consumption data, the characteristics of KL divergence are used to select and extract various types of energy consumption data, and the XGBoost decision tree algorithm is used to determine the effectiveness of energy efficiency data features. This method involves establishing an index of core energy efficiency business data and using a core business data mining model to transform private energy consumption business data into public energy efficiency analysis data.

[0101] Establish a basic energy efficiency service model library. Based on the ownership of energy efficiency business data and the openness of energy efficiency data, distinguish public energy efficiency data, analyze the energy efficiency business data needs, classify the business that can be satisfied by public energy efficiency data as public energy efficiency services, and build a basic energy efficiency service model library to carry out public energy efficiency services. Establish an advanced energy efficiency service model library. Based on the ownership of energy efficiency business data and the openness of energy efficiency data, distinguish energy efficiency data, analyze the energy efficiency business data requirements, classify the business that can be satisfied by public energy efficiency data as public energy efficiency services, and build a basic energy efficiency service model library to carry out advanced energy efficiency services. Establish a conversion model for basic energy efficiency services and advanced energy efficiency services. Based on changes in public and private energy efficiency data, design conversion rules, configure conversion parameters, and form a mutual conversion and support mechanism. Establish an energy efficiency service evaluation model, design energy efficiency service evaluation and assessment rules centered on energy efficiency service, set energy efficiency service evaluation labels, user evaluations, and standardized energy efficiency service evaluations, provide energy efficiency service quality standards, and display energy efficiency service quality evaluation content; Based on the construction of a multi-source energy efficiency database, an energy efficiency service model library, an energy efficiency service conversion model, and an energy efficiency service evaluation model, a standardized energy efficiency service business library is established by summarizing energy efficiency service products and energy efficiency service providers, and a standard-for-energy-efficiency service system is constructed.

[0102] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0103] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0104] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0105] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via 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). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0106] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0107] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0108] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0110] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

Claims

1. A method for constructing a standardized energy efficiency service model, characterized in that, The construction method includes: Step 1: Obtain energy efficiency characteristic data according to the energy efficiency business data requirements, and establish a multi-source energy efficiency database to store the energy efficiency characteristic data; Step 1 includes: Step 1.1: Based on the energy efficiency business data requirements, establish an energy consumption business data index based on spatiotemporal accessibility, and use the energy consumption business data index to collect business data from different energy efficiency business participants; Step 1.1 includes: Step 1.1.1: Taking the energy efficiency business data demand as the endpoint, and the list of available business data provided by the energy efficiency business participants to form the starting point set; under the spatiotemporal constraints formed by the endpoint and the starting point set, calculate the spatiotemporal reachability from each starting point to the endpoint. Step 1.1.2: Construct an energy consumption business data index using the path between the starting point and the ending point corresponding to the minimum value among multiple spatiotemporal reachability values; Step 1.2: Establish an energy efficiency feature data engine. Use the energy efficiency feature data engine to standardize business data and filter it according to industry energy efficiency standard thresholds. After filtering, extract and select energy efficiency features to obtain energy efficiency feature data. The energy efficiency feature data is stored in a multi-source energy efficiency database. Step 1.3: When the energy efficiency characteristic data obtained in Step 1.2 cannot meet the energy efficiency business data requirements published by the energy efficiency business participants, the energy efficiency characteristic data engine and energy consumption business data index are used to reverse locate the business data. Based on the business data, the energy efficiency business participants are located again, and Steps 1.1 and 1.2 are repeated to obtain the required energy efficiency characteristic data. Step 2: Classify the energy efficiency characteristic data in the multi-source energy efficiency database, and establish an energy efficiency service model based on the classification results of the energy efficiency characteristic data; wherein, the energy efficiency service model includes: basic energy efficiency service and advanced energy efficiency service; establish a basic energy efficiency service model library to store basic energy efficiency services, and establish an advanced energy efficiency service model library to store advanced energy efficiency services; Step 3: Based on the energy efficiency business service data of the energy efficiency service model, construct an energy efficiency service evaluation model using the energy efficiency business evaluation algorithm, and use the energy efficiency service evaluation model to evaluate basic energy efficiency services and advanced energy efficiency services. Step 4: Based on the energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library, a standardized energy efficiency service system is constructed.

2. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, Step 2 includes: Step 2.1: Construct an energy efficiency characteristic data partitioning model based on the data ownership and public availability of the energy efficiency characteristic data from Step 1; Step 2.2: Use the energy efficiency characteristic data partitioning model to partition the energy efficiency characteristic data. Energy efficiency characteristic data with non-private ownership and public ownership is partitioned into public energy efficiency data, and energy efficiency characteristic data with private ownership and non-public ownership is partitioned into private energy efficiency data. Step 2.3: Using the energy efficiency business segmentation model, energy efficiency businesses whose public energy efficiency data can meet the energy efficiency business data requirements are classified as basic energy efficiency services, and energy efficiency businesses whose private energy efficiency data can meet the energy efficiency business data requirements are classified as advanced energy efficiency services. Among them, basic energy efficiency services are stored in the basic energy efficiency service model library, and advanced energy efficiency services are stored in the advanced energy efficiency service model library.

3. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, The steps preceding step 4 also include: Based on the classification conditions of the energy efficiency service model in step 3, construct the trigger conditions for energy efficiency service conversion, build the energy efficiency service conversion model based on the trigger conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

4. The method for constructing a standardized energy efficiency service model according to claim 3, characterized in that, The standardized energy efficiency service system also includes an energy efficiency service conversion model.

5. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, Step 1.1.1 includes: Step 1.1.1.1, End Point The starting point is a collection of energy efficiency business data requests issued by energy efficiency business participants. satisfy ,in, and For any two distinct starting points in the starting point set, The number of starting points; each starting point represents a list of proprietary business data that can be supplied. Step 1.1.1.2, using the endpoint The geographical location and power grid location, together with the geographical location and power grid location of each starting point, constitute the spatiotemporal constraints STP. The location of the power grid includes: the voltage level at which the end or starting point of the grid is connected, and the transmission line at which the end or starting point of the grid is connected. Step 1.1.1.3, under the spatiotemporal constraints STP The spatiotemporal reachability from each starting point to the ending point is calculated using the following formula: In the formula, Starting point To the finish line Spatiotemporal reachability This is the power grid location correction factor. Starting point Geographical location to destination The time of the geographical location.

6. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, Step 1.2 includes: Step 1.2.1: Based on rule engine technology, establish an energy efficiency feature data engine using data processing rules; and use the energy efficiency feature data engine to decompose, clean, and integrate the collected business data to obtain energy efficiency data. Step 1.2.2 involves standardizing the energy efficiency data and filtering it according to energy efficiency standards and importance indices to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics. Step 1.2.3: Using energy efficiency evaluation indicators as decision points, establish an XGBoost decision tree based on the correlation between standard energy efficiency characteristics and energy efficiency evaluation indicators. Perform feature determination on production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics to obtain energy efficiency characteristic data. Step 1.2.4: Store the energy efficiency characteristic data in the multi-source energy efficiency database.

7. The method for constructing a standardized energy efficiency service model according to claim 6, characterized in that, Step 1.2.2 includes: Step 1.2.2.1: The original values ​​of the numerical features in the energy efficiency data are linearly transformed using the min-max normalization method. The resulting linearized feature values ​​satisfy the following relationship: In the formula, These are the raw values ​​for the numerical type characteristics in the energy efficiency data. The numerical values ​​of the linearized features. It is the minimum value among the original numerical values ​​of the numerical type feature. The maximum value among the original numerical values ​​of the numerical type feature; Step 1.2.2.2: The z-score normalization method is used to normalize the values ​​of the linearized features. The resulting linearly normalized feature values ​​satisfy the following relationship: In the formula, The numerical values ​​of the linearly normalized features. The average value of the linearized feature. The standard deviation of the linearized feature values; Step 1.2.2.3: Perform decimal scaling normalization on the numerical values ​​of the linear normalized features. The resulting normalized feature values ​​satisfy the following relationship: In the formula, For the numerical values ​​of the normalized features, It makes The smallest integer.

8. The method for constructing a standardized energy efficiency service model according to claim 6, characterized in that, Step 1.2.2 also includes: Step 1.2.2.4: Use separability as a criterion to determine the inter-class separability of standard energy efficiency characteristics, whereby standard energy efficiency characteristics participate in relevant classes of energy efficiency business. Unrelated categories that participate in energy efficiency business with standard energy efficiency characteristics log-likelihood ratio The following relationship must be satisfied: In the formula, Standard energy efficiency characteristics, For standard energy efficiency characteristics related categories, This is a category unrelated to standard energy efficiency characteristics; Related classes For irrelevant classes Class separability The following relationship must be satisfied: Step 1.2.2.5, using KL divergence KL divergence is used as an indicator of the importance of standard energy efficiency characteristics to energy efficiency operations. The following relationship must be satisfied: In the formula, For related classes For irrelevant classes Separability between classes Unrelated class For related classes Separability between classes; Step 1.2.2.6: Filter according to the importance index of standard energy efficiency characteristics for energy efficiency business to obtain production energy consumption characteristics, residential energy consumption characteristics, product energy consumption characteristics, and energy safety characteristics.

9. The method for constructing a standardized energy efficiency service model according to claim 6, characterized in that, Step 1.2.3 includes: Step 1.2.3.1: Using safety performance evaluation index, economic performance evaluation index, and sustainable production performance evaluation index as decision points; when splitting a decision point, if the value of the loss function decreases after splitting, then the decision point is split. The loss function satisfies the following relationship: In the formula, and These represent the left and right subtrees, respectively. For the weights of the child nodes in the decision tree, Represents the sum of the first derivatives of the left child node. Represents the sum of the first derivatives of the right child node. Represents the sum of the first derivatives of the left child node. This represents the sum of the first derivatives of the right child node; is the loss correction value for the sum of derivatives of child nodes; Gain is the loss function; Step 1.2.3.2: Construct the XGBoost decision tree by selecting local features and training data to determine features. In the formula, This represents the prediction result of the k-th tree. This represents the cumulative loss of the prediction results. This represents the transformation of the prediction result of the k-th tree. Representing true characteristics, Indicates pre-stored features, This represents the total amount of data in the k-th tree. This indicates the number of trees created. This indicates the optimization result. Indicates the correction amount.

10. The method for constructing a standardized energy efficiency service model according to claim 2, characterized in that, In step 2.2, the public energy efficiency data includes: total meter readings, energy price data, energy strategies, energy meteorological data, energy-saving technologies, and energy-saving strategies. Private energy efficiency data includes: sub-metering data, user energy consumption strategy data, user energy consumption bills, user production plans, and user product energy consumption.

11. The method for constructing a standardized energy efficiency service model according to claim 2, characterized in that, In step 2.3, for basic energy efficiency services, the model is built, trained, and optimized according to the process of business analysis, data requirement analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to obtain the basic energy efficiency service model. The basic energy efficiency service model is stored in the basic energy efficiency service model library. The basic energy efficiency service model includes: energy meteorology model for industrial enterprises, electricity substitution model for pharmaceutical enterprises, and regional load forecasting model. For advanced energy efficiency services, the process involves business analysis, data requirements analysis, data location, data extraction, data filtering, data cleaning, feature extraction, feature selection, feature standardization, threshold configuration, and algorithm configuration to build, train, and optimize models, thereby obtaining advanced energy efficiency service models. These models are stored in an advanced energy efficiency service model library. Among them, advanced energy efficiency service models include: enterprise product energy consumption improvement models, enterprise capacity demand energy efficiency optimization models, and enterprise energy bill optimization models.

12. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, In step 3, the evaluation objects of the energy efficiency service evaluation model include: energy efficiency service providers, energy efficiency service models, energy efficiency service businesses, and energy efficiency service users; each evaluation object contains evaluation tags. Energy efficiency service users submit user evaluations through the energy efficiency service evaluation model. The user evaluations include: service completeness, service effectiveness, service quality, service attitude, and service process. The user evaluations are used as evaluation tags and are respectively bound to each evaluation object.

13. The method for constructing a standardized energy efficiency service model according to claim 12, characterized in that, Standardized energy efficiency service evaluation includes: energy efficiency business content analysis, energy efficiency business service data collection, service evaluation data feature extraction, energy efficiency business evaluation algorithm configuration, energy efficiency business evaluation rule configuration, and energy efficiency business evaluation model optimization; standardized energy efficiency service evaluation is used as an evaluation tag and is bound to the corresponding evaluation object.

14. The method for constructing a standardized energy efficiency service model according to claim 1, characterized in that, In step 4, the participants in the energy efficiency business include: energy suppliers, government departments, meteorological service providers, energy customers, and energy efficiency service providers. The standard energy efficiency service business database includes a directory of energy efficiency service products and energy efficiency service providers; among them, energy efficiency service products include: enterprise product energy consumption improvement services, enterprise capacity demand energy efficiency optimization services, enterprise energy bill optimization services, and regional load forecasting services.

15. The method for constructing a standardized energy efficiency service model according to claim 3, characterized in that, Triggering conditions include: changes in the source of business data, changes in the entities participating in energy efficiency service business, changes in the demand for energy efficiency service business, changes in the development of energy efficiency technology, changes in the content of energy efficiency service business, and changes in the rules of energy efficiency service business.

16. The method for constructing a standardized energy efficiency service model according to claim 15, characterized in that, When changes in public energy efficiency data render it unable to meet the requirements of the basic energy efficiency service model, the basic energy efficiency service model is removed from the basic energy efficiency service model library and transformed into an advanced energy efficiency service model stored in the advanced energy efficiency service model library. These changes in public energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the public energy efficiency data. When changes in private energy efficiency data render it unable to meet the requirements of the Advanced Energy Efficiency Service Model (AESM), the AESM is removed from the AESM library and converted into a Basic Energy Efficiency Service Model (BESSM) and stored in the BESSM library. Changes in private energy efficiency data include changes in the scope of protection, data sensitivity, and data subject of the private energy efficiency data.

17. A standardized energy efficiency service model construction system, which implements the standardized energy efficiency service model construction method as described in any one of claims 1-16, characterized in that, The construction system includes: a multi-source energy efficiency database construction module, a basic energy efficiency service model library construction module, an energy efficiency service evaluation model construction module, a standard energy efficiency service business library construction module, and a standardized energy efficiency service system construction module; The multi-source energy efficiency database construction module is used to obtain energy efficiency characteristic data according to the energy efficiency business data requirements and establish a multi-source energy efficiency database to store the energy efficiency characteristic data. The basic energy efficiency service model library construction module is used to classify energy efficiency characteristic data in the multi-source energy efficiency database and establish energy efficiency service models based on the classification results. The energy efficiency service models include: basic energy efficiency services and advanced energy efficiency services. The basic energy efficiency service model library construction module includes a basic energy efficiency service model library unit and an advanced energy efficiency service model library unit. The basic energy efficiency service model library unit stores basic energy efficiency services, and the advanced energy efficiency service model library unit stores advanced energy efficiency services. The energy efficiency service evaluation model construction module is used to construct an energy efficiency service evaluation model based on the energy efficiency business service data of the energy efficiency service model and the energy efficiency business evaluation algorithm, and to evaluate the basic energy efficiency service and advanced energy efficiency service using the energy efficiency service evaluation model. The standardized energy efficiency service system construction module is used to construct a standardized energy efficiency service system based on energy efficiency business participants, multi-source energy efficiency database, basic energy efficiency service model library, advanced energy efficiency service model library, energy efficiency service evaluation model, and standard energy efficiency service business library.

18. The standardized energy efficiency service model construction system according to claim 17, characterized in that, The system also includes: an energy efficiency service conversion model construction module; The energy efficiency service conversion model construction module is used to construct the triggering conditions for energy efficiency service conversion based on the classification conditions of the energy efficiency service model, construct the energy efficiency service conversion model based on the triggering conditions, and use the energy efficiency service conversion model to convert between basic energy efficiency services and advanced energy efficiency services.

19. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the standardized energy efficiency service model construction method according to any one of claims 1-16.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the standardized energy efficiency service model construction method according to any one of claims 1-16.

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