Energy efficiency analysis method and device based on multi-source data

Abstract

The present application discloses an energy efficiency analysis method and system based on multi-source data. The server-side method includes: obtaining the power consumption data, equipment operating parameters and equipment environmental parameters of each power device in the target area according to a preset period to obtain the multi-source data of each power device; performing data modeling and standardization on the multi-source data of each power device to obtain the multi-source standardized data of each power device; generating the energy consumption analysis results of each power device based on the multi-source standardized data; visualizing the energy consumption analysis results of each power device, obtaining a dynamic dashboard and sending it to the client to display a real-time energy flow diagram and equipment energy efficiency ranking. Therefore, by adopting the embodiment of the present application, unified management and analysis of data can be achieved, and the comprehensiveness and accuracy of energy efficiency analysis can be improved. At the same time, it can reflect the dynamic changes of energy consumption in real time, so that managers can discover energy waste or abnormal conditions in a timely manner, and thus take energy optimization measures in a timely manner.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an energy efficiency analysis method and device based on multi-source data. Background Art

[0002] In modern industrial production operations, energy efficiency analysis is a key step in achieving efficient operations, reducing costs, and meeting environmental protection requirements. For example, industrial companies need to accurately monitor and optimize energy consumption to reduce production costs while meeting increasingly stringent carbon emission standards.

[0003] In related technologies, energy efficiency analysis mainly adopts the traditional energy efficiency analysis system (EMS).

[0004] However, the system's decentralized software architecture prevented effective data integration across disparate systems. For example, power consumption data, power equipment operating parameters, and equipment environmental parameters were often collected and stored in separate systems by different components, making unified management and analysis difficult. This data siloing limited the comprehensiveness and accuracy of energy efficiency analysis. Furthermore, the system's inability to reflect dynamic changes in energy consumption in real time prevented managers from promptly identifying energy waste or anomalies, hindering their ability to implement energy optimization measures. Summary of the Invention

[0005] The embodiments of this application provide a method and apparatus for energy efficiency analysis based on multi-source data. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is provided below. This summary is not intended to be a comprehensive review, identify key or important elements, or delineate the scope of protection for these embodiments. Its sole purpose is to present some concepts in a simplified form, serving as a prelude to the detailed description that follows.

[0006] In a first aspect, an embodiment of the present application provides an energy efficiency analysis method based on multi-source data, which is applied to a server. The method includes:

[0007] Acquire power consumption data, equipment operating parameters, and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device;

[0008] Perform data modeling and standardization on the multi-source data of each power equipment to obtain multi-source standardized data of each power equipment;

[0009] Generate energy consumption analysis results for each power device based on multi-source standardized data;

[0010] Visualize the energy consumption analysis results of each power device, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

[0011] Optionally, data modeling and standardization are performed on the multi-source data of each power device to obtain the multi-source standardized data of each power device, including:

[0012] Define the entity relationship data structures corresponding to power energy consumption data, equipment operating parameters, and equipment environmental parameters, and obtain the energy consumption data structure, equipment data structure, and environmental parameter data structure;

[0013] The power consumption data, power equipment operating parameters and equipment environmental parameters of each power device are mapped to the energy consumption data structure, the equipment data structure and the environmental parameter data structure respectively, so as to obtain multi-source data with a unified data structure for each power device;

[0014] Time alignment is performed on multi-source data with unified data structure to obtain multi-source standardized data of each power equipment.

[0015] Optionally, the energy consumption data structure includes a power identifier and a power identifier; the device data structure includes a device status identifier, an operating time identifier, and a load rate identifier; and the environmental parameter data structure includes a temperature identifier and a humidity identifier.

[0016] The power consumption data, power equipment operating parameters, and equipment environmental parameters of each power device are mapped to the energy consumption data structure, the equipment data structure, and the environmental parameter data structure, respectively, to obtain multi-source data with a unified data structure, including:

[0017] Extracting descriptive information matching the power identifier and the power identifier from the power consumption data of each power device;

[0018] Extracting descriptive information matching the device status identifier, the operating time identifier, and the load rate identifier from the power device operating parameters of each power device;

[0019] Extracting descriptive information matching the temperature identifier and humidity identifier from the equipment environmental parameters of each power equipment;

[0020] Identify multiple entity keywords of each extracted description information, perform entity screening on the multiple entity keywords of each description information, and generate attribute values ​​of each description information;

[0021] Convert the attribute value of each descriptive information into a format according to the data format indicated by the identifier corresponding to each descriptive information to obtain a standard value of each descriptive information;

[0022] A mapping relationship between the identifier corresponding to each descriptive information and the standard value of each descriptive information is established to obtain multi-source data with a unified data structure.

[0023] Optionally, a keyword recognition model is pre-set, which includes multiple text encoders, an attention network with an attention mechanism, a neural network for labeling and segmenting sequence data, and a keyword output layer;

[0024] Identify multiple entity keywords for each extracted description, including:

[0025] Each extracted description information is sequentially input into multiple text encoders to convert each description information into a vector representation of a fixed dimension to obtain multiple entity vectors;

[0026] Input multiple entity vectors into the attention network and output the word vector given the attention mechanism;

[0027] Input the word vector into the neural network used to label and segment sequence data, and output a sequence of keyword labels;

[0028] The keyword tag sequence is mapped to entity tags through the output layer to obtain multiple entity keywords for each description information.

[0029] Optionally, multiple entity keywords for each description are filtered to generate attribute values ​​for each description, including:

[0030] Traverse the target entity keywords from multiple entity keywords of each description information;

[0031] Calculating the similarity between the target entity keyword and other entity keywords among the multiple entity keywords except the target entity keyword;

[0032] Filter out the entity keywords with the largest attribute value from the entity keywords whose similarity is greater than the preset threshold;

[0033] The attribute value of the entity keyword with the largest attribute value is used as the attribute value of each description information.

[0034] Optionally, data modeling and standardization are performed on the multi-source data of each power device to obtain the multi-source standardized data of each power device, including:

[0035] Perform data cleaning on the multi-source data of each power equipment to obtain a pre-processed multi-source data set;

[0036] Inputting the preprocessed multi-source data set into a preset data structure standardization model; wherein the preset data structure standardization model is obtained by binding a predefined energy consumption data structure, a device data structure, and an environmental parameter data structure to a scaling window established based on a sliding window algorithm, and the scaling window is used to convert the format of the input information according to the bound parameters;

[0037] Output multi-source data with unified data structure for each power device corresponding to multi-source standardized data;

[0038] Multi-source data with unified data structure are aligned in time and space to obtain multi-source standardized data of each power equipment.

[0039] Optionally, generate energy consumption analysis results for each power device based on multi-source standardized data, including:

[0040] Obtain predefined energy efficiency standard values; wherein the predefined energy efficiency standard values ​​include standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity;

[0041] Determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data;

[0042] By comparing the deviation values ​​between the standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment, the energy consumption analysis results of each power equipment are obtained.

[0043] Optionally, generate energy consumption analysis results for each power device based on multi-source standardized data, including:

[0044] Obtain energy consumption data of each power equipment in the historical period;

[0045] Based on the energy consumption data of each power equipment, a regression model is established;

[0046] Based on the regression model, the baseline energy consumption within the preset period is predicted;

[0047] Determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data;

[0048] Determine the deviation value between the benchmark energy consumption and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity as the energy consumption analysis result of each power equipment.

[0049] Optionally, the multi-source standardized data includes power identifier-power standard value, power identifier-power standard value, device status identifier-device status standard value, operating time identifier-operating time standard value, load rate identifier-load rate standard value, temperature identifier-temperature standard value, and humidity identifier-humidity standard value;

[0050] Based on standardized data from multiple sources, the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment are determined, including:

[0051] Determine the power identification, power identification, equipment status identification, operating time identification, load rate identification, temperature identification, and humidity identification of each power equipment;

[0052] Based on the power identification, power identification, equipment status identification, operating time identification, load rate identification, temperature identification, and humidity identification of each power device, the power standard value, power standard value, equipment status standard value, operating time standard value, load rate standard value, temperature standard value, and humidity standard value of each power device are obtained from multi-source standardized data;

[0053] When the equipment status standard value indicates that each power device is operating, the actual energy consumption intensity of each power device is calculated based on the operating time standard value, load rate standard value, and power standard value; when the equipment status standard value indicates that each power device is not operating, the actual energy consumption intensity of each power device is recorded as 0;

[0054] ;

[0055] Based on the temperature standard value and the humidity standard value, a comprehensive correction coefficient is calculated; the calculation formula of the comprehensive correction coefficient is:

[0056] ;

[0057] in, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the standard value of temperature, is the standard value of humidity, To preset the optimal operating temperature, Preset optimal operating humidity, is the temperature coefficient, is the humidity coefficient;

[0058] Calculating the actual equipment efficiency of each of the power devices according to the power standard value, the comprehensive correction coefficient and the preset theoretical maximum value;

[0059] ;

[0060] Calculating the actual energy utilization rate of each power device according to the standard value of power quantity and the standard value of load rate;

[0061] ;

[0062] in, is the standard value of electricity. is the fuel consumption, For equipment efficiency, , is the standard value of load rate;

[0063] Calculating the actual carbon emission intensity of each power device according to the load rate standard value;

[0064] ;

[0065] in, is the transmission and distribution loss correction factor, , the loss rate is 6%-8%.

[0066] In a second aspect, an embodiment of the present application provides an energy efficiency analysis device based on multi-source data, the device comprising:

[0067] A multi-source data acquisition module is used to acquire power consumption data, equipment operating parameters, and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device;

[0068] The data standardization module is used to perform data modeling and standardization on the multi-source data of each power device to obtain the multi-source standardized data of each power device;

[0069] Energy consumption analysis module, used to generate energy consumption analysis results of each power equipment based on multi-source standardized data;

[0070] The energy consumption data display module is used to visualize the energy consumption analysis results of each power equipment, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

[0071] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0072] In an embodiment of the present application, on the one hand, data modeling and standardization are performed on the multi-source data of each power device to obtain multi-source standardized data of each power device. Since the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, so that energy efficiency analysis can be carried out based on a unified and standardized data foundation, eliminating data silos, and not only covering power energy consumption data, but also comprehensively considering equipment operating parameters and equipment environmental parameters, thereby improving the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the visual display of the dynamic dashboard can reflect the dynamic changes in energy consumption in real time. Managers can view the real-time energy flow diagram and equipment energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste in the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined energy management and optimized control.

[0073] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0075] Figure 1 This is a schematic diagram of a method flow of an energy efficiency analysis method based on multi-source data provided by an embodiment of the present application;

[0076] Figure 2 This is a schematic diagram of a model architecture provided by an embodiment of the present application, which is pre-set with a keyword recognition model;

[0077] Figure 3 This is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0078] Figure 4 This is a schematic diagram of a client display result provided by an embodiment of the present application;

[0079] Figure 5 This is another schematic diagram of client display results provided in an embodiment of the present application;

[0080] Figure 6 This is an energy flow diagram provided by an embodiment of the present application;

[0081] Figure 7 This is a schematic diagram of the structure of an energy efficiency analysis device based on multi-source data provided by an embodiment of the present application;

[0082] Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] The following description and the drawings sufficiently illustrate specific embodiments of the application to enable those skilled in the art to practice them.

[0084] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0085] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0086] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of this application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0087] Currently, energy efficiency analysis mainly uses the traditional energy efficiency analysis system (EMS).

[0088] The inventors recognized that, on the one hand, the system's decentralized software architecture prevented effective data integration across disparate systems. For example, power consumption data, power equipment operating parameters, and equipment environmental parameters were often collected and stored in separate systems by different components, making unified management and analysis difficult. This data silo phenomenon limited the comprehensiveness and accuracy of energy efficiency analysis. Furthermore, the system's inability to reflect dynamic changes in energy consumption in real time prevented managers from promptly identifying energy waste or anomalies, hindering their ability to implement energy optimization measures.

[0089] In order to solve the above problems, the present application provides an energy efficiency analysis method and device based on multi-source data to solve the problems existing in the above-mentioned related technical problems. In an embodiment of the present application, on the one hand, data modeling and standardization processing are performed on the multi-source data of each power device to obtain multi-source standardized data of each power device. Because the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, so that energy efficiency analysis can be carried out based on a unified and standardized data foundation, eliminating data silos, and not only covering power energy consumption data, but also comprehensively considering equipment operating parameters and equipment environmental parameters, thereby improving the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the visual display of the dynamic dashboard can reflect the dynamic changes in energy consumption in real time. Managers can view the real-time energy flow diagram and equipment energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste in the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined energy management and optimized control. The following uses an exemplary embodiment to explain in detail.

[0090] The following will be combined with the Figure 1 -Attached Figure 6This article details the multi-source data-based energy efficiency analysis method provided in the embodiments of this application. This method can be implemented using a computer program and run on a multi-source data-based energy efficiency analysis device based on the von Neumann architecture. This computer program can be integrated into an application or run as a standalone tool application.

[0091] See Figure 1 , provides a flow chart of an energy efficiency analysis method based on multi-source data for an embodiment of the present application, which is applied to the server. Figure 1 As shown, the method of the embodiment of the present application includes the following steps:

[0092] S101, acquiring power consumption data, equipment operating parameters, and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device;

[0093] The preset period refers to a pre-set time interval for periodic data collection. The length of the period can be adjusted based on actual needs and application scenarios, such as hourly, daily, or weekly. The target area refers to the specific geographic area or facility area where data collection and analysis is required, such as a factory floor, an office building, or a data center. Power equipment refers to equipment used to generate, transmit, distribute, and use electrical energy, such as generators, transformers, distribution cabinets, motors, and lighting equipment.

[0094] In some embodiments of the present application, during the energy efficiency analysis based on multi-source data, the server will calculate in real time the duration between the current moment and the end moment of the previous analysis. When the duration is equal to the preset period, the server will obtain the power consumption data, equipment operating parameters and equipment environmental parameters of each power equipment in the target area to obtain multi-source data of each power equipment.

[0095] For example, identify the area where data needs to be collected (such as a factory or office building) and all relevant electrical equipment within the area (such as transformers, motors, and lighting systems). Install smart meters or energy consumption monitoring devices and connect them to the circuits of each electrical device. Smart meters can measure parameters such as the device's power consumption and power factor in real time. Install sensors (such as current sensors, voltage sensors, and power sensors) on the equipment to monitor its operating parameters, such as its status, operating time, and load factor, in real time. Install environmental sensors (such as temperature and humidity sensors and pressure sensors) around the equipment. These sensors can monitor the temperature and humidity of the equipment's operating environment in real time.

[0096] S102, performing data modeling and standardization processing on multi-source data of each power device to obtain multi-source standardized data of each power device;

[0097] In some embodiments of the present application, data modeling and standardization are performed on the multi-source data of each power device to obtain the multi-source standardized data of each power device. The process specifically includes: defining the entity relationship data structures corresponding to the power energy consumption data, equipment operating parameters and equipment environmental parameters, and obtaining the energy consumption data structure, equipment data structure and environmental parameter data structure; mapping the power energy consumption data, power equipment operating parameters and equipment environmental parameters of each power device to the energy consumption data structure, equipment data structure and environmental parameter data structure, respectively, and obtaining multi-source data with a unified data structure for each power device; and time-aligning the multi-source data with a unified data structure to obtain multi-source standardized data for each power device.

[0098] Among them, the entity-relationship data structure is a structured framework used to describe various entities in the data (such as power consumption data, equipment operating parameters, and equipment environmental parameters). It helps organize and manage complex data by defining the attributes and structure of the entities. The energy consumption data structure is used to store and manage the data structure of power consumption data, which usually includes attributes such as power identification and power identification. The device data structure is used to store and manage the data structure of equipment operating parameters, including attributes such as equipment status identification, operating time identification, and load rate identification. The environmental parameter data structure is used to store and manage the data structure of equipment environmental parameters, which usually includes attributes such as temperature identification and humidity identification. Time alignment is to adjust data with different timestamps to the same time base to ensure the consistency of data in the time dimension.

[0099] In the embodiment of the present application, by defining the entity relationship data structure of power consumption data, equipment operating parameters, and equipment environmental parameters, and mapping these data to the corresponding energy consumption data structure, equipment data structure, and environmental parameter data structure, the structural unification of multi-source data is achieved. Further, by time-aligning the data, multi-source standardized data is obtained. This process not only standardizes the data format, but also eliminates the problems caused by data source and time differences, providing a high-quality, unified data foundation for subsequent energy consumption analysis, thereby improving the accuracy and reliability of energy efficiency analysis.

[0100] Among them, the energy consumption data structure includes power identification and power identification; the equipment data structure includes equipment status identification, operating time identification and load rate identification; the environmental parameter data structure includes temperature identification and humidity identification.

[0101] In some embodiments of the present application, the power energy consumption data, power equipment operating parameters and equipment environmental parameters of each power equipment are mapped to the energy consumption data structure, the equipment data structure and the environmental parameter data structure respectively, and the specific process of obtaining multi-source data with a unified data structure includes: extracting descriptive information that matches the power identifier and the power identifier from the power energy consumption data of each power equipment; extracting descriptive information that matches the equipment status identifier, the operating time identifier and the load rate identifier from the power equipment operating parameters of each power equipment; extracting descriptive information that matches the temperature identifier and the humidity identifier from the equipment environmental parameters of each power equipment; identifying multiple entity keywords of each extracted descriptive information, and performing entity screening on the multiple entity keywords of each descriptive information to generate attribute values ​​of each descriptive information; converting the attribute values ​​of each descriptive information into the format indicated by the identifier corresponding to each descriptive information to obtain the standard value of each descriptive information; establishing a mapping relationship between the identifier corresponding to each descriptive information and the standard value of each descriptive information to obtain multi-source data with a unified data structure.

[0102] Among them, for example Figure 2 As shown in the figure, a keyword spotting model is pre-configured. The keyword spotting model includes multiple text encoders, an attention network with an attention mechanism, a neural network for labeling and segmenting sequence data, and a keyword output layer. The attention network has 8 attention heads.

[0103] In some embodiments of the present application, the specific process of identifying multiple entity keywords for each extracted descriptive information includes: inputting each extracted descriptive information into multiple text encoders in sequence to convert each descriptive information into a vector representation of a fixed dimension to obtain multiple entity vectors; inputting the multiple entity vectors into an attention network to output word vectors with an attention mechanism; inputting the word vectors into a neural network for labeling and segmenting sequence data to output a keyword label sequence; mapping the keyword label sequence to an entity label through the output layer to obtain multiple entity keywords for each descriptive information.

[0104] A text encoder is a neural network structure used to convert text data into a fixed-dimensional vector representation. Common text encoders include recurrent neural networks. Attention networks are used to dynamically assign weights when processing data, highlighting important information and ignoring irrelevant information. Neural networks used to label and segment sequence data (such as text) are typically used for labeling (such as named entity recognition) and segmentation (such as sentence segmentation).

[0105] For example, each description is converted into a fixed-dimensional vector representation, resulting in multiple entity vectors that retain the semantic characteristics of the description. The attention network outputs word vectors that are assigned attention mechanisms. These word vectors highlight the important parts of the description while ignoring irrelevant information. The neural network outputs a sequence of keyword tags that indicates which words or phrases are keywords.

[0106] In some embodiments of the present application, the specific process of screening multiple entity keywords for each descriptive information and generating attribute values ​​for each descriptive information includes: traversing the target entity keyword from the multiple entity keywords of each descriptive information; calculating the similarity between the target entity keyword and other entity keywords in the multiple entity keywords except the target entity keyword; screening the entity keyword with the largest attribute value from the entity keywords whose similarity is greater than a preset threshold; and using the attribute value of the entity keyword with the largest attribute value as the attribute value of each descriptive information.

[0107] In other embodiments of the present application, data modeling and standardization are performed on the multi-source data of each power device to obtain the multi-source standardized data of each power device. The specific process includes: performing data cleaning on the multi-source data of each power device to obtain a pre-processed multi-source data set; inputting the pre-processed multi-source data set into a preset data structure standardization model; wherein the preset data structure standardization model is obtained by binding a pre-defined energy consumption data structure, a device data structure, and an environmental parameter data structure to a scale window established based on a sliding window algorithm, and the scale window is used to convert the format of the input information according to the bound parameters; outputting the multi-source data with a unified data structure of each power device corresponding to the multi-source standardized data; performing spatiotemporal alignment on the multi-source data with a unified data structure to obtain the multi-source standardized data of each power device.

[0108] In the embodiments of the present application, by binding the energy consumption data structure, device data structure, and environmental parameter data structure to a scale window established based on a sliding window algorithm, the speed and efficiency of data processing can be significantly improved. Data cleaning removes noise and invalid information, reducing the processing burden; the unified data structure simplifies the processing flow and avoids the complex conversion caused by inconsistent formats; the high efficiency of the sliding window algorithm further accelerates the speed of data format conversion; and spatiotemporal alignment ensures the consistency of data in time and space dimensions, providing high-quality data support for subsequent analysis.

[0109] S103, generating energy consumption analysis results of each power device based on multi-source standardized data;

[0110] In some embodiments of the present application, the specific process of generating energy consumption analysis results of each power equipment based on multi-source standardized data includes: obtaining predefined energy efficiency standard values; wherein the predefined energy efficiency standard values ​​include standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity; determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data; comparing the deviation values ​​between the standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment to obtain the energy consumption analysis results of each power equipment.

[0111] In one possible implementation, the standard energy intensity is, for example, 50 kWh / ton. The standard equipment efficiency is, for example, 90%. The standard energy utilization rate is, for example, 85%. The standard carbon emission intensity is, for example, 0.5 kg CO2 / kWh. At this point, actual operating data for a motor is obtained: actual energy intensity: 60 kWh / ton. Actual equipment efficiency: 88%. Actual energy utilization rate: 82%. Actual carbon emission intensity: 0.6 kg CO2 / kWh. Comparing the standard and actual values, the deviation is calculated: Energy intensity deviation: Actual value 60 kWh / ton - Standard value 50 kWh / ton = +10 kWh / ton. Equipment efficiency deviation: Actual value 88% - Standard value 90% = -2%. Energy utilization rate deviation: Actual value 82% - Standard value 85% = -3%. Carbon emission intensity deviation: Actual value 0.6 kg CO2 / kWh - Standard value 0.5 kg CO2 / kWh = +0.1 kg CO2 / kWh. Analysis shows that the motor's actual energy consumption intensity exceeds the standard by 10 kWh / ton, indicating high energy consumption and potential for energy savings. The actual equipment efficiency is 2% below the standard, indicating energy losses during energy conversion. The actual energy utilization rate is 3% below the standard, indicating that the equipment is not fully utilizing input energy during operation. The actual carbon emission intensity is 0.1 kg CO2 / kWh above the standard, indicating that the equipment has a high carbon emission level and a significant environmental impact.

[0112] In other embodiments of the present application, the specific process of generating energy consumption analysis results of each power equipment based on multi-source standardized data includes: obtaining energy consumption data of each power equipment within a historical time period; establishing a regression model based on the energy consumption data of each power equipment; predicting the baseline energy consumption within a preset period based on the regression model; determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data; and determining the deviation value between the baseline energy consumption and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity as the energy consumption analysis result of each power equipment.

[0113] Among them, multi-source standardized data includes power identifier-power standard value, power identifier-power standard value, equipment status identifier-equipment status standard value, operating time identifier-operating time standard value, load rate identifier-load rate standard value, temperature identifier-temperature standard value, and humidity identifier-humidity standard value.

[0114] In some embodiments of the present application, the specific process of determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device based on multi-source standardized data includes: determining the power identifier, power identifier, equipment status identifier, operating time identifier, load rate identifier, temperature identifier, and humidity identifier of each power device; based on the power identifier, power identifier, equipment status identifier, operating time identifier, load rate identifier, temperature identifier, and humidity identifier of each power device, obtaining the power standard value, power standard value, equipment status standard value, operating time standard value, load rate standard value, temperature standard value, and humidity standard value of each power device from the multi-source standardized data; when the equipment status standard value indicates that each power device is operating, calculating the actual energy consumption intensity of each power device based on the operating time standard value, load rate standard value, and power standard value; when the equipment status standard value indicates that each power device is not operating, recording the actual energy consumption intensity of each power device as 0;

[0115] ;

[0116] Based on the temperature standard value and the humidity standard value, a comprehensive correction coefficient is calculated; the calculation formula of the comprehensive correction coefficient is:

[0117] ;

[0118] in, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the standard value of temperature, is the standard value of humidity, To preset the optimal operating temperature, Preset optimal operating humidity, is the temperature coefficient, is the humidity coefficient;

[0119] Calculating the actual equipment efficiency of each of the power devices according to the power standard value, the comprehensive correction coefficient and the preset theoretical maximum value;

[0120] ;

[0121] Calculating the actual energy utilization rate of each power device according to the standard value of power quantity and the standard value of load rate;

[0122] ;

[0123] in, is the standard value of electricity. is the fuel consumption, For equipment efficiency, , is the standard value of load rate;

[0124] Calculating the actual carbon emission intensity of each power device according to the load rate standard value;

[0125] ;

[0126] in, is the transmission and distribution loss correction factor, , the loss rate is 6%-8%.

[0127] S104 , visualize the energy consumption analysis results of each power device, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

[0128] Visualization is the process of displaying data in the form of graphs, charts, or other visual formats to facilitate a more intuitive understanding of the data's meaning and trends. Energy consumption analysis results are obtained by analyzing the energy consumption data of power equipment, including indicators such as energy intensity, equipment efficiency, energy utilization, and carbon emission intensity. A dynamic dashboard is a real-time updated visualization interface used to display changes in key indicators and data. A real-time energy flow diagram is a dynamic chart that displays the flow of electricity between devices, including input, output, and loss. Equipment energy efficiency rankings rank devices based on their energy efficiency indicators (such as energy intensity and equipment efficiency).

[0129] In some embodiments of the present application, data visualization tools (such as Tableau, Power BI, or custom web applications) are used to convert analysis results into graphs and charts. Display the flow of electricity between various devices, including input, output, and loss. Sort the devices according to their energy efficiency indicators, and display high-efficiency and low-efficiency devices. Display the flow of electricity from transformers to motors and lighting systems. Use dynamic arrows and color coding to indicate the direction and intensity of power flow. Sort the devices by energy efficiency indicators (such as device efficiency) and use bar charts or lists to display the rankings. High-efficiency devices are represented by green, and low-efficiency devices are represented by red. Deploy the dynamic dashboard to a web server and display it to managers through a browser or a dedicated client application. Managers can view the dynamic dashboard in real time to understand the energy consumption and energy efficiency ranking of the equipment.

[0130] For example Figure 3 As shown, the server collects relevant data of each power device in the area, and after processing, it can be sent to the client for display. The relevant results displayed are as follows: Figure 4 、 Figure 5 、 Figure 6 shown.

[0131] In an embodiment of the present application, on the one hand, data modeling and standardization are performed on the multi-source data of each power device to obtain multi-source standardized data of each power device. Since the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, so that energy efficiency analysis can be carried out based on a unified and standardized data foundation, eliminating data silos, and not only covering power energy consumption data, but also comprehensively considering equipment operating parameters and equipment environmental parameters, thereby improving the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the visual display of the dynamic dashboard can reflect the dynamic changes in energy consumption in real time. Managers can view the real-time energy flow diagram and equipment energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste in the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined energy management and optimized control.

[0132] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0133] See Figure 7, which shows a schematic diagram of the structure of an energy efficiency analysis device based on multi-source data, provided by an exemplary embodiment of the present application. This energy efficiency analysis device based on multi-source data can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The device 1 includes a multi-source data acquisition module 10, a data standardization module 20, an energy consumption analysis module 30, and an energy consumption data display module 40.

[0134] The multi-source data acquisition module 10 is used to acquire the power consumption data, equipment operating parameters and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device;

[0135] The data standardization module 20 is used to perform data modeling and standardization processing on the multi-source data of each power device to obtain the multi-source standardized data of each power device;

[0136] Energy consumption analysis module 30, used to generate energy consumption analysis results of each power device based on multi-source standardized data;

[0137] The energy consumption data display module 40 is used to visualize the energy consumption analysis results of each power device, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

[0138] It should be noted that the multi-source data-based energy efficiency analysis device provided in the above embodiment, when executing the multi-source data-based energy efficiency analysis method, only uses the division of the above-mentioned functional modules as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the multi-source data-based energy efficiency analysis device provided in the above embodiment and the multi-source data-based energy efficiency analysis method embodiment are based on the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0139] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0140] In an embodiment of the present application, on the one hand, data modeling and standardization are performed on the multi-source data of each power device to obtain multi-source standardized data of each power device. Since the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, so that energy efficiency analysis can be carried out based on a unified and standardized data foundation, eliminating data silos, and not only covering power energy consumption data, but also comprehensively considering equipment operating parameters and equipment environmental parameters, thereby improving the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the visual display of the dynamic dashboard can reflect the dynamic changes in energy consumption in real time. Managers can view the real-time energy flow diagram and equipment energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste in the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined energy management and optimized control.

[0141] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implements the energy efficiency analysis method based on multi-source data provided by the above-mentioned various method embodiments.

[0142] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the energy efficiency analysis method based on multi-source data of each of the above method embodiments.

[0143] See Figure 8 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0144] The communication bus 1002 is used to implement the connection and communication between these components.

[0145] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0146] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0147] The processor 1001 may include one or more processing cores. The processor 1001 utilizes various interfaces and circuits to connect various components within the electronic device 1000. It executes instructions, programs, code sets, or instruction sets stored in the memory 1005, and accesses data stored in the memory 1005 to perform various functions and process data within the electronic device 1000. Optionally, the processor 1001 may be implemented in hardware using at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 1001 and implemented on a separate chip.

[0148] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 8 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an energy efficiency analysis application based on multi-source data.

[0149] exist Figure 8In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain user input data; and the processor 1001 can be used to call the energy efficiency analysis application based on multi-source data stored in the memory 1005 and specifically perform the following operations:

[0150] Acquire power consumption data, equipment operating parameters, and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device;

[0151] Perform data modeling and standardization on the multi-source data of each power equipment to obtain multi-source standardized data of each power equipment;

[0152] Generate energy consumption analysis results for each power device based on multi-source standardized data;

[0153] Visualize the energy consumption analysis results of each power device, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

[0154] In one embodiment, when the processor 1001 performs data modeling and standardization processing on the multi-source data of each power device to obtain the multi-source standardized data of each power device, the processor 1001 specifically performs the following operations:

[0155] Define the entity relationship data structures corresponding to power energy consumption data, equipment operating parameters, and equipment environmental parameters, and obtain the energy consumption data structure, equipment data structure, and environmental parameter data structure;

[0156] The power consumption data, power equipment operating parameters and equipment environmental parameters of each power device are mapped to the energy consumption data structure, the equipment data structure and the environmental parameter data structure respectively, so as to obtain multi-source data with a unified data structure for each power device;

[0157] Time alignment is performed on multi-source data with unified data structure to obtain multi-source standardized data of each power equipment.

[0158] In one embodiment, when the processor 1001 executes mapping of the power consumption data, power equipment operating parameters, and equipment environmental parameters of each power device to the energy consumption data structure, the equipment data structure, and the environmental parameter data structure, respectively, to obtain multi-source data with a unified data structure, the processor 1001 specifically performs the following operations:

[0159] Extracting descriptive information matching the power identifier and the power identifier from the power consumption data of each power device;

[0160] Extracting descriptive information matching the device status identifier, the operating time identifier, and the load rate identifier from the power device operating parameters of each power device;

[0161] Extracting descriptive information matching the temperature identifier and humidity identifier from the equipment environmental parameters of each power equipment;

[0162] Identify multiple entity keywords of each extracted description information, perform entity screening on the multiple entity keywords of each description information, and generate attribute values ​​of each description information;

[0163] Convert the attribute value of each descriptive information into a format according to the data format indicated by the identifier corresponding to each descriptive information to obtain a standard value of each descriptive information;

[0164] A mapping relationship between the identifier corresponding to each descriptive information and the standard value of each descriptive information is established to obtain multi-source data with a unified data structure.

[0165] In one embodiment, when the processor 1001 identifies multiple entity keywords of each extracted description information, it specifically performs the following operations:

[0166] Each extracted description information is sequentially input into multiple text encoders to convert each description information into a vector representation of a fixed dimension to obtain multiple entity vectors;

[0167] Input multiple entity vectors into the attention network and output the word vector given the attention mechanism;

[0168] Input the word vector into the neural network used to label and segment sequence data, and output a sequence of keyword labels;

[0169] The keyword tag sequence is mapped to entity tags through the output layer to obtain multiple entity keywords for each description information.

[0170] In one embodiment, when the processor 1001 filters multiple entity keywords of each description information and generates an attribute value of each description information, the processor 1001 specifically performs the following operations:

[0171] Traverse the target entity keywords from multiple entity keywords of each description information;

[0172] Calculating the similarity between the target entity keyword and other entity keywords among the multiple entity keywords except the target entity keyword;

[0173] Filter out the entity keywords with the largest attribute value from the entity keywords whose similarity is greater than the preset threshold;

[0174] The attribute value of the entity keyword with the largest attribute value is used as the attribute value of each description information.

[0175] In one embodiment, when the processor 1001 performs data modeling and standardization processing on the multi-source data of each power device to obtain the multi-source standardized data of each power device, the processor 1001 specifically performs the following operations:

[0176] Perform data cleaning on the multi-source data of each power equipment to obtain a pre-processed multi-source data set;

[0177] Inputting the preprocessed multi-source data set into a preset data structure standardization model; wherein the preset data structure standardization model is obtained by binding a predefined energy consumption data structure, a device data structure, and an environmental parameter data structure to a scaling window established based on a sliding window algorithm, and the scaling window is used to convert the format of the input information according to the bound parameters;

[0178] Output multi-source data with unified data structure for each power device corresponding to multi-source standardized data;

[0179] Multi-source data with unified data structure are aligned in time and space to obtain multi-source standardized data of each power equipment.

[0180] In one embodiment, when the processor 1001 generates the energy consumption analysis results of each power device based on the multi-source standardized data, the processor 1001 specifically performs the following operations:

[0181] Obtain predefined energy efficiency standard values; wherein the predefined energy efficiency standard values ​​include standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity;

[0182] Determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data;

[0183] By comparing the deviation values ​​between the standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment, the energy consumption analysis results of each power equipment are obtained.

[0184] In one embodiment, when the processor 1001 generates the energy consumption analysis results of each power device based on the multi-source standardized data, the processor 1001 specifically performs the following operations:

[0185] Obtain energy consumption data of each power equipment in the historical period;

[0186] Based on the energy consumption data of each power equipment, a regression model is established;

[0187] Based on the regression model, the baseline energy consumption within the preset period is predicted;

[0188] Determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on multi-source standardized data;

[0189] Determine the deviation value between the benchmark energy consumption and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity as the energy consumption analysis result of each power equipment.

[0190] In an embodiment of the present application, on the one hand, data modeling and standardization are performed on the multi-source data of each power device to obtain multi-source standardized data of each power device. Since the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, so that energy efficiency analysis can be carried out based on a unified and standardized data foundation, eliminating data silos, and not only covering power energy consumption data, but also comprehensively considering equipment operating parameters and equipment environmental parameters, thereby improving the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the visual display of the dynamic dashboard can reflect the dynamic changes in energy consumption in real time. Managers can view the real-time energy flow diagram and equipment energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste in the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined energy management and optimized control.

[0191] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program for energy efficiency analysis based on multi-source data can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium for the program for energy efficiency analysis based on multi-source data can be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0192] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. An energy efficiency analysis method based on multi-source data, characterized in that: Applied to the server, the method includes: Acquire power consumption data, equipment operating parameters, and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device; Performing data modeling and standardization on the multi-source data of each power device to obtain the multi-source standardized data of each power device; wherein, The performing data modeling and standardization on the multi-source data of each power device to obtain the multi-source standardized data of each power device includes: Define the entity relationship data structures corresponding to power energy consumption data, equipment operating parameters, and equipment environmental parameters, and obtain the energy consumption data structure, equipment data structure, and environmental parameter data structure; Mapping the power consumption data, power equipment operating parameters, and equipment environmental parameters of each power device to the energy consumption data structure, the equipment data structure, and the environmental parameter data structure, respectively, to obtain multi-source data with a unified data structure for each power device; Time-aligning the multi-source data with a unified data structure to obtain multi-source standardized data of each power device; The energy consumption data structure includes a power identifier and a power identifier; the device data structure includes a device status identifier, an operating time identifier, and a load rate identifier; and the environmental parameter data structure includes a temperature identifier and a humidity identifier. The power consumption data, power equipment operating parameters, and equipment environmental parameters of each power device are mapped to the energy consumption data structure, the equipment data structure, and the environmental parameter data structure, respectively, to obtain multi-source data with a unified data structure for each power device, including: Extracting descriptive information matching the power identifier and the power identifier from the power energy consumption data of each power device; Extracting descriptive information matching the device status identifier, the operating time identifier, and the load rate identifier from the power device operating parameters of each power device; Extracting descriptive information matching the temperature identifier and the humidity identifier from the device environmental parameters of each power device; Identify multiple entity keywords of each extracted descriptive information, perform entity screening on the multiple entity keywords of each descriptive information, and generate attribute values ​​of each descriptive information; Convert the attribute value of each descriptive information into a format according to the data format indicated by the identifier corresponding to each descriptive information to obtain a standard value of each descriptive information; Establishing a mapping relationship between an identifier corresponding to each descriptive information and a standard value of each descriptive information to obtain multi-source data with a unified data structure for each power device; generating energy consumption analysis results of each of the power devices according to the multi-source standardized data; The energy consumption analysis results of each power device are visualized, and a dynamic dashboard is obtained and sent to the client to display a real-time energy flow diagram and equipment energy efficiency ranking.

2. The method according to claim 1, characterized in that A keyword recognition model is pre-set, comprising a plurality of text encoders, an attention network with an attention mechanism, a neural network for labeling and segmenting sequence data, and a keyword output layer; The identification and extraction of multiple entity keywords for each descriptive information includes: Inputting each extracted description information into the multiple text encoders in sequence to convert each description information into a vector representation of a fixed dimension to obtain multiple entity vectors; Input the multiple entity vectors into the attention network, and output the word vector given the attention mechanism; Inputting the word vector into the neural network for labeling and segmenting sequence data, and outputting a keyword label sequence; The keyword tag sequence is mapped into entity tags through the output layer to obtain multiple entity keywords for each description information.

3. The method according to claim 1, characterized in that The entity screening of the multiple entity keywords of each description information to generate the attribute value of each description information includes: Traversing the target entity keyword from the multiple entity keywords of each description information; Calculating similarities between the target entity keyword and other entity keywords among the multiple entity keywords except the target entity keyword; Filtering out the entity keyword with the largest attribute value from the entity keywords whose similarity is greater than a preset threshold; The attribute value of the entity keyword with the largest attribute value is used as the attribute value of each description information.

4. The method according to claim 1, wherein The performing data modeling and standardization on the multi-source data of each power device to obtain the multi-source standardized data of each power device includes: Performing data cleaning on the multi-source data of each power device to obtain a pre-processed multi-source data set; Inputting the preprocessed multi-source data set into a preset data structure standardization model; wherein the preset data structure standardization model is obtained by binding a predefined energy consumption data structure, a device data structure, and an environmental parameter data structure to a scale window established based on a sliding window algorithm, and the scale window is used to convert the format of the input information according to the bound parameters; Outputting multi-source data with a unified data structure for each power device corresponding to the multi-source standardized data; The multi-source data with a unified data structure are aligned in time and space to obtain the multi-source standardized data of each power device.

5. The method according to claim 1, wherein Generating the energy consumption analysis results of each power device according to the multi-source standardized data includes: Obtain predefined energy efficiency standard values; wherein the predefined energy efficiency standard values ​​include standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity; Determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device based on the multi-source standardized data; Compare the deviation values ​​between the standard energy consumption intensity, standard equipment efficiency, standard energy utilization rate, and standard carbon emission intensity and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment to obtain the energy consumption analysis results of each power equipment.

6. The method according to claim 1, characterized in that Generating the energy consumption analysis results of each power device according to the multi-source standardized data includes: Obtain energy consumption data of each power equipment in the historical period; Establishing a regression model based on the energy consumption data of each power device; Predicting baseline energy consumption within a preset period based on the regression model; Determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device based on the multi-source standardized data; Determine the deviation value between the benchmark energy consumption and the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity as the energy consumption analysis result of each power device.

7. The method according to claim 6, characterized in that The multi-source standardized data includes power identifier-power standard value, power identifier-power standard value, device status identifier-device status standard value, operating time identifier-operating time standard value, load rate identifier-load rate standard value, temperature identifier-temperature standard value, humidity identifier-humidity standard value; Determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device based on the multi-source standardized data includes: Determine the power identification, power identification, device status identification, operating time identification, load rate identification, temperature identification, and humidity identification of each electrical device; Based on the power identifier, power identifier, device status identifier, operating time identifier, load rate identifier, temperature identifier, and humidity identifier of each power device, obtain the power standard value, power standard value, device status standard value, operating time standard value, load rate standard value, temperature standard value, and humidity standard value of each power device from the multi-source standardized data; When the device status standard value indicates that each electrical device is operating, the actual energy consumption intensity of each electrical device is calculated based on the operating time standard value, the load rate standard value, and the power standard value; when the device status standard value indicates that each electrical device is not operating, the actual energy consumption intensity of each electrical device is recorded as 0; ； Based on the temperature standard value and the humidity standard value, a comprehensive correction coefficient is calculated; the calculation formula of the comprehensive correction coefficient is: ； in, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the standard value of temperature, is the standard value of humidity, To preset the optimal operating temperature, Preset optimal operating humidity, is the temperature coefficient, is the humidity coefficient; Calculating the actual equipment efficiency of each of the power devices according to the power standard value, the comprehensive correction coefficient and the preset theoretical maximum value; ； Calculating the actual energy utilization rate of each power device according to the standard value of power quantity and the standard value of load rate; ； in, is the standard value of electricity. is the fuel consumption, For equipment efficiency, , is the standard value of load rate; Calculating the actual carbon emission intensity of each power device according to the load rate standard value; ； in, is the transmission and distribution loss correction factor, , the loss rate is 6%-8%.

8. An energy efficiency analysis system based on multi-source data implemented using the method according to any one of claims 1 to 7, characterized in that: The system comprises: A multi-source data acquisition module is used to acquire power consumption data, equipment operating parameters and equipment environmental parameters of each power device in the target area according to a preset period to obtain multi-source data of each power device; A data standardization module, configured to perform data modeling and standardization processing on the multi-source data of each power device to obtain the multi-source standardized data of each power device; An energy consumption analysis module, configured to generate energy consumption analysis results of each of the power devices based on the multi-source standardized data; The energy consumption data display module is used to visualize the energy consumption analysis results of each power device, obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and equipment energy efficiency ranking.

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