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

Abstract

The invention discloses an energy efficiency analysis method and system based on multi-source data, and a server side method comprises the steps: obtaining the power consumption data, equipment operation parameters and equipment environment parameters of each power equipment in a target region according to a preset period, and obtaining the multi-source data of each power equipment; performing data modeling and standardization processing on the multi-source data of each power device to obtain multi-source standardized data of each power device; generating an energy consumption analysis result of each power device according to the multi-source standardized data; and visualizing the energy consumption analysis result of each power device to obtain a dynamic billboard and sending the dynamic billboard to a client so as to display a real-time energy flow diagram and a device energy efficiency ranking. Therefore, by adopting the embodiment of the invention, unified management and analysis of the data can be realized, and the comprehensiveness and accuracy of energy efficiency analysis are improved. And meanwhile, the dynamic change of energy consumption can be reflected in real time, so that a manager can find energy waste or abnormal conditions in time, and energy optimization measures can be taken in time.

Description

Technical Field

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

[0002] In modern industrial production and operation, energy efficiency analysis is a key link to achieve efficient operation, reduce costs, and meet environmental protection requirements. For example, industrial enterprises need to accurately monitor and optimize energy consumption to reduce production costs while meeting increasingly strict carbon emission standards.

[0003] In related technologies, energy efficiency analysis mainly uses traditional energy efficiency management systems (EMS).

[0004] However, on the one hand, the system is implemented using a decentralized software architecture, resulting in ineffective integration of data between different systems. For example, power consumption data, power equipment operation parameters, and equipment environment parameters are often collected by different components and stored in different systems, making it difficult to achieve unified management and analysis. This data island phenomenon limits the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the system cannot reflect the dynamic changes in energy consumption in real time, preventing managers from promptly discovering energy waste or abnormal situations and thus unable to take timely energy optimization measures. Summary of the Invention

[0005] Embodiments of this application provide an energy efficiency analysis method and device based on multi-source data. To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary section is not a comprehensive review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the subsequent detailed description.

[0006] In a first aspect, embodiments of this application provide an energy efficiency analysis method based on multi-source data, which is applied to a server. The method includes: Obtaining power consumption data, equipment operation parameters, and equipment environment parameters of each power equipment in a target area according to a preset period to obtain multi-source data of each power equipment; Performing data modeling and standardization processing on the multi-source data of each power equipment to obtain multi-source standardized data of each power equipment; Generating an energy consumption analysis result for each power equipment according to the multi-source standardized data; Visualizing the energy consumption analysis results of each power equipment to obtain a dynamic dashboard and sending it to the client to display a real-time energy flow diagram and equipment energy efficiency ranking.

[0007] Optionally, performing data modeling and standardization processing on the multi-source data of each power equipment to obtain multi-source standardized data of each power equipment includes: Define the entity relationship data structures corresponding to the power consumption data, device operation parameters, and device environment parameters respectively, to obtain the energy consumption data structure, device data structure, and environment parameter data structure; Map the power consumption data, power device operation parameters, and device environment parameters of each power device to the energy consumption data structure, device data structure, and environment parameter data structure respectively, to obtain multi-source data with unified data structures for each power device; Perform time alignment on the multi-source data with unified data structures to obtain multi-source standardized data for each power device.

[0008] Optionally, the energy consumption data structure includes a power identifier and an electricity quantity identifier; the device data structure includes a device status identifier, an operation time identifier, and a load rate identifier; the environment parameter data structure includes a temperature identifier and a humidity identifier; Map the power consumption data, power device operation parameters, and device environment parameters of each power device to the energy consumption data structure, device data structure, and environment parameter data structure respectively, to obtain multi-source data with unified data structures, including: Extract the descriptive information that matches the power identifier and the electricity quantity identifier from the power consumption data of each power device; Extract the descriptive information that matches the device status identifier, the operation time identifier, and the load rate identifier from the power device operation parameters of each power device; Extract the descriptive information that matches the temperature identifier and the humidity identifier from the device environment parameters of each power device; Identify multiple entity keywords of each extracted descriptive information, and perform entity screening on the multiple entity keywords of each descriptive information to generate the attribute values of each descriptive information; Convert the attribute values of each descriptive information into standard values according to the data format indicated by the identifier corresponding to each descriptive information; Establish 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 unified data structures.

[0009] Optionally, a keyword recognition model is preset, and the keyword recognition model includes multiple text encoders, an attention network with an attention mechanism, a neural network for annotating and segmenting sequence data, and a keyword output layer; Identify multiple entity keywords of each extracted descriptive information, including: Input each extracted descriptive information into multiple text encoders in turn to convert each descriptive 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 vectors given the attention mechanism; Input the word vectors into a neural network for annotating and segmenting sequence data, and output a sequence of keyword tags; Map the sequence of keyword tags to entity tags through an output layer to obtain multiple entity keywords for each description information.

[0010] Optionally, screen the multiple entity keywords for each description information to generate an attribute value for each description information, including: Traverse the target entity keywords from the multiple entity keywords of each description information; Calculate the similarity between the target entity keywords and other entity keywords except the target entity keywords among the multiple entity keywords; From the entity keywords with a similarity greater than a preset threshold, screen out the entity keyword with the largest attribute value; Use the attribute value of the entity keyword with the largest attribute value as the attribute value of each description information.

[0011] Optionally, perform data modeling and standardization processing on the multi-source data of each power device to obtain multi-source standardized data for each power device, including: Perform data cleaning on the multi-source data of each power device to obtain a preprocessed multi-source data set; Input 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, device data structure, and environmental parameter data structure to a scale window established based on a sliding window algorithm, and the scale window is used to perform format conversion on the input information according to the bound parameters; Output multi-source data with a unified data structure for each power device corresponding to the multi-source standardized data; Perform spatio-temporal alignment on the multi-source data with a unified data structure to obtain multi-source standardized data for each power device.

[0012] Optionally, generate an energy consumption analysis result for each power device according to the multi-source standardized data, including: Obtain predefined energy efficiency standard values; wherein, the predefined energy efficiency standard values include standard energy consumption intensity, standard device efficiency, standard energy utilization rate, and standard carbon emission intensity; Determine the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device according to the multi-source standardized data; Compare the deviation values between the standard energy consumption intensity, standard device efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device to obtain the energy consumption analysis result of each power device.

[0013] Optionally, based on the multi-source standardized data, generate the energy consumption analysis results of each power equipment, including: Obtain the energy consumption data of each power equipment within the historical time period; Based on the energy consumption data of each power equipment, establish a regression model; According to the regression model, predict the benchmark energy consumption within the preset period; According to the multi-source standardized data, determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment; Determine the deviation values 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 results of each power equipment.

[0014] Optionally, the multi-source standardized data includes power identification - power standard value, electricity quantity identification - electricity quantity standard value, equipment status identification - equipment status standard value, operating time identification - operating time standard value, load rate identification - load rate standard value, temperature identification - temperature standard value, humidity identification - humidity standard value; According to the multi-source standardized data, determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment, including: Determine the power identification, electricity quantity identification, equipment status identification, operating time identification, load rate identification, temperature identification, and humidity identification of each power equipment; Based on the power identification, electricity quantity identification, equipment status identification, operating time identification, load rate identification, temperature identification, and humidity identification of each power equipment, obtain the power standard value, electricity quantity standard value, equipment status standard value, operating time standard value, load rate standard value, temperature standard value, and humidity standard value of each power equipment from the multi-source standardized data; When the equipment status standard value indicates that each power equipment is operating, calculate the actual energy consumption intensity of each power equipment according to the operating time standard value, load rate standard value, and electricity quantity standard value; when the equipment status standard value indicates that each power equipment is not operating, record the actual energy consumption intensity of each power equipment as 0; ; Based on the temperature standard value and the humidity standard value, calculate the comprehensive correction coefficient; the calculation formula of the comprehensive correction coefficient is: ; Wherein, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the temperature standard value, is the humidity standard value, is the preset optimal operating temperature, The preset optimal operating humidity is the temperature coefficient and is the humidity coefficient; Calculate the actual equipment efficiency of each power equipment according to the power standard value, the comprehensive correction coefficient, and the preset theoretical maximum value; ; Calculate the actual energy utilization rate of each power equipment according to the electricity quantity standard value and the load rate standard value; ; wherein is the electricity quantity standard value is the fuel consumption is the equipment efficiency , is the load rate standard value; Calculate the actual carbon emission intensity of each power equipment according to the load rate standard value; ; wherein is the transmission and distribution loss correction coefficient , and the loss rate is 6% - 8%.

[0015] In a second aspect, an energy efficiency analysis device based on multi-source data provided by an embodiment of the present application includes: A multi-source data acquisition module, configured to acquire power consumption data, equipment operation parameters, and equipment environment parameters of each power equipment in a target area according to a preset period, so as to obtain multi-source data of each power equipment; A data standardization module, configured to perform data modeling and standardization processing on the multi-source data of each power equipment to obtain multi-source standardized data of each power equipment; An energy consumption analysis module, configured to generate an energy consumption analysis result of each power equipment according to the multi-source standardized data; An energy consumption data display module, configured to visualize the energy consumption analysis results of each power equipment, obtain a dynamic dashboard, and send it to a client to display a real-time energy flow diagram and equipment energy efficiency ranking.

[0016] The technical solution provided by the embodiment of the present application may include the following beneficial effects: In the embodiments of the present application, on the one hand, multi-source data of each power device is subjected to data modeling and standardization processing 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, enabling energy efficiency analysis to be carried out based on a unified and standardized data foundation, eliminating data islands, covering not only power consumption data but also comprehensively considering device operation parameters and device environment 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 device energy efficiency rankings at any time through the client, promptly discover abnormal fluctuations or energy waste phenomena during the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and achieving refined management and optimized control of energy.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0019] Figure 1 It is a schematic flowchart of a method for energy efficiency analysis based on multi-source data provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the model architecture of a keyword recognition model preset by an embodiment of the present application; Figure 3 It is a schematic diagram of an application scenario provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the display result of a client provided by an embodiment of the present application; Figure 5 It is another schematic diagram of the display result of a client provided by an embodiment of the present application; Figure 6 It is an energy flow diagram provided by an embodiment of the present application; Figure 7 It 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; Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following description and the accompanying drawings fully illustrate the specific embodiments of the present application, enabling those skilled in the art to practice them.

[0021] It should be clear that the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0022] 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. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0023] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. In addition, in the description of the present application, unless otherwise specified, "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0024] Currently, energy efficiency analysis mainly uses traditional energy efficiency analysis systems (EMS).

[0025] The inventors have realized that, on the one hand, the system is implemented using a decentralized software architecture, resulting in the inability to effectively integrate data between different systems. For example, power consumption data, power equipment operation parameters, and equipment environment parameters are often collected by different components and stored in different systems, making it difficult to achieve unified management and analysis. This data island phenomenon limits the comprehensiveness and accuracy of energy efficiency analysis. On the other hand, the system cannot reflect the dynamic changes in energy consumption in real time, making it impossible for managers to timely discover energy waste or abnormal situations, and thus unable to take timely energy optimization measures.

[0026] To solve the above problems, the present application provides an energy efficiency analysis method and device based on multi-source data to address the issues existing in the above 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 the multi-source standardized data of each power device. Since the data has undergone modeling and standardization processing, the differences and inconsistencies between data from different sources are eliminated, enabling energy efficiency analysis to be carried out based on a unified and standardized data foundation, eliminating data islands, covering not only power consumption data but also comprehensively considering equipment operation parameters and equipment environment parameters, thereby enhancing 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 phenomena during the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and achieving refined management and optimal control of energy. The following will be described in detail using exemplary embodiments.

[0027] The following will be combined with the attached Figure 1 - attached Figure 6 , to introduce in detail the energy efficiency analysis method based on multi-source data provided by the embodiments of the present application. This method can be implemented relying on a computer program and can run on an energy efficiency analysis device based on multi-source data with a von Neumann architecture. This computer program can be integrated into an application or run as an independent tool-type application.

[0028] Please refer to Figure 1 , which is a schematic flowchart of an energy efficiency analysis method based on multi-source data provided by an embodiment of the present application and is applied to the server. As Figure 1 shown, the method of the embodiment of the present application includes the following steps: S101, obtain the power consumption data, equipment operation parameters, and equipment environment parameters of each power device in the target area according to a preset period to obtain the multi-source data of each power device; Among them, the preset period refers to a preset time interval for regularly collecting data. The length of the period can be adjusted according to actual needs and application scenarios, such as every hour, every day, or every week, etc. The target area refers to a specific geographical range or facility area where data collection and analysis are required, such as a factory workshop, an office building, or a data center, etc. A power device refers to a device used to generate, transmit, distribute, and use electric energy, such as a generator, a transformer, a power distribution cabinet, a motor, a lighting device, etc.

[0029] In some embodiments of the present application, during the process of 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 operation parameters, and equipment environment parameters of each power equipment in the target area, and obtain the multi-source data of each power equipment.

[0030] For example, determine the area where data needs to be collected (such as factories, office buildings, etc.) and all relevant power equipment in the area (such as transformers, motors, lighting systems, etc.). Install smart meters or energy consumption monitoring devices and connect them to the circuits of each power equipment. The smart meter can measure parameters such as the electricity consumption and power factor of the equipment in real time. Install sensors (such as current sensors, voltage sensors, power sensors, etc.) on the equipment. The sensors can monitor the operation parameters of the equipment in real time, such as status, operation time, and load rate. Install environmental sensors (such as temperature and humidity sensors, pressure sensors, etc.) around the equipment. These sensors can monitor the temperature and humidity parameters of the equipment operation environment in real time.

[0031] S102, perform data modeling and standardization processing on the multi-source data of each power equipment to obtain the multi-source standardized data of each power equipment; In some embodiments of the present application, the process of performing data modeling and standardization processing on the multi-source data of each power equipment to obtain the multi-source standardized data of each power equipment specifically includes: defining the entity relationship data structures corresponding to the power consumption data, equipment operation parameters, and equipment environment parameters respectively to obtain the energy consumption data structure, equipment data structure, and environment parameter data structure; mapping the power consumption data, power equipment operation parameters, and equipment environment parameters of each power equipment to the energy consumption data structure, equipment data structure, and environment parameter data structure respectively to obtain the multi-source data with unified data structures for each power equipment; performing time alignment on the multi-source data with unified data structures to obtain the multi-source standardized data of each power equipment.

[0032] Among them, the entity relationship data structure is a structured framework used to describe each entity in the data (such as power consumption data, equipment operation parameters, equipment environment parameters). It helps organize and manage complex data by defining the attributes and structures of the entities. The energy consumption data structure is a data structure used to store and manage power consumption data, usually including attributes such as power identification and electricity consumption identification. The equipment data structure is a data structure used to store and manage equipment operation parameters, including attributes such as equipment status identification, operation time identification, and load rate identification. The environment parameter data structure is a data structure used to store and manage equipment environment parameters, usually including attributes such as temperature identification and humidity identification. Time alignment is to adjust data with different timestamps to the same time reference to ensure the consistency of data in the time dimension.

[0033] In the embodiments of the present application, by defining the entity relationship data structure of power consumption data, device operation parameters, and device environment parameters, and mapping these data to the corresponding energy consumption data structure, device data structure, and environment parameter data structure respectively, the structural unification of multi-source data is achieved. Further, through time alignment processing of the data, multi-source standardized data is obtained. This process not only standardizes the data format but also eliminates the problems caused by data sources and time differences, providing a high-quality and unified data foundation for subsequent energy consumption analysis, thereby improving the accuracy and reliability of energy efficiency analysis.

[0034] Among them, the energy consumption data structure includes a power identifier and an electricity quantity identifier; the device data structure includes a device status identifier, an operation time identifier, and a load rate identifier; the environment parameter data structure includes a temperature identifier and a humidity identifier.

[0035] In some embodiments of the present application, the specific process of mapping the power consumption data, power device operation parameters, and device environment parameters of each power device to the energy consumption data structure, device data structure, and environment parameter data structure respectively to obtain multi-source data with unified data structure includes: extracting description information matching the power identifier and electricity quantity identifier from the power consumption data of each power device; extracting description information matching the device status identifier, operation time identifier, and load rate identifier from the power device operation parameters of each power device; extracting description information matching the temperature identifier and humidity identifier from the device environment parameters of each power device; identifying multiple entity keywords of each extracted description information, and performing entity screening on the multiple entity keywords of each description information to generate an attribute value for each description information; converting the attribute value of each description information into a standard value according to the data format indicated by the identifier corresponding to each description information; establishing a mapping relationship between the identifier corresponding to each description information and the standard value of each description information to obtain multi-source data with unified data structure.

[0036] Among them, for example Figure 2 As shown, a keyword recognition model is preset in advance. The keyword recognition model includes multiple text encoders, an attention network with an attention mechanism, a neural network for annotating and segmenting sequence data, and a keyword output layer. The attention network has 8 attention heads.

[0037] In some embodiments of the present application, the specific process of identifying multiple entity keywords for each extracted description information includes: sequentially inputting each extracted description information into multiple text encoders to convert each description information into a vector representation of a fixed dimension, obtaining 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; and mapping the keyword label sequence through an output layer to an entity label to obtain multiple entity keywords for each description information.

[0038] Among them, the text encoder is a neural network structure used to convert text data into a vector representation of a fixed dimension. Common text encoders include recurrent neural networks. The attention network is used to dynamically allocate weights when processing data, highlighting important information and ignoring irrelevant information. The neural network for labeling and segmenting sequence data is a neural network for processing sequence data (such as text), usually used for tasks such as labeling (such as named entity recognition) and segmentation (such as sentence segmentation).

[0039] For example, each description information is converted into a vector representation of a fixed dimension to obtain multiple entity vectors, and these vectors retain the semantic features of the description information. The attention network outputs word vectors with an attention mechanism, and these word vectors highlight the important parts in the description information while ignoring irrelevant information. The neural network outputs a keyword label sequence, and these label sequences indicate which words or phrases are keywords.

[0040] In some embodiments of the present application, the specific process of screening multiple entity keywords for each description information to generate an attribute value for each description information includes: traversing out target entity keywords from the multiple entity keywords of each description information; calculating the similarity between the target entity keywords and other entity keywords in the multiple entity keywords except the target entity keywords; screening out 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 description information.

[0041] In some other embodiments of the present application, the specific process of data modeling and standardization processing for the multi-source data of each power device to obtain the multi-source standardized data of each power device includes: cleaning the multi-source data of each power device to obtain a preprocessed 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 pre-defined energy consumption data structure, device data structure, and environmental parameter data structure to a scale window established based on the sliding window algorithm, and the scale window is used to perform format conversion on the input information according to the bound parameters; outputting multi-source data with a unified data structure corresponding to the multi-source standardized data; and performing spatio-temporal alignment on the multi-source data with a unified data structure to obtain the multi-source standardized data of each power device.

[0042] 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 the 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 complex conversions caused by inconsistent formats; the high efficiency of the sliding window algorithm further speeds up the data format conversion; and spatio-temporal alignment ensures the consistency of data in the time and space dimensions, providing high-quality data support for subsequent analysis.

[0043] S103, generating an energy consumption analysis result for each power device according to the multi-source standardized data; In some embodiments of the present application, the specific process of generating an energy consumption analysis result for each power device according to the multi-source standardized data includes: obtaining a pre-defined energy efficiency standard value; wherein, the pre-defined energy efficiency standard value includes standard energy consumption intensity, standard device efficiency, standard energy utilization rate, and standard carbon emission intensity; determining the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device according to the multi-source standardized data; and comparing the deviation values between the standard energy consumption intensity, standard device efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device to obtain the energy consumption analysis result of each power device.

[0044] In a possible implementation, the standard energy consumption 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 time, the actual operation data of a certain motor is obtained: the actual energy consumption intensity: 60 kWh / ton. The actual equipment efficiency: 88%. The actual energy utilization rate: 82%. The actual carbon emission intensity: 0.6 kg CO2 / kWh. Comparing the standard values with the actual values, the deviation is calculated as follows: the deviation of energy consumption intensity: actual value 60 kWh / ton - standard value 50 kWh / ton = +10 kWh / ton. The deviation of equipment efficiency: actual value 88% - standard value 90% = -2%. The deviation of energy utilization rate: actual value 82% - standard value 85% = -3%. The deviation of carbon emission intensity: actual value 0.6 kg CO2 / kWh - standard value 0.5 kg CO2 / kWh = +0.1 kg CO2 / kWh. Through analysis, it can be seen that the actual energy consumption intensity of this motor is 10 kWh / ton higher than the standard value, indicating that its energy consumption is relatively high and there may be energy-saving potential. The actual equipment efficiency is 2% lower than the standard value, indicating that there is a certain amount of energy loss during the energy conversion process of the equipment. The actual energy utilization rate is 3% lower than the standard value, indicating that the equipment fails to fully utilize the input energy during operation. The actual carbon emission intensity is 0.1 kg CO2 / kWh higher than the standard value, meaning that the carbon emission level of this equipment is relatively high and has a greater impact on the environment.

[0045] In some other embodiments of the present application, the specific process of generating the energy consumption analysis results of each power equipment according to the multi-source standardized data includes: obtaining the 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 benchmark energy consumption within a preset period according to 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 according to the multi-source standardized data; and determining the deviation values 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 results of each power equipment.

[0046] Among them, the multi-source standardized data includes power identification - power standard value, power consumption identification - power consumption standard value, equipment status identification - equipment status standard value, operation time identification - operation time standard value, load rate identification - load rate standard value, temperature identification - temperature standard value, and humidity identification - humidity standard value.

[0047] 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 equipment according to multi-source standardized data includes: determining the power identifier, electricity quantity identifier, equipment status identifier, operating time identifier, load factor identifier, temperature identifier, and humidity identifier of each power equipment; based on the power identifier, electricity quantity identifier, equipment status identifier, operating time identifier, load factor identifier, temperature identifier, and humidity identifier of each power equipment, obtaining the power standard value, electricity quantity standard value, equipment status standard value, operating time standard value, load factor standard value, temperature standard value, and humidity standard value of each power equipment from the multi-source standardized data; when the equipment status standard value indicates that each power equipment is operating, calculating the actual energy consumption intensity of each power equipment according to the operating time standard value, load factor standard value, and electricity quantity standard value; when the equipment status standard value indicates that each power equipment is not operating, recording the actual energy consumption intensity of each power equipment as 0; ; Calculating a comprehensive correction coefficient based on the temperature standard value and the humidity standard value; the calculation formula of the comprehensive correction coefficient is: ; Wherein, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the temperature standard value, is the humidity standard value, is the preset optimal operating temperature, preset optimal operating humidity, is the temperature coefficient, is the humidity coefficient; Calculating the actual equipment efficiency of each power equipment 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 equipment according to the electricity quantity standard value and the load factor standard value; ; Wherein, is the electricity quantity standard value, is the fuel consumption, is the equipment efficiency, , is the load factor standard value; Calculating the actual carbon emission intensity of each power equipment according to the load factor standard value; ; Wherein, is the correction coefficient of transmission and distribution losses, , and the loss rate is 6%-8%.

[0048] S104, 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 the equipment energy efficiency ranking.

[0049] Among them, visualization is to display data in the form of graphs, charts or other visual forms to more intuitively understand the meaning and trend of the data. The energy consumption analysis results are the results obtained by analyzing the energy consumption data of power equipment, including indicators such as energy consumption intensity, equipment efficiency, energy utilization rate, and carbon emission intensity. The dynamic dashboard is a real-time updated visual interface used to display the changes of key indicators and data. The real-time energy flow diagram is a dynamic chart that shows the flow of electricity between equipment, including input, output and losses. The equipment energy efficiency ranking is to sort the equipment according to the energy efficiency indicators of the equipment (such as energy consumption intensity, equipment efficiency, etc.).

[0050] In some embodiments of the present application, a data visualization tool (such as Tableau, Power BI or a custom Web application) is used to convert the analysis results into graphs and charts. Show the flow of electricity between each equipment, including input, output and losses. Sort the equipment according to the energy efficiency indicators of the equipment, and show high-energy-efficiency and low-energy-efficiency equipment. Show the flow of electricity from the transformer to the motor and lighting system. Use dynamic arrows and color coding to represent the direction and intensity of the electricity flow. Sort the equipment according to the energy efficiency indicators (such as equipment efficiency), and use bar charts or lists to show the ranking. High-energy-efficiency equipment is represented by green, and low-energy-efficiency equipment is represented by red. Deploy the dynamic dashboard to a Web server and display it to the management personnel through a browser or a dedicated client application. The management personnel can view the dynamic dashboard in real time to understand the energy consumption situation and energy efficiency ranking of the equipment.

[0051] For example Figure 3 As shown, the server collects the relevant data of each power equipment in the area. After processing, it can be sent to the client for display. The relevant results displayed are for example Figure 4 , Figure 5 , Figure 6 as shown.

[0052] In the embodiments 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 the multi-source standardized data of each power device. Since the data has undergone modeling and standardization processing, the differences and inconsistencies between data from different sources are eliminated, enabling energy efficiency analysis to be carried out based on a unified and standardized data foundation, eliminating data islands, covering not only power consumption data but also comprehensively considering device operation parameters and device environment 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 device energy efficiency ranking at any time through the client, promptly discover abnormal fluctuations or energy waste phenomena during the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and achieving refined management and optimized control of energy.

[0053] The following are the device embodiments of the present application, which can be used to execute 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.

[0054] Please refer to Figure 7 , which shows a schematic structural diagram of an energy efficiency analysis device based on multi-source data provided by an exemplary embodiment of the present application. The 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.

[0055] The multi-source data acquisition module 10 is configured to obtain the power consumption data, device operation parameters, and device environment parameters of each power device in the target area at a preset period to obtain the multi-source data of each power device; The data standardization module 20 is 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; The energy consumption analysis module 30 is configured to generate an energy consumption analysis result for each power device according to the multi-source standardized data; The energy consumption data display module 40 is configured to visualize the energy consumption analysis result of each power device, obtain a dynamic dashboard, and send it to the client to display the real-time energy flow diagram and device energy efficiency ranking.

[0056] It should be noted that when the energy efficiency analysis device based on multi-source data provided in the above embodiments executes the energy efficiency analysis method based on multi-source data, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the energy efficiency analysis device based on multi-source data provided in the above embodiments and the embodiments of the energy efficiency analysis method based on multi-source data belong to the same concept. The implementation process is detailed in the method embodiments and will not be elaborated here.

[0057] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0058] In the embodiments 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 the 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, enabling energy efficiency analysis to be carried out based on a unified and standardized data foundation, eliminating data islands, covering not only power consumption data but also comprehensively considering device operation parameters and device environment 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 device energy efficiency rankings at any time through the client, promptly discover abnormal fluctuations or energy waste phenomena during the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and achieving refined management and optimal control of energy.

[0059] The present application also provides a computer-readable medium, on which program instructions are stored, and when the program instructions are executed by a processor, the energy efficiency analysis method based on multi-source data provided in each of the above method embodiments is implemented.

[0060] The present application also provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the energy efficiency analysis method based on multi-source data in each of the above method embodiments.

[0061] Please refer to Figure 8 , which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 8 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.

[0062] Among them, the communication bus 1002 is used to realize the connection and communication between these components.

[0063] Among them, the user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may further include standard wired interfaces and wireless interfaces.

[0064] Among them, the network interface 1004 may optionally include standard wired interfaces and wireless interfaces (such as WI-FI interfaces).

[0065] Among them, the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005, it executes various functions of the electronic device 1000 and processes data. Optionally, the processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 1001 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001 and may be implemented separately through a single chip.

[0066] Among them, the memory 1005 may include a Random Access Memory (RAM), or may also include a 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. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 1005 can also be at least one storage system located far from the aforementioned processor 1001. As Figure 8 shown, in the memory 1005 as a computer storage medium, it may include an operating system, a network communication module, a user interface module, and an energy efficiency analysis application program based on multi-source data.

[0067] In Figure 8 the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user to obtain the data input by the user; while the processor 1001 can be used to call the energy efficiency analysis application program based on multi-source data stored in the memory 1005, and specifically perform the following operations: Obtain the power consumption data, device operation parameters, and device environment parameters of each power device in the target area according to a preset period to obtain the multi-source data of each power device; 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; Generate the energy consumption analysis results of each power device according to the multi-source standardized data; Visualize the energy consumption analysis results of each power device to obtain a dynamic dashboard and send it to the client to display the real-time energy flow diagram and the device energy efficiency ranking.

[0068] In one embodiment, when the processor 1001 executes the 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, it specifically performs the following operations: Define the entity relationship data structures corresponding to the power consumption data, device operation parameters, and device environment parameters respectively to obtain an energy consumption data structure, a device data structure, and an environment parameter data structure; Map the power consumption data, operation parameters of power equipment, and equipment environment parameters of each power equipment to the energy consumption data structure, equipment data structure, and environment parameter data structure respectively, to obtain multi-source data with unified data structures for each power equipment. Perform time alignment on the multi-source data with unified data structures to obtain multi-source standardized data for each power equipment.

[0069] In one embodiment, when the processor 1001 executes the operation of mapping the power consumption data, operation parameters of power equipment, and equipment environment parameters of each power equipment to the energy consumption data structure, equipment data structure, and environment parameter data structure respectively to obtain multi-source data with unified data structures, it specifically performs the following operations: Extract the description information that matches the power identifier and electricity quantity identifier from the power consumption data of each power equipment; Extract the description information that matches the equipment status identifier, operation time identifier, and load rate identifier from the operation parameters of each power equipment; Extract the description information that matches the temperature identifier and humidity identifier from the equipment environment parameters of each power equipment; Identify multiple entity keywords of each extracted description information, and perform entity screening on the multiple entity keywords of each description information to generate the attribute values of each description information; Convert the attribute values of each description information into standard values according to the data format indicated by the identifier corresponding to each description information; Establish the mapping relationship between the identifier corresponding to each description information and the standard value of each description information to obtain multi-source data with unified data structures.

[0070] In one embodiment, when the processor 1001 executes the operation of identifying multiple entity keywords of each extracted description information, it specifically performs the following operations: Input each extracted description information into multiple text encoders in sequence to convert each description information into a vector representation of a fixed dimension, and obtain multiple entity vectors; Input the multiple entity vectors into the attention network, and output the word vectors given the attention mechanism; Input the word vectors into the neural network for labeling and segmenting sequence data, and output the keyword label sequence; Map the keyword label sequence to entity labels through the output layer to obtain multiple entity keywords of each description information.

[0071] In one embodiment, when the processor 1001 executes the operation of screening multiple entity keywords of each description information to generate the attribute values of each description information, it specifically performs the following operations: Traverse the target entity keywords from the multiple entity keywords of each description information; Calculate the similarity between the target entity keyword and other entity keywords in the multiple entity keywords except the target entity keyword; From the entity keywords with similarity greater than the preset threshold, screen out the entity keyword with the largest attribute value; Use the attribute value of the entity keyword with the largest attribute value as the attribute value of each description information.

[0072] In one embodiment, when the processor 1001 executes 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 following operations are specifically performed: Perform data cleaning on the multi-source data of each power device to obtain a preprocessed multi-source data set; Input 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, device data structure, and environmental parameter data structure to a scale window established based on a sliding window algorithm, and the scale window is used to perform format conversion on the input information according to the bound parameters; Output multi-source data with a unified data structure for each power device corresponding to the multi-source standardized data; Perform spatio-temporal alignment on the multi-source data with a unified data structure to obtain the multi-source standardized data of each power device.

[0073] In one embodiment, when the processor 1001 executes generating the energy consumption analysis results of each power device according to the multi-source standardized data, the following operations are specifically performed: Obtain predefined energy efficiency standard values; wherein, the predefined energy efficiency standard values include standard energy consumption intensity, standard device efficiency, standard energy utilization rate, and standard carbon emission intensity; Determine the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device according to the multi-source standardized data; Compare the deviation values between the standard energy consumption intensity, standard device efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device to obtain the energy consumption analysis results of each power device.

[0074] In one embodiment, when the processor 1001 executes generating the energy consumption analysis results of each power device according to the multi-source standardized data, the following operations are specifically performed: Obtain the energy consumption data of each power device within a historical time period; Based on the energy consumption data of each power device, establish a regression model; Predict the baseline energy consumption within a preset period according to the regression model; Determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment according to the multi-source standardized data; Determine the deviation values 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 results of each power equipment.

[0075] In the embodiments of the present application, on the one hand, data modeling and standardization processing are performed on the multi-source data of each power equipment to obtain the multi-source standardized data of each power equipment. Since the data has been modeled and standardized, the differences and inconsistencies between data from different sources are eliminated, enabling energy efficiency analysis to be carried out based on a unified and standardized data foundation, eliminating data islands, covering not only power consumption data but also comprehensively considering equipment operation parameters and equipment environment 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 rankings at any time through the client, promptly discover abnormal fluctuations or energy waste phenomena during the energy consumption process and take corresponding energy optimization measures, thereby effectively reducing energy consumption, improving energy utilization efficiency, and realizing refined management and optimized control of energy.

[0076] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant 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 the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium of 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, etc.

[0077] The above-disclosed are only the preferred embodiments of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall 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 side, the method includes: Obtaining the power consumption data, device operation parameters, and device environment 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 processing on the multi-source data of each power device to obtain the multi-source standardized data of each power device; Generating an energy consumption analysis result for each power device according to the multi-source standardized data; Visualizing the energy consumption analysis results of each power device to obtain a dynamic dashboard and sending it to the client to display the real-time energy flow diagram and the device energy efficiency ranking.

2. The method according to claim 1, wherein The performing 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 includes: Defining the entity relationship data structures corresponding to the power consumption data, device operation parameters, and device environment parameters respectively to obtain an energy consumption data structure, a device data structure, and an environment parameter data structure; Mapping the power consumption data, power device operation parameters, and device environment parameters of each power device to the energy consumption data structure, device data structure, and environment parameter data structure respectively to obtain multi-source data with unified data structures for each power device; Performing time alignment on the multi-source data with unified data structures to obtain the multi-source standardized data of each power device.

3. The method according to claim 2, wherein The energy consumption data structure includes a power identifier and an electricity quantity identifier; the device data structure includes a device status identifier, an operation time identifier, and a load rate identifier; the environment parameter data structure includes a temperature identifier and a humidity identifier; Mapping the power consumption data, power device operation parameters, and device environment parameters of each power device to the energy consumption data structure, device data structure, and environment parameter data structure respectively to obtain multi-source data with unified data structures, including: Extracting the description information matching the power identifier and electricity quantity identifier from the power consumption data of each power device; Extracting the description information matching the device status identifier, operation time identifier, and load rate identifier from the power device operation parameters of each power device; Extracting the description information matching the temperature identifier and humidity identifier from the device environment parameters of each power device; Identifying multiple entity keywords of each extracted description information and performing entity screening on the multiple entity keywords of each description information to generate the attribute value of each description information; Converting the attribute value of each description information into a standard value according to the data format indicated by the identifier corresponding to each description information; Establishing a mapping relationship between the identifier corresponding to each description information and the standard value of each description information to obtain multi-source data with unified data structures.

4. The method according to claim 3, characterized in that A keyword recognition model is preset in advance, and the keyword recognition model includes multiple text encoders, an attention network with an attention mechanism, a neural network for annotating and segmenting sequence data, and a keyword output layer; The identifying multiple entity keywords of each extracted description information includes: Input each extracted description information into the multiple text encoders in sequence to convert each description information into a vector representation of a fixed dimension, obtaining multiple entity vectors; Input the multiple entity vectors into the attention network to output word vectors with attention mechanism; Input the word vectors into the neural network for annotating and segmenting sequence data to output a keyword label sequence; Map the keyword label sequence to entity labels through an output layer to obtain multiple entity keywords for each description information.

5. The method according to claim 3, wherein Screen the multiple entity keywords for each description information to generate the attribute value for each description information, including: Traverse the target entity keywords from the multiple entity keywords for each description information; Calculate the similarity between the target entity keywords and other entity keywords except the target entity keywords among the multiple entity keywords; Select the entity keyword with the largest attribute value from the entity keywords with similarity greater than a preset threshold; Use the attribute value of the entity keyword with the largest attribute value as the attribute value for each description information.

6. The method according to any one of claims 1-5, characterized in that, 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, including: Perform data cleaning on the multi-source data of each power device to obtain a preprocessed multi-source data set; Input the preprocessed multi-source data set into a preset data structure standardization model; wherein, the preset data structure standardization model is obtained by binding predefined energy consumption data structures, device data structures, and environmental parameter data structures to a scale window established based on a sliding window algorithm, and the scale window is used to perform format conversion on the input information according to the bound parameters; Output the multi-source data with unified data structures for each power device corresponding to the multi-source standardized data; Perform spatio-temporal alignment on the multi-source data with unified data structures to obtain the multi-source standardized data of each power device.

7. The method according to claim 1, wherein Generate the energy consumption analysis results for each power device according to the multi-source standardized data, including: Obtain predefined energy efficiency standard values; wherein, the predefined energy efficiency standard values include standard energy consumption intensity, standard device efficiency, standard energy utilization rate, and standard carbon emission intensity; Determine the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, and actual carbon emission intensity of each power device according to the multi-source standardized data; Compare the deviation values between the standard energy consumption intensity, standard device efficiency, standard energy utilization rate, standard carbon emission intensity and the actual energy consumption intensity, actual device efficiency, actual energy utilization rate, actual carbon emission intensity of each power device to obtain the energy consumption analysis results for each power device.

8. The method according to claim 1, wherein Generate the energy consumption analysis results for each power device according to the multi-source standardized data, including: Obtain the energy consumption data of each power device within a historical time period; Establish a regression model based on the energy consumption data of each power device; Predict the benchmark energy consumption within a preset period according to the regression model; Based on the multi-source standardized data, determine the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment; Determine the deviation values 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 results of each power equipment.

9. The method according to any one of claims 7-8, characterized in that, The multi-source standardized data includes power identification - power standard value, electricity quantity identification - electricity quantity standard value, equipment status identification - equipment status standard value, operation time identification - operation time standard value, load rate identification - load rate standard value, temperature identification - temperature standard value, and humidity identification - humidity standard value; The determining the actual energy consumption intensity, actual equipment efficiency, actual energy utilization rate, and actual carbon emission intensity of each power equipment based on the multi-source standardized data includes: Determine the power identification, electricity quantity identification, equipment status identification, operation time identification, load rate identification, temperature identification, and humidity identification of each power equipment; Based on the power identification, electricity quantity identification, equipment status identification, operation time identification, load rate identification, temperature identification, and humidity identification of each power equipment, obtain the power standard value, electricity quantity standard value, equipment status standard value, operation time standard value, load rate standard value, temperature standard value, and humidity standard value of each power equipment from the multi-source standardized data; When the equipment status standard value indicates that each power equipment is operating, calculate the actual energy consumption intensity of each power equipment according to the operation time standard value, the load rate standard value, and the electricity quantity standard value; when the equipment status standard value indicates that each power equipment is not operating, record the actual energy consumption intensity of each power equipment as 0; ； Based on the temperature standard value and the humidity standard value, calculate a comprehensive correction coefficient; the calculation formula for the comprehensive correction coefficient is: ； Among them, is the comprehensive correction coefficient, is the temperature correction result, is the humidity correction result, is the temperature standard value, is the humidity standard value, is the preset optimal operating temperature, is the preset optimal operating humidity, is the temperature coefficient, is the humidity coefficient; According to the power standard value, the comprehensive correction coefficient, and a preset theoretical maximum value, calculate the actual equipment efficiency of each power equipment; ； According to the electricity quantity standard value and the load rate standard value, calculate the actual energy utilization rate of each power equipment; ； Among them, is the standard value of the power consumption, is the fuel consumption, is the equipment efficiency, , is the standard value of the load rate; According to the load rate standard value, calculate the actual carbon emission intensity of each power equipment; ； Among them, is the correction factor for transmission and distribution losses, , and the loss rate is 6% - 8%.

10. An energy efficiency analysis system based on multi-source data, characterized in that, The system includes: A multi-source data acquisition module, configured to acquire the power energy consumption data, equipment operation parameters, and equipment environment parameters of each power equipment in a target area at a preset period to obtain the multi-source data of each power equipment; A data standardization module, configured to perform data modeling and standardization processing on the multi-source data of each power equipment to obtain the multi-source standardized data of each power equipment; An energy consumption analysis module, configured to generate the energy consumption analysis results of each power equipment according to the multi-source standardized data; An energy consumption data display module, configured to visualize the energy consumption analysis results of each power equipment to obtain a dynamic dashboard and send it to a client to display a real-time energy flow diagram and equipment energy efficiency ranking.

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