Energy fusion analysis method and device based on big data and electronic equipment

Abstract

The invention provides an energy fusion analysis method and device based on big data and electronic equipment, and relates to the field of energy management. The method comprises the following steps: acquiring cross-type energy data; information extraction is carried out on the electric power energy data, the natural gas energy data and the water energy data, an initial intermediate key value pair is generated, and the initial intermediate key value pair comprises a plurality of initial keys and initial values corresponding to the initial keys; performing data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replacing the initial value with the conversion value to obtain a target intermediate key value pair; performing data fusion on the target intermediate key value pair to obtain fused data, and performing energy consumption prediction on the fused data to obtain a prediction result; and according to a prediction result, performing energy planning on the electric energy data, the natural gas energy data and the water energy data to obtain an energy analysis result, and performing data fusion to realize cross-energy-type data analysis.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and specifically to an energy fusion analysis method, device and electronic equipment based on big data. Background Art

[0002] With the growing demand for energy conservation and emission reduction in society, energy data consumption analysis has become a hot topic in the field of energy management. The consumption analysis of energy data can provide a scientific basis for the formulation of energy conservation and emission reduction measures, help reduce greenhouse gas emissions, and promote environmental protection and sustainable development. In related technologies, energy management systems are used for data analysis, but most of them focus on the monitoring and optimization of a single energy type and lack the ability to analyze data across energy types. Summary of the invention

[0003] The present application provides a method, device and electronic device for energy fusion analysis based on big data, which can realize data analysis across energy types by performing data fusion.

[0004] The technical solution of the embodiment of the present application is as follows: In a first aspect, an embodiment of the present application provides an energy fusion analysis method based on big data, the method comprising: Acquire cross-category energy data, where the cross-category energy data includes electric energy data, natural gas energy data, and water energy data; Extracting information from the electric energy data, the natural gas energy data, and the water energy data to generate an initial intermediate key-value pair, wherein the initial intermediate key-value pair includes a plurality of initial keys and initial values ​​corresponding to each of the initial keys; Performing data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replacing the initial value with the conversion value to obtain a target intermediate key-value pair; Performing data fusion on the target intermediate key-value pair to obtain fused data, and performing energy consumption prediction on the fused data to obtain a prediction result; According to the prediction results, energy planning is performed on the electric power energy data, the natural gas energy data and the water energy data to obtain energy analysis results.

[0005] In the above technical scheme, cross-category energy data is first obtained, and the cross-category energy data includes electric energy data, natural gas energy data and water energy data. By obtaining the cross-category energy data, data support is provided for the subsequent integration of multiple types of energy data, so as to realize cross-category energy data analysis; information is extracted from the electric energy data, natural gas energy data and water energy data to generate initial intermediate key-value pairs, and the initial intermediate key-value pairs include multiple initial keys and initial values ​​corresponding to each initial key. By extracting information from different types of data, data conversion can be performed subsequently; data type conversion is performed on the initial value corresponding to each initial key to obtain the conversion value corresponding to the initial key. Value, and use the converted value to replace the initial value to obtain the target intermediate key-value pair. By performing data conversion, the uniformity of data types can be guaranteed, so as to perform data fusion, thereby realizing data analysis across energy types; the target intermediate key-value pair is subjected to data fusion to obtain fused data, and energy consumption is predicted for the fused data to obtain prediction results. By performing predictive analysis on energy data, the consumption can be estimated so as to perform reasonable planning later and reduce energy consumption costs; according to the prediction results, energy planning is performed on electric power energy data, natural gas energy data and water energy data to obtain energy analysis results. According to the prediction results, data analysis across energy types can be realized to improve data analysis accuracy.

[0006] In some possible embodiments of the present application, extracting information from the electric energy data, the natural gas energy data, and the water energy data to generate initial intermediate key-value pairs includes: Extracting timestamps and data types of the electric energy data, the natural gas energy data, and the water energy data respectively, and fusing the extracted timestamps and data types to obtain the electric energy type corresponding to the electric energy data, the natural gas type corresponding to the natural gas energy data, and the water energy type corresponding to the water energy data; Performing numerical extraction on the electric energy data, the natural gas energy data and the water energy data respectively to obtain the electric power value corresponding to the electric energy data, the natural gas value corresponding to the natural gas energy data and the water energy value corresponding to the water energy data; The electricity type, the natural gas type and the water energy type are used as the initial keys, and the electricity value, the natural gas value and the water energy value are used as the initial values ​​corresponding to the initial keys.

[0007] In some possible embodiments of the present application, performing data type conversion on the initial value corresponding to each initial key to obtain the converted value corresponding to the initial key includes: Obtaining a conversion coefficient of hydroelectric power generation, wherein the conversion coefficient is obtained through a historical hydroelectric power generation conversion rate; Convert the natural gas value corresponding to the natural gas type and the water energy value corresponding to the water energy type into the same data type as the electricity type to obtain a converted natural gas value and a converted initial water energy value; The converted initial water energy value is multiplied by the conversion coefficient to obtain the converted target water energy value.

[0008] In some possible embodiments of the present application, performing energy consumption prediction on the fusion data to obtain a prediction result includes: Using a preset target deep learning model to perform energy consumption prediction on the fused data to obtain the prediction result; The target deep learning model is obtained through the following process: Acquire a plurality of historical fusion data, and extract weather conditions, timestamps, and energy prices from the historical fusion data; Using a preset initial deep learning model to perform feature processing on the weather state, the timestamp, and the energy price to obtain a predicted energy demand; The parameters of the initial deep learning model are adjusted based on the predicted energy demand to obtain the target deep learning model.

[0009] In some possible embodiments of the present application, energy planning is performed on the electric power energy data, the natural gas energy data, and the water energy data according to the prediction result to obtain energy analysis results, including: In the case where the prediction result is a first energy demand, the electric power energy data, the natural gas energy data and the water energy data are divided according to a first allocation ratio corresponding to the first energy demand to obtain the energy analysis result; When the prediction result is the second energy demand, the electric power energy data, the natural gas energy data and the water energy data are divided according to the second allocation ratio corresponding to the second energy demand to obtain the energy analysis result, wherein the first energy demand is greater than the second energy demand.

[0010] In some possible embodiments of the present application, the first allocation ratio is that the electric energy data accounts for a first ratio, the natural gas energy data accounts for a second ratio, and the water energy data accounts for a third ratio, the first ratio is greater than the third ratio, and the third ratio is greater than the second ratio; The second distribution ratio is that the electric power energy data accounts for the fourth ratio, the natural gas energy data accounts for the fifth ratio, and the water energy data accounts for the sixth ratio, and the fourth ratio, the fifth ratio, and the sixth ratio are evenly distributed.

[0011] In some possible embodiments of the present application, after fusing the target intermediate key-value pair to obtain fused data, the method further includes: Encrypting the fused data to obtain fused encrypted data; Energy consumption prediction is performed on the fused encrypted data to obtain a prediction result.

[0012] In a second aspect, an embodiment of the present application provides an energy fusion analysis device based on big data, the device comprising: An energy data acquisition module, used to acquire cross-category energy data, wherein the cross-category energy data includes electric energy data, natural gas energy data and water energy data; A data extraction module, used to extract information from the electric power energy data, the natural gas energy data and the water energy data, and generate an initial intermediate key-value pair, wherein the initial intermediate key-value pair includes a plurality of initial keys and initial values ​​corresponding to each of the initial keys; A data conversion module, used to perform data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replace the initial value with the conversion value to obtain a target intermediate key-value pair; A fusion prediction module is used to perform data fusion on the target intermediate key-value pair to obtain fused data, and perform energy consumption prediction on the fused data to obtain a prediction result; The energy data analysis module is used to perform energy planning on the electric power energy data, the natural gas energy data and the water energy data according to the prediction results to obtain energy analysis results.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a user interface, a communication bus and a network interface, wherein the processor, the memory, the user interface and the network interface are respectively connected to the communication bus, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods provided in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the methods provided in the first aspect is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The method of first acquiring cross-category energy data, which includes electric energy data, natural gas energy data and water energy data, provides data support for the subsequent integration of multiple types of energy data by acquiring cross-category energy data, so as to realize cross-category energy data analysis; extracting information from electric energy data, natural gas energy data and water energy data, generating initial intermediate key-value pairs, which include multiple initial keys and initial values ​​corresponding to each initial key, extracting information from different types of data, so as to facilitate subsequent data conversion; performing data type conversion on the initial value corresponding to each initial key, and obtaining the conversion value corresponding to the initial key. , and use the conversion value to replace the initial value to obtain the target intermediate key-value pair. By performing data conversion, the uniformity of data types can be guaranteed so that data fusion can be performed to realize data analysis across energy types; the target intermediate key-value pair is data-fused to obtain fused data, and energy consumption is predicted for the fused data to obtain prediction results. By performing prediction analysis on energy data, consumption can be estimated so that reasonable planning can be carried out later to reduce energy consumption costs; according to the prediction results, energy planning is carried out for electric energy data, natural gas energy data, and water energy data to obtain energy analysis results. According to the prediction results, data analysis across energy types can be realized to improve data analysis accuracy. Therefore, the problem that the related technologies focus on the monitoring and optimization of a single energy type and lack the ability to analyze data across energy types is effectively solved.

[0016] 2. Distributed processing of big data can improve the accuracy and speed of data analysis and processing.

[0017] 3. By encrypting the data, data security can be ensured and data leakage can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of an energy fusion analysis method based on big data provided by an embodiment of the present application; Figure 2 yes Figure 1 A schematic flow chart of a sub-step of step S200; Figure 3 yes Figure 1 A schematic flow chart of a sub-step of step S300; Figure 4 yes Figure 1 A schematic flow chart of a sub-step of step S500; Figure 5 It is a structural schematic diagram of an energy fusion analysis device based on big data provided by an embodiment of the present application; Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] The embodiments of the present application provide a method, device, electronic device and readable storage medium for energy fusion analysis based on big data. The method for energy fusion analysis based on big data first obtains cross-category energy data, and the cross-category energy data includes electric energy data, natural gas energy data and water energy data. By obtaining the cross-category energy data, data support is provided for the subsequent fusion of multiple types of energy data, so as to realize cross-category energy data analysis; information is extracted from the electric energy data, natural gas energy data and water energy data to generate initial intermediate key-value pairs, and the initial intermediate key-value pairs include multiple initial keys and initial values ​​corresponding to each initial key, and information is extracted from different types of data to facilitate subsequent data conversion; and each initial key corresponding to a specific value is converted into a specific value. The data type of the initial value is converted to obtain the conversion value corresponding to the initial key, and the initial value is replaced by the conversion value to obtain the target intermediate key-value pair. By performing data conversion, the uniformity of the data type can be guaranteed, so as to facilitate data fusion and realize data analysis across energy types; the target intermediate key-value pair is subjected to data fusion to obtain fused data, and energy consumption is predicted on the fused data to obtain the prediction result. By performing predictive analysis on the energy data, the consumption can be estimated so as to carry out reasonable planning in the future and reduce the energy consumption cost; according to the prediction results, energy planning is carried out on the electric energy data, the natural gas energy data and the water energy data to obtain the energy analysis result. According to the prediction results, data analysis across energy types can be realized to improve the accuracy of data analysis.

[0023] It should be noted that this energy fusion analysis method based on big data is mainly used in intelligent building energy management systems, industrial production energy efficiency monitoring, urban integrated energy services, etc. By converting, integrating and predicting various types of energy data, it is possible to achieve cross-energy data analysis, improve data processing accuracy and speed, and reduce energy consumption.

[0024] The technical solution provided in the embodiments of the present application is further described below in conjunction with the accompanying drawings.

[0025] Reference Figure 1 , Figure 1 It is a flow chart of the energy fusion analysis method based on big data provided by the embodiment of the present application. The energy fusion analysis method based on big data is applied to an energy fusion analysis device based on big data, and the energy fusion analysis method based on big data is executed by a processor in an electronic device or a readable storage medium. The energy fusion analysis method based on big data includes step S100, step S200, step S300, step S400 and step S500.

[0026] Step S100, obtaining cross-category energy data, where the cross-category energy data includes electric energy data, natural gas energy data, and water energy data.

[0027] In one embodiment, cross-category energy data includes electric energy data, natural gas energy data and water energy data. Electric energy data includes weather conditions, timestamps, corresponding energy prices, energy types, power generation, power generation capacity, power generation efficiency, power consumption, etc. when data is collected; natural gas energy data includes weather conditions, timestamps, corresponding natural gas prices, energy types, natural gas consumption, etc.; water energy data includes weather conditions, timestamps, corresponding water prices, energy types, water consumption, water resource conversion rate, etc. A preset crawler algorithm can be used to obtain electric energy data, natural gas energy data and water energy data from government departments or regulatory agencies; crawler algorithms can also be used to crawl data publicly available on the Internet, and then mine and analyze to extract electric energy data, natural gas energy data and water energy data. By obtaining cross-category energy data, data support is provided for the subsequent fusion of multiple types of energy data, so as to realize cross-category energy data analysis.

[0028] Step S200, extracting information from electric power data, natural gas data and water data to generate initial intermediate key-value pairs, the initial intermediate key-value pairs including a plurality of initial keys and initial values ​​corresponding to the initial keys.

[0029] In one embodiment, a distributed computing framework can be used for data processing, and distributed computing can accelerate the data processing process and improve the data processing speed. Among them, the Map() function of the MapReduce framework in distributed computing is used to first extract information from the power energy data, natural gas energy data, and water energy data to generate an initial intermediate key-value pair. Since the power energy data, natural gas energy data, and water energy data include multiple data, the initial intermediate key-value pair includes multiple initial keys and initial values ​​corresponding to each initial key. By extracting information from different types of data, data conversion can be performed later.

[0030] like Figure 2 As shown, information is extracted from the electric energy data, natural gas energy data, and water energy data to generate initial intermediate key-value pairs, including but not limited to the following steps: Step S210, extract timestamps and data types for the electric energy data, natural gas energy data, and water energy data, respectively, and merge the extracted timestamps and data types to obtain the electric type corresponding to the electric energy data, the natural gas type corresponding to the natural gas energy data, and the water energy type corresponding to the water energy data.

[0031] In some possible embodiments of the present application, the Map() function is used to extract the timestamp and data type in the electric energy data; since it is cross-category energy data, in order to distinguish the extracted data, the data type is the energy type corresponding to the extracted information, and the timestamp and data type are merged to identify the extracted data. Similarly, the Map() function is used to extract the timestamp and data type in the natural gas energy data, and the Map() function is used to extract the timestamp and data type in the water energy data.

[0032] It should be noted that the way to fuse the timestamp and the data type is to concatenate the timestamp and the data type. The timestamp and the corresponding strings of the data type can be concatenated in the form of a string, or they can be represented in the form of a matrix, and the matrices corresponding to the timestamp and the data type are added and fused. By fusing the timestamp and the data type, it is possible to achieve a one-to-one correspondence between the extracted information and the corresponding energy type, and obtain the electricity type corresponding to the electric energy data, the natural gas type corresponding to the natural gas energy data, and the water energy type corresponding to the water energy data, so that the cross-type energy data can be represented in the form of key-value pairs later.

[0033] Step S220, numerical extraction is performed on the electric energy data, the natural gas energy data and the water energy data respectively to obtain the electric power value corresponding to the electric energy data, the natural gas value corresponding to the natural gas energy data and the water energy value corresponding to the water energy data.

[0034] In some possible embodiments of the present application, in step S210, information extraction is performed on the data type, and the corresponding values ​​of different energy data types also need to be extracted. The Map() function is also used to extract the values ​​of the electric energy data, natural gas energy data, and water energy data, respectively, to obtain the electric value corresponding to the electric energy data, the natural gas value corresponding to the natural gas energy data, and the water energy value corresponding to the water energy data, so as to form a key-value pair later. Among them, the electric value includes the weather status value, time, and the electric energy value; the natural gas value includes the weather status value, time, and the natural gas amount; the water energy value includes the weather status value, time, water consumption, conversion rate, etc.

[0035] Step S230: The electricity type, natural gas type and water energy type are used as initial keys, and the electricity value, natural gas value and water energy value are used as initial values ​​corresponding to the initial keys.

[0036] In some possible embodiments of the present application, the initial intermediate key-value pair is represented in the form of a key-value pair, including an initial key and an initial value corresponding to the initial key, and the power type, natural gas type, and water energy type obtained in step S210 are used as the initial key, and the power value, natural gas value, and water energy value obtained in step S220 are used as the initial value corresponding to the initial key, and the initial intermediate key-value pair is formed by the initial key and the initial value. The initial intermediate key-value pair can not only uniformly represent cross-type energy data, but also facilitates subsequent data conversion and data prediction.

[0037] Step S300, performing data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replacing the initial value with the conversion value to obtain a target intermediate key-value pair.

[0038] In one embodiment, by representing cross-category energy data as key-value pairs, only the initial value can be converted into a data type so that the value can be used for subsequent prediction processing. The converted value replaces the initial value to obtain a target intermediate key-value pair, in which the values ​​corresponding to different types in the target intermediate key-value pair have the same data type and the same unit, and the value corresponding to the initial key can be directly processed.

[0039] like Figure 3 As shown, the data type conversion is performed on the initial value corresponding to each initial key to obtain the conversion value corresponding to the initial key, including but not limited to the following steps: Step S310, obtaining a conversion coefficient of hydroelectric power generation, where the conversion coefficient is obtained through a historical hydroelectric power generation conversion rate.

[0040] In one embodiment, the units of water energy data and electric energy data are different, and there will be a numerical loss when the data is converted. The numerical loss of the conversion is represented by a conversion coefficient. The conversion coefficient is obtained by mathematically counting the conversion rate of historical hydropower generation. Specifically, the mode or average value of the conversion rate can be calculated to obtain the conversion coefficient. The conversion coefficient is recorded, and the conversion coefficient of hydropower generation is obtained through a data reading function, so as to perform accurate data conversion later. The data reading function can be a read() function.

[0041] Step S320, converting the natural gas value corresponding to the natural gas type and the water energy value corresponding to the water energy type into the same data type as the electricity type, to obtain the converted natural gas value and the converted initial water energy value.

[0042] In one embodiment, the unit of the electric power value is kilowatt-hour, and the unit of the natural gas value can also be expressed as kilowatt-hour. When converting the natural gas value to the same unit as the electric power value, there is no energy loss. The natural gas value corresponding to the natural gas type can be directly converted to the same data type as the electric power type to obtain the converted natural gas value. The converted natural gas value has the same unit as the electric power value and is more accurately expressed, providing support for subsequent accurate data fusion and prediction. The unit of water energy is usually expressed as cubic meters. There is a conversion loss when converting the same unit. First, the water energy value corresponding to the water energy type is converted to the same data type as the electric power type to obtain the converted initial water energy value, in preparation for subsequent conversion.

[0043] Step S330, multiplying the converted initial water energy value by the conversion coefficient to obtain the converted target water energy value.

[0044] In one embodiment, since the unit of water energy is usually expressed as cubic meters, there is a conversion loss when converting the same unit. Therefore, the converted initial water energy value is multiplied by the conversion coefficient to obtain the converted target water energy value. The converted target water energy value has the same unit as the electric power value and is expressed more accurately, providing support for subsequent accurate data fusion and prediction.

[0045] Step S400, data fusion is performed on the target intermediate key-value pair to obtain fused data, and energy consumption prediction is performed on the fused data to obtain a prediction result.

[0046] In one embodiment, the Reduce() function in the MapReduce framework can be used to fuse the target intermediate key-value pairs to obtain fused data. By performing data fusion, the uniformity of the data types of cross-type energy data can be ensured, so as to realize the subsequent cross-type energy data analysis. Then, the preset target deep learning model is used to predict the energy consumption of the fused data to obtain the prediction results. By performing predictive analysis on cross-type energy data, the consumption can be estimated, so as to carry out reasonable planning and reduce energy consumption costs.

[0047] Among them, the target deep learning model is obtained through the following process: obtaining multiple historical fusion data, extracting weather conditions, timestamps and energy prices from the historical fusion data; using the preset initial deep learning model to perform feature processing on weather conditions, timestamps and energy prices to obtain predicted energy demand; adjusting the parameters of the initial deep learning model based on the predicted energy demand to obtain the target deep learning model.

[0048] In some possible embodiments of the present application, the historical fusion data includes fusion-processed electric energy data, natural gas energy data, and water energy data. The electric energy data includes the weather status, timestamp, corresponding energy price, energy type, power generation, power generation capacity, power generation efficiency, power consumption, etc. when the data is collected; the natural gas energy data includes the weather status, timestamp, corresponding natural gas price, energy type, natural gas consumption, etc.; the water energy data includes the weather status, timestamp, corresponding water price, energy type, water consumption, water resource conversion rate, etc., wherein the fusion processing method is similar to that of step S200, which is not repeated here. The historical data can be obtained by the crawler algorithm, and then the data fusion processing is performed to provide data preparation for the subsequent training of the deep learning model. The weather status, timestamp and energy price can be extracted from the historical fusion data. The weather status and timestamp affect the conversion amount of energy, and the energy price affects which type of energy data is used, so as to predict the energy consumption. The preset initial deep learning model is then used to perform feature processing on weather conditions, timestamps, and energy prices, capture the dependencies between sequence data, and make predictions for the next time period, outputting predicted energy demand so that network training can be performed later based on the predicted energy demand.

[0049] In some other possible embodiments of the present application, a loss function is calculated based on the predicted energy demand and the actual energy demand, and the parameters of the initial deep learning model are adjusted using the value of the loss function. After multiple trainings, when the loss function meets the preset training conditions or the number of trainings meets the preset number of times, the training is terminated to obtain the target deep learning model. The energy consumption of the fused data is predicted using the preset target deep learning model to obtain the prediction result. By predicting and analyzing cross-category energy data, the consumption can be estimated so that reasonable planning can be carried out later to reduce energy consumption costs.

[0050] In another embodiment, after data fusion of the target intermediate key-value pairs is performed to obtain fused data, the energy fusion analysis method based on big data also includes but is not limited to: encrypting the fused data to obtain fused encrypted data; and performing energy consumption prediction on the fused encrypted data to obtain prediction results.

[0051] Specifically, in order to ensure the security of data, encryption algorithms (such as symmetric encryption algorithms, etc.) or blockchain technology can be used to encrypt the fused data to obtain fused encrypted data, and the energy consumption of the fused encrypted data is predicted using the preset target deep learning model to obtain the prediction results. This not only ensures the security of the data, but also estimates the consumption through predictive analysis of cross-category energy data, so as to facilitate subsequent reasonable planning.

[0052] Step S500, based on the prediction results, energy planning is performed on the electric power energy data, the natural gas energy data and the water energy data to obtain energy analysis results.

[0053] like Figure 4 As shown, according to the prediction results, energy planning is performed on the electric energy data, natural gas energy data and water energy data to obtain energy analysis results, including but not limited to the following steps: Step S510, when the prediction result is the first energy demand, the electric energy data, the natural gas energy data and the water energy data are divided according to the first allocation ratio corresponding to the first energy demand to obtain an energy analysis result.

[0054] In one embodiment, the first energy demand is a large demand for energy consumption. When the prediction result is the first energy demand, it indicates that more energy is needed to generate electricity. In order to reduce energy consumption, the electric energy data, natural gas energy data, and water energy data are divided according to the first allocation ratio corresponding to the first energy demand to obtain energy analysis results. According to the prediction result, data analysis across energy types can be achieved, thereby improving data analysis accuracy.

[0055] In another embodiment, the first allocation ratio is that electric energy data accounts for a first ratio, natural gas energy data accounts for a second ratio, and water energy data accounts for a third ratio, the first ratio is greater than the third ratio, and the third ratio is greater than the second ratio.

[0056] Among them, the proportion of the first ratio, the second ratio and the third ratio can be expressed as 5:3:2, or other proportion values, to ensure that the first ratio is greater than the third ratio, and the third ratio is greater than the second ratio. The specific proportion values ​​are set by professionals according to the first energy demand. On the basis of meeting the energy supply, energy conservation and emission reduction can also be achieved.

[0057] Step S520, when the prediction result is the second energy demand, the electric energy data, the natural gas energy data and the water energy data are divided according to the second allocation ratio corresponding to the second energy demand to obtain an energy analysis result, wherein the first energy demand is greater than the second energy demand.

[0058] In one embodiment, the first energy demand is greater than the second energy demand, and the second energy demand is a smaller demand for energy consumption. When the prediction result is the second energy demand, it indicates that less energy is needed to generate electricity. In order to reduce energy consumption, the electric energy data, natural gas energy data, and water energy data are divided according to the second allocation ratio corresponding to the second energy demand to obtain energy analysis results. According to the prediction results, data analysis across energy types can be achieved, thereby improving data analysis accuracy.

[0059] In another embodiment, the second allocation ratio is that electric energy data accounts for the fourth ratio, natural gas energy data accounts for the fifth ratio, and water energy data accounts for the sixth ratio, and the fourth ratio, the fifth ratio, and the sixth ratio are evenly distributed.

[0060] Among them, the proportion of the fourth proportion, the fifth proportion and the sixth proportion can be expressed as 1:1:1, or other proportion values ​​to ensure that the fourth proportion, the fifth proportion and the sixth proportion are evenly distributed. The specific proportion values ​​are set by professionals according to the second energy demand. On the basis of meeting the energy supply, energy conservation and emission reduction can also be achieved. It should be noted that the fourth proportion, the fifth proportion and the sixth proportion can also be unevenly distributed. According to the second energy demand, the proportion is adjusted to achieve reasonable planning and reduce energy consumption costs.

[0061] like Figure 5 As shown, an embodiment of the present application provides an energy fusion analysis device 100 based on big data. The energy fusion analysis device 100 based on big data obtains cross-category energy data through an energy data acquisition module 110. The cross-category energy data includes electric energy data, natural gas energy data and water energy data. By obtaining the cross-category energy data, data support is provided for the subsequent fusion of multiple types of energy data, so as to realize cross-category energy data analysis; then the data extraction module 120 is used to extract information from the electric energy data, the natural gas energy data and the water energy data to generate an initial intermediate key-value pair. The initial intermediate key-value pair includes multiple initial keys and initial values ​​corresponding to each initial key. By extracting information from different types of data, data conversion can be performed later; and then the data conversion module 130 is used to extract information from each initial key. The initial value corresponding to the key is converted into a data type to obtain the conversion value corresponding to the initial key, and the initial value is replaced by the conversion value to obtain the target intermediate key-value pair. By performing data conversion, the uniformity of the data type can be guaranteed so as to perform data fusion, thereby realizing data analysis across energy types; the target intermediate key-value pair is subjected to data fusion through the fusion prediction module 140 to obtain fused data, and energy consumption is predicted for the fused data to obtain a prediction result. By performing predictive analysis on the energy data, the consumption can be estimated so as to perform reasonable planning later and reduce energy consumption costs; the energy data analysis module 150 is used to perform energy planning on the electric power energy data, the natural gas energy data and the water energy data according to the prediction results to obtain energy analysis results. According to the prediction results, data analysis across energy types can be realized to improve the accuracy of data analysis.

[0062] It should be noted that the energy data acquisition module 110 is connected to the data extraction module 120, the data extraction module 120 is connected to the data conversion module 130, the data conversion module 130 is connected to the fusion prediction module 140, and the fusion prediction module 140 is connected to the energy data analysis module 150. The above-mentioned energy fusion analysis method based on big data is applied to the energy fusion analysis device 100 based on big data. The energy fusion analysis device 100 based on big data obtains cross-category energy data, and the cross-category energy data includes electric energy data, natural gas energy data and water energy data. By obtaining the cross-category energy data, data support is provided for the subsequent fusion of multiple types of energy data, so as to realize cross-category energy data analysis; information is extracted from the electric energy data, the natural gas energy data and the water energy data to generate an initial intermediate key-value pair, the initial intermediate key-value pair includes multiple initial keys and initial values ​​corresponding to each initial key, and information is extracted from different types of data to facilitate subsequent data conversion; the corresponding values ​​of each initial key are The initial value is converted into a data type to obtain a conversion value corresponding to the initial key, and the initial value is replaced by the conversion value to obtain a target intermediate key-value pair. By performing data conversion, the uniformity of the data type can be ensured so that data fusion can be performed, thereby realizing data analysis across energy types; the target intermediate key-value pair is subjected to data fusion to obtain fused data, and energy consumption is predicted for the fused data to obtain a prediction result. By performing predictive analysis on the energy data, the consumption can be estimated so that reasonable planning can be carried out subsequently to reduce energy consumption costs; based on the prediction results, energy planning is carried out for electric power energy data, natural gas energy data, and water energy data to obtain energy analysis results. Based on the prediction results, data analysis across energy types can be realized to improve data analysis accuracy.

[0063] It should also be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, 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 device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0064] The present application also discloses an electronic device. Figure 6 , Figure 6 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0065] The communication bus 502 is used to realize the connection and communication between these components.

[0066] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0067] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0068] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; 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 501, and it can be implemented separately through a chip.

[0069] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 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 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Refer to Figure 6 , the memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an energy fusion analysis method based on big data.

[0070] exist Figure 6 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call the application program stored in the memory 505 for an energy fusion analysis method based on big data, and when executed by one or more processors 501, the electronic device 500 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0071] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0073] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0074] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0075] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0076] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0077] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An energy fusion analysis method based on big data, characterized in that: The method comprises: Acquire cross-category energy data, where the cross-category energy data includes electric energy data, natural gas energy data, and water energy data; Extracting information from the electric energy data, the natural gas energy data, and the water energy data to generate an initial intermediate key-value pair, wherein the initial intermediate key-value pair includes a plurality of initial keys and initial values ​​corresponding to each of the initial keys; Performing data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replacing the initial value with the conversion value to obtain a target intermediate key-value pair; Performing data fusion on the target intermediate key-value pair to obtain fused data, and performing energy consumption prediction on the fused data to obtain a prediction result; According to the prediction results, energy planning is performed on the electric power energy data, the natural gas energy data and the water energy data to obtain energy analysis results.

2. The method according to claim 1, characterized in that The extracting information from the electric power energy data, the natural gas energy data and the water energy data to generate initial intermediate key-value pairs includes: Extracting timestamps and data types of the electric energy data, the natural gas energy data, and the water energy data respectively, and fusing the extracted timestamps and data types to obtain the electric energy type corresponding to the electric energy data, the natural gas type corresponding to the natural gas energy data, and the water energy type corresponding to the water energy data; Performing numerical extraction on the electric energy data, the natural gas energy data and the water energy data respectively to obtain the electric power value corresponding to the electric energy data, the natural gas value corresponding to the natural gas energy data and the water energy value corresponding to the water energy data; The electricity type, the natural gas type and the water energy type are used as the initial keys, and the electricity value, the natural gas value and the water energy value are used as the initial values ​​corresponding to the initial keys.

3. The method according to claim 2, characterized in that The performing data type conversion on the initial value corresponding to each of the initial keys to obtain the converted value corresponding to the initial key includes: Obtaining a conversion coefficient of hydroelectric power generation, wherein the conversion coefficient is obtained through a historical hydroelectric power generation conversion rate; Convert the natural gas value corresponding to the natural gas type and the water energy value corresponding to the water energy type into the same data type as the electricity type to obtain a converted natural gas value and a converted initial water energy value; The converted initial water energy value is multiplied by the conversion coefficient to obtain the converted target water energy value.

4. The method according to claim 1, characterized in that: The performing energy consumption prediction on the fused data to obtain a prediction result includes: Using a preset target deep learning model to perform energy consumption prediction on the fused data to obtain the prediction result; The target deep learning model is obtained through the following process: Acquire a plurality of historical fusion data, and extract weather conditions, timestamps, and energy prices from the historical fusion data; Using a preset initial deep learning model to perform feature processing on the weather state, the timestamp, and the energy price to obtain a predicted energy demand; The parameters of the initial deep learning model are adjusted based on the predicted energy demand to obtain the target deep learning model.

5. The method according to claim 1, characterized in that The energy planning is performed on the electric power energy data, the natural gas energy data and the water energy data according to the prediction result to obtain energy analysis results, including: In the case where the prediction result is a first energy demand, the electric power energy data, the natural gas energy data and the water energy data are divided according to a first allocation ratio corresponding to the first energy demand to obtain the energy analysis result; When the prediction result is the second energy demand, the electric power energy data, the natural gas energy data and the water energy data are divided according to the second allocation ratio corresponding to the second energy demand to obtain the energy analysis result, wherein the first energy demand is greater than the second energy demand.

6. The method according to claim 5, characterized in that The first allocation ratio is that the electric energy data accounts for a first ratio, the natural gas energy data accounts for a second ratio, and the water energy data accounts for a third ratio, the first ratio is greater than the third ratio, and the third ratio is greater than the second ratio; The second distribution ratio is that the electric power energy data accounts for the fourth ratio, the natural gas energy data accounts for the fifth ratio, and the water energy data accounts for the sixth ratio, and the fourth ratio, the fifth ratio, and the sixth ratio are evenly distributed.

7. The method according to claim 1, characterized in that After fusing the target intermediate key-value pairs to obtain fused data, the method further includes: Encrypting the fused data to obtain fused encrypted data; Energy consumption prediction is performed on the fused encrypted data to obtain a prediction result.

8. An energy fusion analysis device based on big data, characterized in that: The device comprises: An energy data acquisition module (110), used for acquiring cross-category energy data, wherein the cross-category energy data includes electric energy data, natural gas energy data and water energy data; A data extraction module (120) is used to extract information from the electric power energy data, the natural gas energy data and the water energy data, and generate an initial intermediate key-value pair, wherein the initial intermediate key-value pair includes a plurality of initial keys and initial values ​​corresponding to each of the initial keys; A data conversion module (130) is used to perform data type conversion on the initial value corresponding to each initial key to obtain a conversion value corresponding to the initial key, and replace the initial value with the conversion value to obtain a target intermediate key-value pair; A fusion prediction module (140) is used to perform data fusion on the target intermediate key-value pair to obtain fused data, and perform energy consumption prediction on the fused data to obtain a prediction result; The energy data analysis module (150) is used to perform energy planning on the electric power energy data, the natural gas energy data and the water energy data according to the prediction result, so as to obtain an energy analysis result.

9. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503), a communication bus (502) and a network interface (504), wherein the processor (501), the memory (505), the user interface (503) and the network interface (504) are respectively connected to the communication bus (502), the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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