New energy data acquisition and analysis device

Abstract

The invention relates to a new energy data acquisition and analysis device in the technical field of new energy power generation, and the device comprises an acquisition module which is used for collecting the data of new energy equipment in real time; the central processing module is used for receiving, screening and classifying the data acquired by the acquisition module and sending the processed data to the storage module; the storage module comprises a primary storage sub-module and a secondary storage sub-module which are respectively used for storing original data and analyzed result data; the big data basic analysis module obtains the data from the storage module through the calling module, compares and checks the data to generate an analysis result, and feeds back the analysis result to the storage module for storage; a data analysis task can be flexibly executed at a cloud end or an edge end through the edge cloud processing module, so that the data processing efficiency is remarkably improved; and an optimal data transmission path can be selected through the cost degree analysis module, so that the cost of data acquisition and transmission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power generation, and particularly to a new energy data acquisition and analysis device. Background Art

[0002] New energy power generation refers to the process of generating electricity by converting renewable energy in nature using new technologies and materials. These renewable energy sources include solar energy, wind energy, water energy (hydropower), biomass energy, etc.

[0003] With the increasing global demand for clean energy, new energy power generation technology has developed rapidly. However, there are many challenges in data acquisition, processing, and analysis in existing new energy power generation systems, such as high data transmission costs, low data processing efficiency, insufficient system reliability, etc. Therefore, it is particularly important to develop an efficient, reliable, and economical new energy data acquisition and analysis device.

[0004] For this reason, we have designed a new energy data acquisition and analysis device. Summary of the Invention

[0005] In order to overcome the deficiencies in the background art, the present invention discloses a new energy data acquisition and analysis device.

[0006] To achieve the above invention objective, the present invention adopts the following technical solutions: A new energy data acquisition and analysis device, comprising: An acquisition module, configured to acquire data of new energy devices in real time; A central processing module, configured to receive, screen, and classify the data acquired by the acquisition module, and send the processed data to the storage module; A storage module, including a primary storage sub-module and a secondary storage sub-module, respectively configured to store raw data and analyzed result data; A big data basic analysis module, configured to obtain data from the storage module through a call module, compare and calculate the data to generate an analysis result, and then feedback it to the storage module for storage; An edge cloud processing module, configured to execute data analysis tasks in the cloud or at the edge to optimize data processing efficiency and cost; A health degree analysis module, configured to evaluate the operation health degree of each new energy module based on historical data; A cost degree analysis module, configured to evaluate the costs of different data transmission paths and select the optimal communication method.

[0007] Preferably, the central processing module includes: A central processing unit, configured to receive new energy data and send it; A data screening module, which is used to receive new energy data and screen the data to eliminate unreasonable data information; A data classification module, which is used to classify the screened data and send it to the first-level storage sub-module for storage through the data transmission module according to the category.

[0008] Preferably, the big data basic analysis module includes: A data receiving module, which is used to receive new energy data sent by the calling module; A data comparison module, which is used to compare the reference data pre-stored in the storage module with the new energy data; A data calculation module, which is used to calculate the new energy data; A result output module, which is used to send the calculation result to the second-level storage sub-module for storage.

[0009] Preferably, the health analysis module evaluates the operation status of each new energy module based on historical data, identifies potential problems and provides maintenance suggestions.

[0010] Preferably, it further includes an intelligent scheduling algorithm, which is used to dynamically select the cloud or the edge for data processing to balance cost and performance.

[0011] Preferably, the intelligent scheduling algorithm includes the following steps: Step 1: Data collection and preprocessing. Obtain real-time data from the collection module, and conduct preliminary screening and classification, eliminate unreasonable data information, and send the processed data to the first-level storage sub-module of the storage module; Step 2: Health assessment. Based on historical data, evaluate the operation health of each new energy module, identify potential problems and provide maintenance suggestions, and calculate the health value of each module ; Where n is the number of new energy modules with data collection and analysis configured at the edge in the collection control scheme, is the weight set according to the power generation level of each module, is the collection cost of the jth new energy module with data collection and analysis configured at the edge in the collection control scheme; Step 3: Cost analysis. Evaluate the costs of different data transmission paths, select the optimal communication method, and calculate the cost value of each path ; Where m is the number of different data transmission paths, is the transmission time of each path, is the bandwidth cost of each path; Step 4: Optimize the decision-making. According to the optimization function FO, select the optimal data processing location and dynamically adjust the allocation of data processing tasks to ensure the best balance between cost and performance of the system; Among them, ; In the formula, a is an adjustment coefficient used to balance the impacts of health degree and cost degree; Step 5: Execute the task. Execute the data analysis task at the selected location, send the analysis result to the secondary storage sub-module of the storage module, and generate corresponding reports or warning messages.

[0012] Due to the above-mentioned technical solution, the present invention has the following beneficial effects: 1. Through the edge cloud processing module, data analysis tasks can be flexibly executed in the cloud or at the edge, significantly improving the data processing efficiency; 2. Through the cost degree analysis module, the optimal data transmission path can be selected, reducing the costs of data acquisition and transmission; 3. Through the health degree analysis module, potential problems can be discovered in a timely manner, facilitating timely maintenance and improving the stability of the system. Description of the Drawings

[0013] Figure 1 is the structural block diagram of the present invention; Figure 2 is the structural block diagram of the central processing module in the present invention; Figure 3 is the structural block diagram of the big data basic analysis module in the present invention.

[0014] In the figure: 100, acquisition module; 200, central processing module; 210, central processor; 220, data screening module; 230, data classification module; 240, data transmission module; 300, storage module; 400, big data basic analysis module; 410, data receiving module; 420, data comparison module; 430, data calculation module; 440, result output module; 500, call module; 600, edge cloud processing module; 700, health degree analysis module; 800, cost degree analysis module. Detailed Embodiments

[0015] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "rear", "left", "right", etc. indicating the orientation or position relationship, it is only corresponding to the drawings of the present application for the convenience of describing the present invention; it should be understood that if there are terms such as "end", "side", "end part", "side part", "lateral", "longitudinal", etc. indicating the orientation or position relationship, it is only corresponding to the length and width of the corresponding component, that is, the "end part" indicates the head and tail regions in the length direction of the corresponding component, and the "side part" indicates the head and tail regions in the width direction of the corresponding component; it is for the convenience of describing the present invention rather than indicating or implying that the device or element referred to must have a specific orientation.

[0016] Embodiment 1, in combination with the attached Figures 1-3 , a new energy data acquisition and analysis device, comprising: An acquisition module 100, configured to acquire data of new energy devices in real time; In the example, for solar power generation, data such as the orientation information and light intensity of the photovoltaic panel are acquired; for wind power generation, data such as the current orientation information and wind direction information of the wind power equipment are acquired. A central processing module 200, configured to receive, screen, and classify the data acquired by the acquisition module 100, and send the processed data to the storage module 300; Further, the central processing module 200 includes: A central processor 210, configured to receive and send new energy data; A data screening module 220, configured to receive new energy data and screen the data to eliminate unreasonable data information; A data classification module 230, configured to classify the screened data and send it to the first-level storage sub-module for storage according to the category through the data transmission module 240.

[0017] A storage module 300, including a first-level storage sub-module and a second-level storage sub-module, respectively configured to store original data and analyzed result data; In the example, taking solar power generation as an example, the first-level storage sub-module stores the real-time monitoring data of the photovoltaic panel; the second-level storage sub-module stores the result data processed by the big data basic analysis module 400.

[0018] A big data basic analysis module 400, configured to obtain data from the storage module 300 by calling the module 500, compare and calculate the data to generate an analysis result, and then feedback it to the storage module 300 for storage; Further, the big data basic analysis module 400 includes: A data receiving module 410, configured to receive new energy data sent by a calling module 500; A data comparison module 420, configured to compare reference data pre-stored in a storage module 300 with the new energy data; A data calculation module 430, configured to calculate the new energy data; A result output module 440, configured to send the calculation result to a secondary storage sub-module for storage.

[0019] An edge cloud processing module 600, configured to execute data analysis tasks at the cloud or the edge to optimize data processing efficiency and cost; In the example, when the data volume is large and the real-time requirement is not high, cloud processing is selected; when the data volume is small but the real-time requirement is high, edge processing is selected; specifically, it is selected according to the actual collected data and job requirements, and no specific limitation is made here.

[0020] A health analysis module 700, configured to evaluate the operating health of each new energy module based on historical data; Further, the health analysis module 700 also identifies potential problems and provides maintenance suggestions for the evaluated operating health.

[0021] A cost analysis module 800, configured to evaluate the costs of different data transmission paths and select the optimal communication method; Specifically, by comparing the communication costs and latency times of different transmission paths, the most economical and efficient solution is selected.

[0022] An intelligent scheduling algorithm, configured to dynamically adjust the data processing location (cloud / edge) to balance cost and performance.

[0023] The method for collecting and analyzing new energy data using the intelligent scheduling algorithm by the new energy data collection and analysis device is as follows: Real-time monitoring and status determination: For each type of new energy device (such as photovoltaic panels, inverters, wind power generation devices, etc.), the system will collect its working parameters in real time and perform status judgment.

[0024] Data comparison and anomaly detection: Compare the actual data with the pre-stored standard data, find the differences and mark them as anomalies.

[0025] Comprehensive analysis and prediction: Use machine learning algorithms to deeply analyze the data accumulated over a long time, and predict possible future problems or trends of efficiency decline.

[0026] Cost-benefit analysis: Analyze the cost-benefits of different data processing strategies, such as the choice between edge computing and cloud computing, to achieve the optimal allocation of resources Specifically, the intelligent scheduling algorithm includes the following steps: Step 1: Data collection and preprocessing. Obtain real-time data from the collection module 100, perform preliminary screening and classification, eliminate unreasonable data information, and send the processed data to the primary storage sub-module of the storage module 300. By eliminating unreasonable data information, this step improves the quality of the data, reduces the burden of subsequent processing, and provides guarantees for the accuracy and reliability of the system.

[0027] Step 2: Health assessment. Based on historical data, evaluate the operating health of each new energy module, identify potential problems and provide maintenance suggestions, and calculate the health value of each module. ; In the formula, n is the number of new energy modules with data collection and analysis configuration at the edge in the collection control scheme. is the weight set according to the power generation level of each module. is the collection cost degree of the jth new energy module with data collection and analysis configuration at the edge in the collection control scheme. This step can not only timely detect problems that may affect the system performance, but also take measures in advance to avoid the occurrence of faults, thereby extending the service life of the equipment and reducing the maintenance cost.

[0028] Step 3: Cost degree analysis. Evaluate the costs of different data transmission paths, select the optimal communication method, and calculate the cost degree value of each path. ; In the formula, m is the number of different data transmission paths. is the transmission time of each path. is the bandwidth cost of each path. By calculating the cost degree value of each path, the system can select the most economical and effective data transmission path, which helps to reduce the operating cost and ensure the speed and efficiency of data transmission at the same time.

[0029] Step 4: Optimization decision-making. According to the optimization function FO, select the optimal data processing location and dynamically adjust the allocation of data processing tasks to ensure the best balance between cost and performance of the system. Among them, ; In the formula, a is an adjustment coefficient used to balance the influence of health degree and cost degree. This step enables the new energy data collection and analysis device to minimize costs as much as possible while meeting the performance requirements, and realizes the effective utilization of resources.

[0030] It should be noted that when the new energy data to be collected and analyzed includes biomass power generation data, the new energy data collection and analysis device preferably has a safety protection module, which monitors the proximity of people in the protection area established for biomass power generation and prompts personnel to wear protective equipment. For example, through image recognition technology (a camera with voice function), it monitors whether the personnel entering the protection area are wearing the necessary protective equipment, and gives voice prompts and warnings to those who are not wearing the necessary protective equipment.

[0031] The parts not detailed in the present invention are prior art. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and are intended to cover all changes falling within the meaning and scope of the equivalent elements within the present invention.

Claims

1. A new energy data collection and analysis device, characterized in that: include: The acquisition module is used to collect data of new energy equipment in real time; A central processing module, used to receive, filter and classify the data collected by the collection module, and send the processed data to the storage module; The storage module includes a primary storage submodule and a secondary storage submodule, which are used to store the original data and the analyzed result data respectively; The big data basic analysis module obtains data from the storage module by calling the module, compares and calculates the data to generate analysis results, and then feeds them back to the storage module for storage; Edge cloud processing module, used to perform data analysis tasks in the cloud or edge to optimize data processing efficiency and cost; Health analysis module, which evaluates the operating health of each new energy module based on historical data; The cost analysis module evaluates the costs of different data transmission paths and selects the optimal communication method.

2. The new energy data collection and analysis device according to claim 1, characterized in that: The central processing module comprises: A central processing unit, used to receive new energy data and send it; Data screening module, used to receive new energy data and screen the data to eliminate unreasonable data information; The data classification module is used to classify the filtered data and send it to the first-level storage submodule for storage through the data transmission module according to the category.

3. The new energy data collection and analysis device according to claim 1, characterized in that: The big data basic analysis module includes: A data receiving module, used for receiving new energy data sent by the calling module; A data comparison module, used to compare the reference data pre-stored in the storage module with the new energy data; Data accounting module, used to calculate new energy data; The result output module is used to send the calculation results to the secondary storage submodule for storage.

4. The new energy data collection and analysis device according to claim 1, characterized in that: The health analysis module evaluates the operating status of each new energy module based on historical data, identifies potential problems and provides maintenance suggestions.

5. The new energy data collection and analysis device according to claim 1 is characterized in that: It also includes intelligent scheduling algorithms that dynamically select the cloud or edge for data processing to balance cost and performance.

6. The new energy data collection and analysis device according to claim 5, characterized in that: The intelligent scheduling algorithm comprises the following steps: Step 1: Data collection and preprocessing: obtaining real-time data from the collection module, and performing preliminary screening and classification, eliminating unreasonable data information, and sending the processed data to the primary storage submodule of the storage module; Step 2: Health assessment: Based on historical data, evaluate the operating health of each new energy module, identify potential problems and provide maintenance suggestions, and calculate the health value of each module. ; Where n is the number of new energy modules configured at the edge for data collection and analysis in the acquisition control solution. It is the weight set according to the power generation level of each module. is the acquisition cost of the jth new energy module configured at the edge for data acquisition and analysis in the acquisition control scheme; Step 3: Cost analysis, evaluate the cost of different data transmission paths, select the optimal communication method, and calculate the cost value of each path ; Where m is the number of different data transmission paths, is the transmission time of each path, is the bandwidth cost of each path; Step 4: Optimize decision-making. According to the optimization function FO, select the optimal data processing location and dynamically adjust the allocation of data processing tasks to ensure the best balance between cost and performance of the system. in, ; In the formula, a is the adjustment coefficient, which is used to balance the influence of health and cost; Step 5: Task execution: execute the data analysis task at the selected location, send the analysis result to the secondary storage submodule of the storage module, and generate corresponding report or warning information.

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