A 3D visualized park energy consumption management system based on big data analysis

Abstract

The application discloses a 3D visual park energy consumption management system based on big data analysis, and relates to the technical field of park energy consumption management.The 3D visual park energy consumption management system based on big data analysis comprises a data partition storage management module, an energy consumption feature extraction management module and a superimposed visualization management module.The application quantifies the concurrent read-write efficiency of the data partition storage process of park energy consumption data, quantifies the energy consumption feature extraction rate of the energy consumption feature extraction process of the park energy consumption data after the concurrent read-write is qualified, quantifies the superimposed visualization efficiency of the display superposition process of the park energy consumption data after the energy consumption feature extraction is qualified, simultaneously performs visualization parameter optimization determination, and thus improves the 3D visual synchronous management efficiency of the park energy consumption data in the park big data platform, and solves the problem of low 3D visual synchronous management efficiency of the park energy consumption data in the park big data platform in the prior art.

Description

Technical Field

[0001] This invention relates to the field of park energy management technology, and in particular to a 3D visualization park energy management system based on big data analysis. Background Technology

[0002] With the acceleration of urbanization and the continuous expansion of industrial parks, energy management in these parks faces increasingly severe challenges. Energy conservation, emission reduction, and green development have become important themes in contemporary society. As a significant energy-consuming sector, industrial parks have received considerable attention for their energy management. Traditional energy management models for industrial parks have many drawbacks and are insufficient to meet the demands of modern parks for efficient, precise, and intelligent energy management. Against this backdrop, the rise of big data platforms and 3D visualization technology has brought new solutions to energy management in industrial parks.

[0003] Existing technologies leverage big data to deeply mine and analyze multi-dimensional energy consumption data collected in real time, identify energy consumption patterns and problems, and then use 3D visualization technology to intuitively integrate and present the park's buildings, equipment, and energy consumption data, thereby achieving dynamic energy consumption monitoring, precise anomaly location, and scientific decision-making management.

[0004] For example, the invention patent announcement CN118446431B discloses an energy consumption visualization and controllable optimization management system applicable to smart parks, which includes: dividing the smart park into several areas according to a preset monitoring area and marking them sequentially as monitoring areas, while generating data acquisition instructions; acquiring energy consumption information of the monitoring areas in real time and sending the energy consumption information to the energy consumption display module and the optimization management platform; establishing a coordinate system with time as the horizontal axis and energy consumption information as the vertical axis to obtain an energy consumption map; and sounding an energy consumption anomaly alarm after receiving an energy consumption deviation instruction.

[0005] For example, patent application CN115796454A discloses a 3D visualization management method and system based on smart parks, which includes: acquiring information on IoT devices and employees in the park, and building an online management platform for device visualization; establishing an online communication network; synchronizing device operation commands to the online management platform for device visualization; constructing a 3D visualization management platform for the park after the device operation commands are synchronized to the online management platform for device visualization; recording employee ID cards, and combining the 3D visualization management platform for the park to perform visualized management.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, during peak periods of the park's network, a large number of devices may access the internet simultaneously, which may lead to network congestion, affecting the normal transmission and visualization of energy consumption data. This can cause the visualization interface to flicker, lag, or even lose data. Furthermore, existing technologies do not dynamically optimize for performance bottlenecks in the visualization process, resulting in an inability to adaptively improve efficiency. Consequently, there is a problem of low efficiency in the synchronous 3D visualization management of park energy consumption data on the park's big data platform. Summary of the Invention

[0008] This application provides a 3D visualization-based park energy consumption management system based on big data analysis, which solves the problem of low efficiency in the synchronous 3D visualization management of park energy consumption data on the park big data platform in the prior art, and improves the efficiency of synchronous 3D visualization management of park energy consumption data on the park big data platform.

[0009] This application provides a 3D visualization-based park energy consumption management system based on big data analysis, including: a data partitioning and storage management module, an energy consumption feature extraction management module, and an overlay visualization management module. The data partitioning and storage management module is used to partition and store acquired park energy consumption data through a preset park big data platform, and simultaneously quantifies the concurrent read / write efficiency of the data partitioning and storage process based on the acquired concurrent read / write energy consumption data. The energy consumption feature extraction management module is used to perform spatial parameter optimization judgment based on the acquired concurrent read / write efficiency quantification results. After the concurrent read / write is qualified, it quantifies the energy consumption feature extraction rate of the park energy consumption data based on the acquired feature extraction energy consumption data. The spatial parameter optimization judgment is used to determine whether... Whether increasing the remaining capacity of the storage medium and the frequency of the central processing unit can improve the concurrent read and write efficiency of the park's big data; the overlay visualization management module is used to optimize the disk I / O operation frequency based on the obtained energy consumption feature extraction rate quantification results. After the energy consumption feature extraction is qualified, the overlay visualization efficiency of the park's energy consumption data overlay process is quantified based on the obtained overlay visualization efficiency data. At the same time, the visualization parameter optimization is determined based on the obtained overlay visualization efficiency quantification results. The disk I / O operation frequency optimization determination is used to determine whether to improve the energy consumption feature extraction efficiency of the park's big data by reducing the disk I / O operation frequency. The visualization parameter optimization determination is used to determine whether to improve the overlay visualization efficiency of the park's big data platform by adjusting the process I / O priority and visualization frame rate.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0011] 1. By quantifying the concurrent read / write efficiency of the data partitioning storage process for park energy consumption data, and after the concurrent read / write is qualified, the energy consumption feature extraction process of the park energy consumption data is quantified by the energy consumption feature extraction rate. After the energy consumption feature extraction is qualified, the overlay visualization process of the park energy consumption data is quantified by the overlay visualization efficiency. At the same time, visualization parameter optimization judgment is performed, covering the entire process of data storage, feature extraction, and visualization rendering. This achieves continuous optimization of the performance and experience of the park big data platform, thereby improving the efficiency of 3D visualization synchronous management of park energy consumption data in the park big data platform. This effectively solves the problem of low efficiency in 3D visualization synchronous management of park energy consumption data in the existing technology.

[0012] 2. By processing the acquired concurrent read / write energy consumption data with corresponding set values, the ratios of data read / write power consumption, partition key response time, and synchronous locking cumulative time are obtained. Simultaneously, the results of the coupled calculations of the acquired data read / write power consumption ratio, partition key response time ratio, and synchronous locking cumulative time ratio are inversely proportionalized to obtain the quantified value of concurrent read / write efficiency. This improves the accuracy of obtaining the quantified value of concurrent read / write efficiency, thereby enabling a more accurate assessment of the concurrent read / write efficiency of the park's big data platform in access hotspot areas.

[0013] 3. By obtaining the number of energy consumption features extracted at the end of the energy consumption feature extraction process on the park's big data platform, if the number of energy consumption features extracted is not less than the number of energy consumption features extracted as set in the database, a data extraction anomaly command is sent; otherwise, the offline batch analysis time and energy consumption feature extraction time of the park's big data platform at the end of the energy consumption feature extraction process are obtained. Based on the ratio of the obtained offline batch analysis time and energy consumption feature extraction time, a quantitative value of the energy consumption feature extraction rate is obtained, thereby improving the accuracy of obtaining the quantitative value of the energy consumption feature extraction rate and achieving a more accurate assessment of the energy consumption feature extraction efficiency of the park's energy consumption data.

[0014] 4. By processing the acquired display and overlay energy consumption data with the corresponding set values, the ratios of the visual dynamic adjustment time, the synchronous update time, and the synchronous overlay time are obtained. At the same time, the acquired ratios of the visual dynamic adjustment time, the synchronous update time, and the synchronous overlay time are coupled and calculated to obtain the quantitative value of the overlay visualization efficiency. This improves the accuracy of obtaining the quantitative value of the overlay visualization efficiency, and enables the park's big data platform to more accurately evaluate the display and overlay efficiency of the park's energy consumption data. Attached Figure Description

[0015] Figure 1 A schematic diagram of the structure of a 3D visualization park energy consumption management system based on big data analysis provided in an embodiment of this application;

[0016] Figure 2 A flowchart illustrating the quantification of the efficiency of partitioned storage and concurrent read / write of energy consumption data in a park, as provided in this embodiment of the application.

[0017] Figure 3 A flowchart for the optimization determination of processing space parameters and the quantification of energy consumption feature extraction rate provided in the embodiments of this application;

[0018] Figure 4 A flowchart for determining and superimposing visualized efficiency quantification of disk I / O operation frequency optimization is provided in the embodiments of this application;

[0019] Figure 5 This is one of the real-time display and management interface diagrams of park energy consumption data provided in the embodiments of this application;

[0020] Figure 6 The second diagram shows the real-time display and management interface for park energy consumption data provided in this application embodiment;

[0021] Figure 7 This is a diagram of the energy consumption feature extraction and management interface provided in an embodiment of this application;

[0022] Figure 8 This is one of the park energy consumption data distribution and visualization management interface diagrams provided in the embodiments of this application;

[0023] Figure 9 The second diagram shows the distribution and visualization management interface for park energy consumption data provided in this application embodiment. Detailed Implementation

[0024] This application provides a 3D visualization-based park energy consumption management system based on big data analytics. This addresses the problem of low efficiency in the synchronous 3D visualization management of park energy consumption data within a park big data platform, as described in the prior art. The system utilizes a data partitioning and storage management module to partition and store the acquired park energy consumption data according to a pre-defined park big data platform. Simultaneously, it quantifies the concurrent read / write efficiency of the partitioned storage process based on the acquired concurrent read / write energy consumption data. Then, an energy consumption feature extraction management module optimizes processing space parameters based on the quantified concurrent read / write efficiency results. After successful concurrent read / write, it quantifies the energy consumption feature extraction rate based on the acquired feature extraction energy consumption data. Finally, an overlay visualization management module optimizes disk I / O operation frequency based on the quantified energy consumption feature extraction rate results. After successful energy consumption feature extraction, it quantifies the overlay visualization efficiency of the overlay process based on the acquired display overlay energy consumption data, and optimizes visualization parameters based on the quantified overlay visualization efficiency results. This significantly improves the efficiency of synchronous 3D visualization management of park energy consumption data within the park big data platform.

[0025] The technical solution in this application embodiment aims to address the problem of low efficiency in synchronous 3D visualization management of park energy consumption data on the park's big data platform. The overall approach is as follows:

[0026] By quantifying the concurrent read and write efficiency of the data partitioning storage process of the park's energy consumption data, and after the concurrent read and write is qualified, the energy consumption feature extraction process of the park's energy consumption data is quantified by the energy consumption feature extraction rate, and after the energy consumption feature extraction is qualified, the overlay visualization efficiency of the park's energy consumption data is quantified by the overlay visualization process. At the same time, the visualization parameter optimization judgment is performed, which achieves the effect of improving the efficiency of 3D visualization synchronous management of park energy consumption data in the park's big data platform.

[0027] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1 The diagram shown is a structural schematic of a 3D visualization park energy consumption management system based on big data analysis provided in this application embodiment. The 3D visualization park energy consumption management system based on big data analysis provided in this application embodiment includes: a data partition storage management module, an energy consumption feature extraction management module, and an overlay visualization management module.

[0029] The data partitioning and storage management module is used to partition and store the acquired park energy consumption data through a preset park big data platform. At the same time, it quantifies the concurrent read and write efficiency of the data partitioning and storage process of park energy consumption data based on the acquired concurrent read and write energy consumption data, and obtains the concurrent read and write efficiency quantification result. The preset park big data platform stores the park energy consumption data acquired after edge gateway preprocessing. The concurrent read and write efficiency quantification is used to quantify the concurrent read and write performance of the data partitioning and storage process of park energy consumption data in concurrent read and write scenarios. The concurrent read and write energy consumption data includes the data read and write power consumption of the corresponding access hotspot area, partition key response time, and cumulative synchronization lock duration of the park big data platform at the end of the data partitioning and storage period.

[0030] The energy consumption feature extraction management module is used to optimize and determine the processing space parameters based on the acquired concurrent read and write efficiency quantification results. After the concurrent read and write is qualified, the module quantifies the energy consumption feature extraction rate of the energy consumption feature extraction process of the park's energy consumption data based on the acquired feature extraction energy consumption data, and obtains the energy consumption feature extraction rate quantification result. The processing space parameter optimization and determination is used to determine whether to improve the concurrent read and write efficiency of the park's big data by increasing the remaining capacity of the storage medium and the frequency of the central processing unit. The energy consumption feature extraction rate quantification is used to quantify the energy consumption extraction performance of the energy consumption feature extraction process of the park's energy consumption data in the energy consumption extraction scenario. The feature extraction energy consumption data includes the number of energy consumption features extracted, the offline batch analysis time, and the energy consumption feature extraction time.

[0031] The overlay visualization management module is used to optimize disk I / O operation frequency based on the obtained energy consumption feature extraction rate quantification results. After the energy consumption feature extraction is qualified, the module quantifies the overlay visualization efficiency of the overlay process of the park's energy consumption data based on the obtained overlay energy consumption data, and obtains the overlay visualization efficiency quantification results. At the same time, the module optimizes visualization parameters based on the obtained overlay visualization efficiency quantification results. The disk I / O operation frequency optimization determination is used to determine whether to improve the energy consumption feature extraction efficiency of the park's big data by reducing the disk I / O operation frequency. The overlay visualization efficiency quantification is used to quantify the overlay display performance of the overlay process of the park's energy consumption data in the overlay scenario. The visualization parameter optimization determination is used to determine whether to improve the overlay visualization efficiency of the park's big data platform by adjusting the process I / O priority and visualization frame rate. The overlay energy consumption data includes the visualization dynamic adjustment duration, the synchronous update duration, and the synchronous overlay duration.

[0032] The park's energy consumption data includes, but is not limited to, energy consumption from buildings, equipment, lighting, air conditioning, and solar energy. After being preprocessed by the edge gateway, the acquired park energy consumption data is uploaded to the park's big data platform in a unified manner according to timestamps and spatial tags. At the same time, the park's big data platform uses distributed storage technology to partition and store the data. The park's big data platform has the ability to monitor park energy consumption data in real time and displays concurrent read and write energy consumption data, feature-extracted energy consumption data, and overlay display energy consumption data to park managers in the form of intuitive charts, reports, etc. This enables managers to understand the real-time status of park energy consumption at any time, promptly detect abnormalities, and take corresponding measures.

[0033] 3D visualization is a technology that presents three-dimensional spatial information, data, or models in an intuitive graphical way. In a 3D visualization-based energy management system for industrial parks, built on big data analytics, 3D visualization plays a crucial role. By constructing a virtual 3D model of the park, it transforms abstract energy consumption data into intuitive visual information, thereby helping users understand, analyze, and make decisions more efficiently. The process of adjusting, displaying, and overlaying refers to: adjusting—dynamically adjusting the visualization resources of the display module based on the calculation and analysis results; displaying—adjusting and preparing the resources of the display module; overlaying—importing the processed data and overlaying it with the 3D park model; and displaying—showing the overlaid result in the mobile 3D visualization engine.

[0034] like Figure 2 The diagram shows a flowchart of the partitioned storage and concurrent read / write efficiency quantification of park energy consumption data provided in this application embodiment. The specific process logic is as follows: it describes the process of obtaining park energy consumption data from a preset park big data platform, performing data partitioned storage after preprocessing by an edge gateway, and then obtaining concurrent read / write energy consumption data and performing concurrent read / write efficiency quantification.

[0035] like Figure 3 The diagram shows a flowchart of the processing space parameter optimization judgment and energy consumption feature extraction rate quantification provided in the embodiment of this application. The specific process logic is as follows: starting from obtaining the concurrent read and write efficiency quantification result, it is judged whether the concurrent read and write is qualified. If it is not qualified, the remaining capacity of the storage medium and the CPU frequency are increased, and the result is obtained again. If it is qualified, the processing space parameter optimization judgment is entered to evaluate whether further parameter adjustment is needed. After the judgment is passed, the concurrent read and write is qualified, and the stage of obtaining feature extraction energy consumption data is entered. Finally, the energy consumption feature extraction rate quantification is performed.

[0036] like Figure 4The diagram shows a flowchart of disk I / O operation frequency optimization determination and overlay visualization efficiency quantification provided in this application embodiment. The specific process logic is as follows: after obtaining the energy consumption feature extraction rate quantification result, the energy consumption feature extraction qualification determination is performed. If it is not qualified, the disk I / O operation frequency optimization determination is performed (reducing the disk I / O operation frequency) until the energy consumption feature extraction is qualified. If it is qualified, the display overlay energy consumption data is obtained and the overlay visualization efficiency quantification is performed. Finally, the visualization parameter optimization determination is performed (adjusting the process I / O priority and visualization frame rate).

[0037] In this embodiment, during the data storage phase, the data partitioning storage management module can accurately grasp the performance of the storage process under concurrent read / write scenarios, promptly identify potential problems, and optimize processing space parameters in a targeted manner through quantification results. This ensures efficient and stable data storage, providing a solid foundation for subsequent processing and avoiding the impact of storage delays on overall management efficiency. After the concurrent read / write operation is deemed satisfactory, the energy consumption feature extraction management module quantifies the energy consumption feature extraction rate based on the extracted energy consumption data. This helps to accurately evaluate the performance of the energy consumption feature extraction process, thereby quickly and accurately obtaining energy consumption features and providing strong support for subsequent analysis and decision-making. The overlay visualization management module makes the 3D visualization display of park energy consumption data smoother and more efficient. Managers can understand the park's energy consumption situation in real time and intuitively, make quick decisions, and effectively improve the synchronicity and efficiency of park energy consumption management. This significantly improves the efficiency of 3D visualization synchronous management of park energy consumption data on the park's big data platform.

[0038] Furthermore, based on the acquired concurrent read / write energy consumption data, the concurrent read / write efficiency of the data partition storage process for the park's energy consumption data is quantified. The specific steps are as follows: the acquired concurrent read / write energy consumption data is proportionally processed with the corresponding set values ​​to obtain the data read / write power consumption ratio, the partition key response time ratio, and the synchronous locking cumulative time ratio. At the same time, the results of the coupled calculation of the acquired data read / write power consumption ratio, partition key response time ratio, and synchronous locking cumulative time ratio are inversely proportionally calculated to obtain the quantified value of concurrent read / write efficiency. The access hotspot area represents the concurrent read / write area corresponding to the data access frequency acquired during the data partition storage period being greater than the data access frequency set in the database.

[0039] The data read / write power consumption ratio represents the result of a compensation calculation on the proportion of the acquired data read / write power consumption relative to the set data read / write power consumption. The specific constraint expression for the data read / write power consumption ratio SDH is as follows: In the formula, SDH represents the ratio of data read / write power consumption of the park's big data platform in the corresponding hotspot area at the end of the data partition storage period, d1 represents the data read / write power consumption compensation value, SDH1 represents the data read / write power consumption of the park's big data platform in the corresponding hotspot area at the end of the data partition storage period, and SDH0 represents the set data read / write power consumption. The units of the data read / write power consumption and the set data read / write power consumption are the same, both being watts (W). The set data read / write power consumption is represented by the sum and average of the historical data read / write power consumption of the park's big data platform in the corresponding hotspot area at the end of the historical data partition storage period in the database.

[0040] The partition key response time ratio represents the result of a compensation calculation performed on the data partition key response time compensation value to compensate for the proportion of the acquired partition key response time relative to the set partition key response time. The specific constraint expression for the partition key response time ratio FJX is as follows: In the formula, FJX represents the ratio of the partition key response time of the corresponding access hotspot area at the end of the data partition storage period of the park big data platform, d2 represents the partition key response time compensation value, FJX1 represents the partition key response time of the corresponding access hotspot area at the end of the data partition storage period of the park big data platform, and FJX0 represents the set partition key response time. The unit of the partition key response time and the set partition key response time is the same, which is milliseconds (ms). The set partition key response time is represented by the sum and average of the historical partition key response times of the corresponding access hotspot area at the end of the historical data partition storage period of the park big data platform in the database.

[0041] The cumulative duration ratio of synchronous locking represents the result of a compensation calculation on the proportion of the acquired cumulative duration of synchronous locking relative to the set cumulative duration of synchronous locking. The specific constraint expression for the cumulative duration ratio of synchronous locking TSJ is as follows: In the formula, TSJ represents the ratio of the cumulative synchronization and locking duration of the access hotspot area corresponding to the end of the data partition storage period of the park's big data platform, d3 represents the compensation value of the cumulative synchronization and locking duration, TSJ1 represents the cumulative synchronization and locking duration of the access hotspot area corresponding to the end of the data partition storage period of the park's big data platform, and TSJ0 represents the set cumulative synchronization and locking duration. The units of the cumulative synchronization and locking duration and the set cumulative synchronization and locking duration are the same, both being milliseconds (ms). The set cumulative synchronization and locking duration is represented by the sum and average of the historical cumulative synchronization and locking durations of the access hotspot area corresponding to the end of the historical data partition storage period of the park's big data platform.

[0042] The concurrent read / write efficiency quantification value is used to reflect the degree to which concurrent read / write energy consumption data quantifies the concurrent read / write efficiency of the park's big data platform in access hotspot areas. The specific constraint expression for the concurrent read / write efficiency quantification value SFT is as follows: In the formula, SDT represents the quantified value of concurrent read and write efficiency of the hot spot area accessed by the park's big data platform at the end of the data partition storage period.

[0043] The aforementioned database is a database established before the design of a 3D visualization park energy consumption management system based on big data analysis to store various set data. The database includes, but is not limited to, set concurrent read / write efficiency quantification values, set energy consumption feature extraction rate quantification values, set overlay visualization efficiency quantification values, data partition storage periods, and display overlay periods. Various values ​​are directly set by technical personnel. The setting of concurrent read / write efficiency quantification values ​​can be determined based on the actual application scenario of the park big data platform. For example, the set concurrent read / write efficiency quantification values ​​can be represented by the sum and average of the historical concurrent read / write efficiency quantification values ​​of the park big data platform at the end of the historical data partition storage period in the database. In addition, various values ​​in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0044] In this example, the data read / write power consumption compensation value, data partition key response time compensation value, and synchronous locking cumulative time compensation value represent the degree of impact of pre-set data read / write power consumption, data partition key response time, and synchronous locking cumulative time on the data partitioning and storage process of the park's big data platform. Specifically, the database stores preset compensation values ​​corresponding to data read / write power consumption, data partition key response time, and synchronous locking cumulative time. These compensation values ​​have a pre-defined mapping relationship with these values; this mapping relationship can be one-to-one or many-to-one. For example, in practical applications, real-time data read / write power consumption, data partition key response time, and synchronous locking cumulative time can be input into this mapping relationship to quickly obtain the corresponding compensation values.

[0045] In this example, the values ​​of data read / write power consumption compensation, data partition key response time compensation, and synchronous locking cumulative time compensation are typically between 0 and 1, and the sum of the three is 1.

[0046] In this embodiment, the quantified value of concurrent read / write efficiency decreases as the power consumption of data read / write, partition key response time, and cumulative synchronization lock duration increase. Increased power consumption of data read / write often means that the system needs to consume more energy when processing data read / write operations. This may be due to increased data access volume, increased storage device load, etc. This increase in power consumption usually triggers a series of chain reactions. On the one hand, it may cause the storage device temperature to rise, thereby affecting the device's stability and performance, leading to a longer partition key response time. On the other hand, increased power consumption may also reflect intensified resource competition within the system, causing the synchronization lock operation to wait longer to complete, thus increasing the cumulative duration.

[0047] The extended response time of partition keys may be caused by uneven data distribution, unreasonable partitioning strategies, or insufficient optimization of the index structure. This will increase the overall time of data read and write operations because the system needs to spend more time to find the target data, which further increases the system load and may lead to a further increase in data read and write power consumption. It will also increase the time for synchronous locking operations to wait for other operations to complete, thus increasing the cumulative duration of synchronous locking.

[0048] An increase in the cumulative duration of synchronization lock indicates that during concurrent read and write operations, the competition and synchronization of shared resources among multiple threads or processes become more complex and time-consuming. This directly leads to delays in data read and write operations, reducing the number of data read and write operations that the system can complete per unit of time, thereby reducing concurrent read and write efficiency. It also indirectly affects data read and write power consumption and partition key response time, because operation delays will increase the overall system runtime and resource occupation time.

[0049] By considering the aforementioned indirect impact mechanisms, it is helpful to comprehensively and deeply understand the performance bottlenecks of park energy consumption data in concurrent read and write scenarios. Based on these interrelationships, managers can optimize the system in a targeted manner, thereby effectively improving the quantitative value of concurrent read and write efficiency. This effectively solves the problem of low efficiency in 3D visualization and synchronous management of park energy consumption data in the park's big data platform in existing technologies.

[0050] Furthermore, based on the obtained concurrent read / write efficiency quantification results, space parameter optimization is performed. Specifically, if the obtained concurrent read / write efficiency quantification results meet the first judgment condition, the concurrent read / write efficiency quantification results obtained at the end of the data partition storage period are recorded as qualified, and energy consumption feature extraction rate quantification is performed. If the obtained concurrent read / write efficiency quantification results meet the second judgment condition, the concurrent read / write efficiency quantification results obtained at the end of the data partition storage period are recorded as unqualified, and storage medium remaining capacity optimization and storage medium remaining capacity optimization result judgment are performed until concurrent read / write is qualified. The first judgment condition indicates that the obtained concurrent read / write efficiency quantification value is not greater than the concurrent read / write efficiency quantification value set in the database. The second judgment condition indicates that the obtained concurrent read / write efficiency quantification value is greater than the concurrent read / write efficiency quantification value set in the database. The storage medium remaining capacity optimization result judgment is used to determine whether to perform central processing unit frequency optimization.

[0051] The optimization of remaining storage capacity involves the following steps: The obtained deviations in concurrent read / write efficiency and storage medium consumption are used as the increase in remaining storage capacity to correct disk read / write seek responses. The deviation in concurrent read / write efficiency is used to quantify the difference between the obtained and preset concurrent read / write efficiency values, i.e., the difference between the obtained and preset concurrent read / write efficiency values. The storage medium consumption deviation represents the difference between the storage medium consumption in the corresponding big data platform at the end of the data partition storage period and the preset storage medium consumption in the database. The difference in storage medium consumption is represented by the average of the historical storage medium consumption in the corresponding park big data platform at the end of the historical data partition storage period in the database. The concurrent read / write efficiency quantification value obtained after the remaining storage medium capacity is increased is compared with the concurrent read / write efficiency quantification value obtained before the remaining storage medium capacity is increased. The difference comparison result represents the difference between the deviation of the concurrent read / write efficiency quantification value obtained before the remaining storage medium capacity is increased and the deviation of the concurrent read / write efficiency quantification value obtained after the remaining storage medium capacity is increased.

[0052] The determination of the storage medium remaining capacity optimization result involves the following steps: If the obtained difference comparison result is greater than the difference comparison result set in the database, the storage medium remaining capacity optimization is completed and the energy consumption feature extraction rate is quantified; otherwise, the CPU frequency optimization is performed. The CPU frequency optimization is used to improve the CPU's task switching response based on the obtained CPU frequency increase. The CPU frequency increase represents the deviation of the concurrent read / write efficiency quantization value and the CPU utilization deviation re-obtained by the PID control algorithm in the CPU after the storage medium remaining capacity is increased, in order to correct the CPU's processing rate deviation of the park's energy consumption data.

[0053] In this embodiment, the increase in remaining storage medium capacity refers to the control increment in the PID (Proportional-Integral-Derivative Control) algorithm. The PID control algorithm calculates the adjustment amount of the remaining storage medium capacity based on the deviation of the input concurrent read / write efficiency quantization value and the deviation of storage medium consumption. This adjustment is used to correct the disk's read / write seek response, reducing read / write latency caused by insufficient storage space or unreasonable distribution, thereby improving concurrent read / write efficiency. The increase in CPU frequency refers to the control output in the PID control algorithm. The PID control algorithm calculates the adjustment amount of the CPU frequency based on the deviation of the input concurrent read / write efficiency quantization value and the CPU utilization deviation. This adjustment is used to correct the CPU's processing rate deviation for campus energy consumption data, improving task switching response speed and correcting processing rate deviations, thereby enhancing overall system performance.

[0054] This example, by setting first and second judgment conditions, can quickly determine whether the concurrent read and write efficiency meets the standard. When the concurrent read and write efficiency is unqualified, the remaining capacity of the storage medium is optimized. Based on the deviation of the quantified value of concurrent read and write efficiency and the deviation of storage medium consumption, the remaining capacity of the storage medium is reasonably increased, the disk read and write seek response is corrected, and the read and write latency caused by insufficient storage space or unreasonable distribution is effectively reduced. This enables the system to handle a large number of concurrent data read and write requests more efficiently, and ensures the fast and stable storage and retrieval of campus energy consumption data.

[0055] In the process of determining the optimization results of the remaining storage capacity, the subsequent operations are flexibly decided based on the difference comparison results. If the optimization effect meets expectations, the process directly enters the energy consumption feature extraction rate quantification stage to avoid unnecessary resource waste. If the optimization effect does not meet expectations, the CPU frequency is optimized in a timely manner. The CPU frequency is precisely adjusted through the PID control algorithm to improve the task switching response speed, correct the deviation in the processing rate of campus energy consumption data, realize the rational allocation and efficient utilization of CPU resources, and further improve the overall system performance.

[0056] This optimization mechanism enables the system to automatically adjust parameters based on actual operating conditions, enhancing the system's stability and adaptability. At the same time, it provides a solid foundation for the future growth of energy consumption data in the park and the expansion of business needs, ensuring that the system can maintain efficient and stable operation when facing complex and ever-changing scenarios. It effectively solves the problems of low efficiency in park energy consumption data management and insufficient resource utilization in existing technologies.

[0057] Furthermore, based on the acquired energy consumption data, the energy consumption feature extraction process of the park's energy consumption data is quantified by the energy consumption feature extraction rate. The specific steps are as follows: First, obtain the number of energy consumption features extracted by the park's big data platform at the end of the energy consumption feature extraction process. If the number of energy consumption features extracted is not less than the number of energy consumption features extracted as set in the database, a data extraction exception command is sent. Otherwise, obtain the offline batch analysis duration and energy consumption feature extraction duration of the park's big data platform at the end of the energy consumption feature extraction process. The set number of energy consumption features extracted is represented by the sum and average of the historical energy consumption feature extraction durations of the park's big data platform at the end of historical energy consumption feature extraction processes in the database. Based on the ratio processing of the acquired offline batch analysis duration and energy consumption feature extraction duration, the quantified value of the energy consumption feature extraction rate is obtained, which is the ratio of the energy consumption feature extraction duration to the offline batch analysis duration. The quantified value of the energy consumption feature extraction rate is used to quantify the time proportion of the energy consumption feature extraction process in the entire offline batch analysis process.

[0058] The process involves optimizing disk I / O operation frequency based on the obtained energy consumption feature extraction rate quantization results. Specifically, if the obtained energy consumption feature extraction rate quantization value is not greater than the set energy consumption feature extraction rate quantization value in the database, the obtained energy consumption feature extraction rate quantization result is recorded as qualified and overlaid with visual efficiency quantization. The set energy consumption feature extraction rate quantization value is represented by the sum and average of historical energy consumption feature extraction rate quantization values ​​from the park's big data platform at the end of historical energy consumption feature extraction. If the obtained energy consumption feature extraction rate quantization value is greater than the set energy consumption feature extraction rate quantization value in the database, the obtained energy consumption feature extraction rate quantization result is recorded as unqualified and disk I / O operation frequency optimization is performed until energy consumption feature extraction is qualified. Disk I / O operation frequency optimization is based on the obtained disk I / O operation... The frequency reduction amount decreases the number of disk read and write requests to reduce latency caused by data queue congestion. The disk I / O operation frequency reduction amount represents the sum of the deviation of the quantized value of the energy consumption feature extraction rate and the deviation of the disk I / O operation data volume as the disk I / O operation frequency correction value, in order to correct the utilization deviation of the disk in the process of processing park energy consumption data. The deviation of the quantized value of the energy consumption feature extraction rate represents the difference between the quantized value of the energy consumption feature extraction rate and the set quantized value of the energy consumption feature extraction rate. The deviation of the disk I / O operation data volume represents the difference between the disk I / O operation data volume of the park big data platform at the end of energy consumption feature extraction and the disk I / O operation data volume set in the database. The set disk I / O operation data volume is represented by the sum and average of the historical disk I / O operation data volumes of the park big data platform at the end of historical energy consumption feature extraction in the database.

[0059] In this embodiment, the disk I / O operation frequency correction value refers to the disk utilization rate in the load balancing algorithm. By adjusting the number of disk read and write requests, the load balancing algorithm can reduce the congestion of the data queue and prevent a disk from becoming overloaded due to too many requests. While ensuring load balancing, it makes full use of disk resources, further optimizes the disk I / O operation frequency, and thus improves the efficiency of disk processing campus energy consumption data.

[0060] This example uses the ratio of offline batch analysis time to energy consumption feature extraction time to obtain a quantified value of energy consumption feature extraction rate. This accurately quantifies the time proportion of the energy consumption feature extraction process within the entire offline batch analysis process, providing a reliable basis for subsequent optimization. Existing load balancing algorithms only consider the data volume deviation of disk I / O operations, thus correcting the resulting disk I / O operation frequency deviation. This city-wide approach comprehensively considers both the quantified value deviation of energy consumption feature extraction rate and the data volume deviation of disk I / O operations, inputting these into the load balancing algorithm to obtain a reduction in disk I / O operation frequency. This reduces the number of disk read / write requests, lowers latency caused by data queue congestion, and improves the efficiency of disk processing of park energy consumption data. This optimization mechanism based on the quantification of energy consumption feature extraction rate not only improves the processing speed and accuracy of park energy consumption data but also enhances the stability and reliability of the system, providing better data support for park energy consumption management.

[0061] Furthermore, the steps for acquiring the energy consumption data for display and overlay are as follows: The dynamic adjustment duration of the visualization on the park's big data platform at the end of the display and overlay period is acquired. If the acquired dynamic adjustment duration is within the allowed range of the dynamic adjustment duration set in the database, the synchronous update duration and synchronous overlay duration are acquired; otherwise, it is determined to be a temporary fault, and the preset personnel are prompted to perform a structured check. The dynamic adjustment duration represents the time required for the park's energy consumption data, after qualified energy consumption feature extraction, to undergo 3D visualization resource allocation and adjustment on the park's big data platform. The synchronous update duration represents the time required for the park's energy consumption data, after completing 3D visualization resource allocation and adjustment, to be imported and updated in the 3D park model. The synchronous overlay duration represents the time required for the park's energy consumption data, after completing 3D visualization resource allocation and adjustment, to be imported and overlaid on the 3D park model. The 3D park model is used to provide interactive functions such as historical playback, threshold warning, and custom dashboards for the data management process of park energy consumption data on the park's big data platform. It also visualizes the energy consumption distribution and changes of park energy consumption data in the park's big data platform during concurrent read / write, energy consumption feature extraction, and overlay visualization processes, ensuring the effective application of 3D visualization resources by the park's big data platform.

[0062] In this embodiment, after the dynamic adjustment duration is deemed acceptable, the synchronous update duration and synchronous overlay duration are obtained. Targeted attention to the data update and overlay processes helps to promptly identify and optimize performance bottlenecks in these two key steps, improving the speed of data update and overlay in the 3D park model. When the visualized dynamic adjustment duration exceeds the allowable range, it is determined as a temporary fault and a preset personnel are prompted to check, enabling rapid problem location and timely repair, enhancing system stability. At the same time, the structured inspection process also improves system maintainability. With the rich interactive functions of the 3D park model, historical playback, threshold warnings, etc., are realized, providing users with a more intuitive and comprehensive park energy consumption data management experience and assisting in efficient decision-making.

[0063] Furthermore, based on the acquired display and overlay energy consumption data, the efficiency of the overlay visualization process of the park's energy consumption data is quantified. The specific steps are as follows: the acquired display and overlay energy consumption data are processed by ratio with the corresponding set values ​​to obtain the ratio of visualization dynamic adjustment time, the ratio of synchronous update time, and the ratio of synchronous overlay time. At the same time, the acquired ratio of visualization dynamic adjustment time, the ratio of synchronous update time, and the ratio of synchronous overlay time are coupled and calculated to obtain the quantified value of overlay visualization efficiency.

[0064] Among them, the visual dynamic adjustment duration ratio represents the result of a compensation calculation on the proportion of the acquired visual dynamic adjustment duration relative to the set visual dynamic adjustment duration by the visual dynamic adjustment duration compensation value. The specific constraint expression of the visual dynamic adjustment duration ratio KSH is as follows: KSH1∈ΔKSH1, where KSH represents the ratio of the dynamic adjustment duration of the visualization of the park's big data platform at the end of the display period, h1 represents the compensation value of the dynamic adjustment duration of the visualization, KSH1 represents the dynamic adjustment duration of the visualization of the park's big data platform at the end of the display period, KSH0 represents the set dynamic adjustment duration of the visualization, and ΔKSH1 represents the set allowable range of the dynamic adjustment duration of the visualization.

[0065] The synchronization update duration ratio represents the result of a compensation calculation that adjusts the acquired synchronization update duration relative to the set synchronization update duration. The specific constraint expression for the synchronization update duration ratio TGX is as follows: In the formula, TGX represents the ratio of synchronous update duration of the park's big data platform at the end of the display period, h2 represents the synchronous update duration compensation value, TGX1 represents the synchronous update duration of the park's big data platform at the end of the display period, and TGX0 represents the set synchronous update duration.

[0066] The Synchronous Overlay Duration Ratio (TBD) represents the result of a compensation calculation that adjusts the acquired synchronous overlay duration relative to the set synchronous overlay duration. The specific constraint expression for the TBD is as follows: In the formula, TBD represents the ratio of synchronous overlay duration at the end of the display period of the park's big data platform, h3 represents the synchronous overlay duration compensation value, TBD1 represents the synchronous overlay duration at the end of the display period of the park's big data platform, and TBD0 represents the set synchronous overlay duration.

[0067] The superimposed visualization efficiency quantification value represents the degree of influence of the superimposed energy consumption data on the superimposed efficiency of the park's energy consumption data in the park's big data platform. The specific constraint expression of the superimposed visualization efficiency quantification value DJA is: DJA = KSH + TGX + TBD, where DJA represents the superimposed visualization efficiency quantification value of the park's big data platform at the end of the superimposed period.

[0068] In this example, the compensation values ​​for the visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration represent the degree of influence of the pre-set visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration on the display process of the park's big data platform. Specifically, the database stores preset compensation values ​​corresponding to the visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration. These compensation values ​​have a pre-defined mapping relationship with the visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration. This mapping relationship can be one-to-one or many-to-one. For example, in practical applications, the real-time visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration can be input into this mapping relationship to quickly obtain the corresponding compensation values.

[0069] In this example, the values ​​of the visually dynamically adjusted duration compensation value, the synchronous update duration compensation value, and the synchronous overlay duration compensation value are typically between 0 and 1, and the sum of the three is 1.

[0070] In this embodiment, the quantification value of visualization efficiency increases with the increase of the visualization dynamic adjustment duration, synchronous update duration, and synchronous overlay duration. When the visualization dynamic adjustment duration increases, it may cause data updates to lag behind user operations. It is necessary to optimize the rendering engine to shorten the adjustment duration, thereby reducing interference with synchronous updates. If data re-overlay is frequently triggered during the dynamic adjustment process (such as overlaying historical data when switching time dimensions), it may prolong the overlay duration. It is necessary to reduce redundant calculations through caching mechanisms or asynchronous loading techniques.

[0071] Synchronization update time refers to the time it takes for park energy consumption data to be synchronized from the data source (such as sensors or databases) to the 3D visualization interface and updated. When the synchronization update time increases, dynamic adjustment may lag due to data incompleteness, requiring optimization through preloading or incremental update strategies. Synchronization overlay time refers to the time required to overlay multiple sets of energy consumption data (such as data from different devices or time dimensions) in the 3D visualization interface. When the synchronization overlay time increases, dynamic adjustment response slows down, requiring optimization through layered rendering or data aggregation techniques.

[0072] Parallel processing decouples dynamic adjustment, update, and overlay operations into independent threads. Parallelism is achieved through task queues and resource allocation mechanisms, reducing mutual blocking. By considering the aforementioned mutual influence relationships, the inefficiency caused by mutual interference between dynamic adjustment, update, and overlay operations in existing technologies is finally solved. This enables real-time, efficient, and intuitive management of park energy consumption data, and effectively solves the problem of low efficiency in 3D visualization and synchronous management of park energy consumption data in existing technologies on park big data platforms.

[0073] Furthermore, based on the obtained superimposed visualization efficiency quantification results, visualization parameter optimization is determined. The specific steps are as follows: if the obtained superimposed visualization efficiency quantification results meet the third determination condition, the obtained superimposed visualization efficiency quantification results are recorded as qualified for display and the next stage of 3D visualization park energy consumption management instructions is sent; if the obtained superimposed visualization efficiency quantification results meet the fourth determination condition, the obtained superimposed visualization efficiency quantification results are recorded as qualified for display and the visualization parameters are optimized until the display is qualified; the third determination condition indicates that the obtained superimposed visualization efficiency quantification value is not greater than the superimposed visualization efficiency quantification value set in the database; the fourth determination condition indicates that the obtained superimposed visualization efficiency quantification value is greater than the superimposed visualization efficiency quantification value set in the database; visualization parameters include process I / O priority and visualization frame rate.

[0074] Specifically, the optimization of visualization parameters involves the following steps: The deviation of the acquired superimposed visualization efficiency quantification value and the deviation of the process processing time are used as adjustments to the process I / O priority to reduce the loading latency of park energy consumption data in the park's big data platform. The deviation of the superimposed visualization efficiency quantification value quantifies the difference between the set superimposed visualization efficiency quantification value and the acquired superimposed visualization efficiency quantification value; that is, the difference between the acquired superimposed visualization efficiency quantification value and the set superimposed visualization efficiency quantification value. The deviation of the process processing time represents the difference between the process processing time of the park's big data platform at the end of the overlay display period and the process processing time set in the database. The set process processing time is represented by the sum and average of the historical process processing times of the park's big data platform at the end of the historical display period in the database; the changes in the visualization frame rate in the park's big data platform during the process I / O priority adjustment are monitored in real time. If the obtained visualization frame rate is within the allowed range of visualization frame rate set in the database (set by preset personnel), it indicates that the process I / O priority adjustment is effective and the changes in visualization frame rate are monitored. Otherwise, the process I / O priority is restored to the state before adjustment and an alarm is triggered through a message queue (such as Kafka) (such as checking disk health or optimizing query statements).

[0075] In this embodiment, the process I / O priority adjustment amount refers to the dynamic priority adjustment value in the process scheduling algorithm. The process scheduling algorithm determines the magnitude and direction (increase or decrease priority) of the corresponding process I / O priority based on the deviation of the superimposed visual efficiency quantization value and the deviation of process processing time. By dynamically adjusting the process I / O priority, it is possible to: reduce loading latency: prioritize the processing of I / O intensive processes, reduce data loading time, and thus ensure that critical tasks (such as campus energy consumption data) obtain I / O resources first.

[0076] This example uses the deviation of the superimposed visualization efficiency quantification value and the deviation of the process processing time as the process I / O priority adjustment amount. It can specifically correct the loading delay problem of park energy consumption data in the park's big data platform. This precise adjustment based on deviation avoids blind optimization, effectively improves the data loading speed, and ensures that park energy consumption data can be displayed in the 3D visualization interface in a timely and accurate manner, providing strong support for subsequent energy consumption management decisions.

[0077] When the visualization frame rate is within the set allowable range, it indicates that the process I / O priority adjustment is effective and monitoring can continue to maintain stability. This mechanism ensures the smoothness of the 3D visualization interface, enabling users to have a good interactive experience and easily observe the distribution and changes of energy consumption data in the park. If the visualization frame rate exceeds the allowable range, the system will automatically restore the process I / O priority to the state before adjustment and trigger an alarm through the message queue. This fault-tolerant mechanism can quickly respond to abnormal situations, prevent problems from escalating, and prompt users to check the disk health status or optimize query statements, which helps to troubleshoot and solve problems in a timely manner and enhances the stability and reliability of the system.

[0078] It should be added that, such as Figure 5 The image shown is one of the real-time display and management interfaces for park energy consumption data provided in this application embodiment. It demonstrates the real-time monitoring of park energy consumption data, which may include real-time values ​​of various energy consumption indicators (such as electricity, water, gas, etc.), chart displays, and energy consumption trend analysis. This design helps managers intuitively understand the park's energy consumption status and promptly detect energy consumption anomalies. Figure 6 The diagram shown is the second one of the real-time display and management interface diagrams of park energy consumption data provided in the embodiments of this application. It further refines the management functions of energy consumption data, such as adding functions such as classification statistics, comparative analysis or energy consumption early warning of energy consumption data. Through this diagram, managers can analyze the energy consumption structure of the park more deeply, find areas or equipment with high energy consumption, and thus take targeted energy-saving measures.

[0079] like Figure 7 The diagram shown is an interface diagram for energy consumption feature extraction and management provided in an embodiment of this application. It illustrates the basic process and interface layout for energy consumption feature extraction, including key steps such as data input, feature selection, and feature extraction algorithm. It provides a visual view of energy consumption feature extraction for technical or management personnel, making it easy to understand and operate.

[0080] like Figure 8 As shown, this is one of the park energy consumption data distribution and visualization management interface diagrams provided in the embodiments of this application. Figure 9 As shown, this is the second diagram of the park energy consumption data distribution and visualization management interface provided in the embodiment of this application. These two diagrams intuitively show the distribution of energy consumption data in different areas or at different times within the park. Managers can clearly see the energy consumption status of each part of the park, including the level of energy consumption, the trend of change, and possible anomalies. This design helps managers to more accurately grasp the energy consumption situation of the park and helps to improve the energy consumption management level and efficiency of the park.

[0081] The 3D visualization park energy consumption management system based on big data analysis provided in this application embodiment also includes a settings and help interface, specifically: User permission management: configuring access permissions for different roles to data and functions; Historical data playback: retrieving historical storage, extraction, and visualization records of park energy consumption data; Help center: providing operation guides, troubleshooting manuals, and technical support access.

[0082] In summary, this application embodiment quantifies the concurrent read / write efficiency of the data partitioning storage process for park energy consumption data. After the concurrent read / write is qualified, the energy consumption feature extraction rate of the park energy consumption data extraction process is quantified. After the energy consumption feature extraction is qualified, the overlay visualization efficiency of the park energy consumption data display process is quantified. At the same time, visualization parameter optimization judgment is performed, covering the entire process of data storage, feature extraction, and visualization rendering. This achieves continuous optimization of the performance and experience of the park big data platform, thereby improving the efficiency of 3D visualization synchronous management of park energy consumption data in the park big data platform. This effectively solves the problem of low efficiency in 3D visualization synchronous management of park energy consumption data in the existing technology.

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0085] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A 3D visualized park energy management system based on big data analysis, characterized in that, The application relates to a park energy consumption data management method, which comprises the following steps: The data partition storage management module is used for performing data partition storage on acquired park energy consumption data through a preset park big data platform, and simultaneously performing concurrent read-write efficiency quantification on a data partition storage process of the park energy consumption data based on acquired concurrent read-write energy consumption data, wherein the concurrent read-write energy consumption data comprises data read-write power consumption of a hot access region corresponding to the park big data platform at the end of a data partition storage period, partition key response duration and synchronization lock cumulative duration; The energy consumption feature extraction management module is used for processing space parameter optimization determination according to the acquired concurrent read-write efficiency quantification result, and performing energy consumption feature extraction rate quantification on an energy consumption feature extraction process of the park energy consumption data based on acquired feature extraction energy consumption data after the concurrent read-write is qualified, wherein the processing space parameter optimization determination is used for determining whether to increase the residual capacity of a storage medium and the frequency of a central processing unit to improve the concurrent read-write efficiency of the park big data, and the feature extraction energy consumption data comprises energy consumption feature extraction quantity, offline batch analysis duration and energy consumption feature extraction duration; The superimposed visualization management module is used for disk I / O operation frequency optimization determination according to the acquired energy consumption feature extraction rate quantification result, and performing superimposed visualization efficiency quantification on a display and superimposition process of the park energy consumption data based on acquired display and superimposition energy consumption data after the energy consumption feature extraction is qualified, and simultaneously performing visualization parameter optimization determination according to the acquired superimposed visualization efficiency quantification result, wherein the display and superimposition energy consumption data comprises visualization dynamic adjustment duration, synchronization update duration and synchronization superimposition duration, the disk I / O operation frequency optimization determination is used for determining whether to reduce the disk I / O operation frequency to improve the energy consumption feature extraction efficiency of the park big data, and the visualization parameter optimization determination is used for determining whether to adjust the process I / O priority and the visualization frame rate to improve the superimposed visualization efficiency of the park big data platform. The concurrent read-write efficiency quantification on the data partition storage process of the park energy consumption data based on the acquired concurrent read-write energy consumption data comprises the following steps:

2. A 3D visualized park energy management system based on big data analysis as claimed in claim 1, wherein, The acquired concurrent read-write energy consumption data is subjected to proportion processing with corresponding set values respectively to obtain data read-write power consumption ratio, partition key response duration ratio and synchronization lock cumulative duration ratio, and the acquired data read-write power consumption ratio, partition key response duration ratio and synchronization lock cumulative duration ratio are subjected to inverse proportional operation to obtain a concurrent read-write efficiency quantification value; The hot access region represents a concurrent read-write region corresponding to a data access frequency greater than a set data access frequency in a data partition storage period; The data read-write power consumption ratio represents a compensation operation result of a data read-write power consumption compensation value on the proportion degree of the acquired data read-write power consumption relative to a set data read-write power consumption; The partition key response duration ratio represents a compensation operation result of a data partition key response duration compensation value on the proportion degree of the acquired partition key response duration relative to a set partition key response duration; The acquired concurrent read-write energy consumption data is subjected to proportion processing with corresponding set values respectively to obtain data read-write power consumption ratio, partition key response duration ratio and synchronization lock cumulative duration ratio, and the acquired data read-write power consumption ratio, partition key response duration ratio and synchronization lock cumulative duration ratio are subjected to inverse proportional operation to obtain a concurrent read-write efficiency quantification value; The synchronization lock cumulative duration ratio represents a compensation operation result of a synchronization lock cumulative duration compensation value to an occupancy degree of a synchronization lock cumulative duration obtained relative to a set synchronization lock cumulative duration; The concurrent read-write efficiency quantitative value is used to reflect a quantitative degree of a concurrent read-write energy consumption data to a concurrent read-write efficiency of the park big data platform in the access hotspot area.

3. A big data analytics based 3D visualized park energy management system as claimed in claim 2 wherein, The processing space parameter optimization determination according to the obtained concurrent read-write efficiency quantitative result includes the following specific steps: If the obtained concurrent read-write efficiency quantitative result meets the first determination condition, the obtained concurrent read-write efficiency quantitative result is recorded as a concurrent read-write qualified result, and the energy consumption feature extraction rate quantification is performed; If the obtained concurrent read-write efficiency quantitative result meets the second determination condition, the obtained concurrent read-write efficiency quantitative result is recorded as a concurrent read-write unqualified result, and the storage medium residual capacity optimization and the storage medium residual capacity optimization result determination are performed; The first determination condition represents that the obtained concurrent read-write efficiency quantitative value is not greater than a set concurrent read-write efficiency quantitative value in the database; The second determination condition represents that the obtained concurrent read-write efficiency quantitative value is greater than the set concurrent read-write efficiency quantitative value in the database; The storage medium residual capacity optimization result determination is used to determine whether to perform the central processing unit frequency optimization.

4. A 3D visualized park energy management system based on big data analysis as claimed in claim 3 wherein, The storage medium residual capacity optimization includes the following specific steps: The obtained concurrent read-write efficiency quantitative value deviation and the storage medium consumption amount deviation are used as a storage medium residual capacity increase amount to correct a read-write seek response of the disk, and the concurrent read-write efficiency quantitative value deviation is used to quantify a difference degree between the obtained concurrent read-write efficiency quantitative value and a preset concurrent read-write efficiency quantitative value; The obtained difference degree comparison result is obtained by comparing a concurrent read-write efficiency quantitative value obtained after the storage medium residual capacity is increased with a concurrent read-write efficiency quantitative value obtained before the storage medium residual capacity is increased.

5. A 3D visualized park energy management system based on big data analysis as claimed in claim 4 wherein, The storage medium residual capacity optimization result determination includes the following specific steps: If the obtained difference degree comparison result is greater than a set difference degree comparison result in the database, the storage medium residual capacity optimization is completed and the energy consumption feature extraction rate quantification is performed, otherwise, the central processing unit frequency optimization is performed; The central processing unit frequency optimization is used to improve a task switching response of the central processing unit according to an obtained central processing unit frequency increase amount. The central processing unit frequency increase amount represents that a PID control algorithm in the central processing unit corrects a central processing unit processing rate deviation of the park energy consumption data by using a concurrent read-write efficiency quantitative value deviation obtained after the storage medium residual capacity is increased and a central processing unit utilization rate deviation.

6. A 3D visualized park energy management system based on big data analysis as claimed in claim 1, wherein, The energy consumption feature extraction rate quantification of the energy consumption feature extraction process of the park energy consumption data based on the obtained feature extraction energy consumption data includes the following specific steps: An energy consumption feature extraction quantity of the park big data platform at the end of the energy consumption feature extraction is obtained, if the obtained energy consumption feature extraction quantity is not less than a set energy consumption feature extraction quantity in the database, a data extraction exception instruction is sent, otherwise, an offline batch analysis time length and an energy consumption feature extraction time length of the park big data platform at the end of the energy consumption feature extraction are obtained; Based on the obtained offline batch analysis duration and energy consumption feature extraction duration, the proportion of the results is processed to obtain the energy consumption feature extraction rate quantitative value, which is used to quantify the time proportion of the energy consumption feature extraction process in the entire offline batch analysis process.

7. A big data analytics based 3D visualized park energy management system as claimed in claim 6 wherein, The obtained energy consumption feature extraction rate quantitative result is used to determine the frequency optimization of the disk I / O operation, and the specific steps are as follows: If the obtained energy consumption feature extraction rate quantitative value is not greater than the energy consumption feature extraction rate quantitative value set in the database, the obtained energy consumption feature extraction rate quantitative result is recorded as energy consumption feature extraction qualified and superimposed visualization efficiency quantification is performed; If the obtained energy consumption feature extraction rate quantitative value is greater than the energy consumption feature extraction rate quantitative value set in the database, the obtained energy consumption feature extraction rate quantitative result is recorded as energy consumption feature extraction unqualified and disk I / O operation frequency optimization is performed; The disk I / O operation frequency optimization means that the read and write request times of the disk are reduced based on the obtained disk I / O operation frequency reduction amount, so as to reduce the delay caused by data queue congestion; The disk I / O operation frequency reduction amount means that the sum of the energy consumption feature extraction rate quantitative value deviation and the disk I / O operation data amount deviation is used as the disk I / O operation frequency correction value to correct the utilization deviation of the disk in processing park energy consumption data.

8. A 3D visualized park energy management system based on big data analysis as claimed in claim 1, wherein, The acquisition step of the display superposition energy consumption data is as follows: The visualization dynamic adjustment duration of the park big data platform at the end of the display superposition period is obtained, if the obtained visualization dynamic adjustment duration is within the range of the visualization dynamic adjustment duration set in the database, the synchronization update duration and the synchronization superposition duration are obtained, otherwise it is determined as a temporary failure and the preset personnel is prompted to perform structured inspection; The visualization dynamic adjustment duration means that the park energy consumption data after energy consumption feature extraction qualified is allocated and adjusted in the 3D visualization resource of the park big data platform; The synchronization update duration means that the park energy consumption data after 3D visualization resource allocation and adjustment is imported and updated in the three-dimensional park model; The synchronization superposition duration means that the park energy consumption data after 3D visualization resource allocation and adjustment is imported and superimposed with the three-dimensional park model; The three-dimensional park model is used for visualizing the energy consumption distribution and change of the park energy consumption data in the park big data platform in the process of concurrent read and write, energy consumption feature extraction and superimposed visualization.

9. A big data analytics based 3D visualized campus energy management system as claimed in claim 8 wherein, The display superposition process of the obtained display superposition energy consumption data is quantified based on the superimposed visualization efficiency, and the specific steps are as follows: The obtained display superposition energy consumption data is respectively processed with the corresponding set value to obtain the visualization dynamic adjustment duration ratio, the synchronization update duration ratio and the synchronization superposition duration ratio, and the visualization dynamic adjustment duration ratio, the synchronization update duration ratio and the synchronization superposition duration ratio are coupled and operated to obtain the superimposed visualization efficiency quantitative value; The visualization dynamic adjustment time length ratio value represents a result of compensation operation of a visualization dynamic adjustment time length compensation value on an acquired visualization dynamic adjustment time length relative to a set visualization dynamic adjustment time length; The synchronization update time length ratio value represents a result of compensation operation of a synchronization update time length compensation value on an acquired synchronization update time length relative to a set synchronization update time length; The synchronization superimposition time length ratio value represents a result of compensation operation of a synchronization superimposition time length compensation value on an acquired synchronization superimposition time length relative to a set synchronization superimposition time length; The superimposed visualization efficiency quantification value represents quantification data of an influence degree of the superimposed visualization efficiency of the energy consumption data on the park big data platform.

10. A big data analytics based 3D visualized campus energy management system as claimed in claim 9 wherein, The visualization parameter optimization determination according to the acquired superimposed visualization efficiency quantification result includes the following steps: If the acquired superimposed visualization efficiency quantification result meets the third determination condition, the acquired superimposed visualization efficiency quantification result is recorded as qualified superimposed visualization and a 3D visualization park energy consumption management instruction of the next stage is sent; If the acquired superimposed visualization efficiency quantification result meets the fourth determination condition, the acquired superimposed visualization efficiency quantification result is recorded as qualified superimposed visualization and a visualization parameter optimization is performed; The third determination condition represents that the acquired superimposed visualization efficiency quantification value is not greater than a set superimposed visualization efficiency quantification value in the database; The fourth determination condition represents that the acquired superimposed visualization efficiency quantification value is greater than the set superimposed visualization efficiency quantification value in the database; The visualization parameter includes a process I / O priority and a visualization frame rate; The visualization parameter optimization includes the following steps: The acquired superimposed visualization efficiency quantification value deviation and process processing time length deviation are used as a process I / O priority adjustment amount to correct and reduce the loading delay of the park energy consumption data on the park big data platform, and the superimposed visualization efficiency quantification value deviation is used to quantify the difference between the set superimposed visualization efficiency quantification value and the acquired superimposed visualization efficiency quantification value; The change of the visualization frame rate in the process I / O priority adjustment process in the park big data platform is monitored in real time, if the acquired visualization frame rate is within the set visualization frame rate range in the database, it is indicated that the process I / O priority adjustment is effective and the change of the visualization frame rate is continuously monitored, otherwise the process I / O priority is restored to the state before the adjustment and an alarm is triggered through a message queue.

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