Cloud platform control system based on business management

Abstract

The invention discloses a cloud platform control system based on service management. An operation method of the system comprises the following steps: constructing and managing a bidirectional long-short-term memory neural network prediction model; carrying out weight distribution and data processing control on the constructed prediction model; carrying out cloud platform monitoring analysis control management on the heat source plant; and performing verification display control management of the heat source factory cloud platform. The construction management of the bidirectional long-short-term memory neural network prediction model comprises the step of constructing the bidirectional long-short-term memory neural network prediction model to carry out power load prediction management of the heat source plant, wherein the model is divided into two independent long-short-term memory neural networks. The step of carrying out weight distribution and data processing control on the constructed prediction model comprises the step of carrying out weight value distribution management on the power load influence factors through Attention after construction of the bidirectional long-short-term memory neural network prediction model is completed. The system has the characteristics of intelligent control management and high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platforms, and specifically to a cloud platform control system based on business management. Background Art

[0002] With the acceleration of the urbanization process and the improvement of people's living standards, the demand for urban heating by people is gradually increasing. In order to meet people's heating demand, it is necessary to construct large-scale heat source plants for centralized heating on a large scale. Compared with the decentralized heating system, centralized heating can effectively save energy and improve thermal efficiency. Therefore, centralized heating has become an important part of urban public utilities and is also a key industry vigorously promoted by the state for energy conservation and consumption reduction. However, currently, it is a critical period for the construction of a sustainable and green development of our country's industrial system and the promotion of all-round transformation. The emissions of air pollutants have had a huge negative impact on the lives of the people of the country. The problems of low energy utilization rate and large exhaust gas emissions existing in current heat source plants are mainly due to the lack of effective energy consumption monitoring and management. Therefore, it is very necessary to design an intelligent control management and highly efficient cloud platform control system based on business management. Summary of the Invention

[0003] The purpose of the present invention is to provide a cloud platform control system based on business management to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solutions: A cloud platform control method based on business management, including: Conducting the construction management of a bidirectional long short-term memory neural network prediction model; Performing weight assignment and data processing control on the constructed prediction model; Conducting the cloud platform monitoring and analysis control management of the heat source plant; Conducting the verification and display control management of the heat source plant cloud platform.

[0005] According to the above technical solutions, the conducting of the construction management of the bidirectional long short-term memory neural network prediction model includes: Constructing a bidirectional long short-term memory neural network prediction model for the power load prediction management of the heat source plant, where the model is divided into two independent long short-term memory neural networks.

[0006] According to the above technical solutions, the performing of weight assignment and data processing control on the constructed prediction model includes: After the construction of the bidirectional long short-term memory neural network prediction model is completed, the weight value assignment management of the influencing factors of the power load is carried out through Attention; Identifying the historical load data collected by the power system and conducting abnormal data analysis and processing according to the identification results.

[0007] According to the above technical solution, the cloud platform monitoring, analysis, control and management of the heat source plant includes: Design multiple communication interfaces in the data collector of the heat source plant to enable a single data acquisition device to simultaneously collect multiple devices, and control the data collector to convert the collected data into digital signals and store the corresponding data information in a set interval; Establish communication between the server and the client through the WebSocket protocol to implement a two-way transmission channel, and use this channel to enable the client and the server to send information to each other; Store and record the data information of the heat source plant cloud platform by establishing a database. Separate data tables are established for different types of data, and the data uploaded by the collector is stored according to the set location.

[0008] According to the above technical solution, the verification, display, control and management of the heat source plant cloud platform includes: The staff of the heat source plant need to log in to the energy consumption monitoring cloud platform of the heat source plant through the registered account and password. When forgetting the password, select to enter the password retrieval page to verify and modify the password through the reserved mobile phone number to prevent unauthorized personnel outside the plant from logging in; Control the display interface of the heat source plant cloud platform. The display interface includes the total electricity consumption statistics, real-time energy consumption ranking, annual energy consumption comparison, important notifications and to-do items, so as to facilitate the management personnel to see various information in the first time and process it in a timely manner.

[0009] According to the above technical solution, a cloud platform control system based on business management includes: A load prediction module for performing power load prediction and control of the heat source plant; An analysis and management module for performing analysis and management of the heat source plant cloud platform; A verification and display module for performing verification, display and control of the heat source plant cloud platform.

[0010] According to the above technical solution, the load prediction module includes: A model construction module for performing prediction model construction and management of the heat source plant; A weight assignment module for performing factor weight assignment processing of the heat source plant; A processing and control module for processing and controlling the data information of the heat source plant.

[0011] According to the above technical solution, the analysis and management module includes: A data acquisition module for performing data information acquisition and control of the heat source plant cloud platform; A communication management module for performing communication transmission processing of the heat source plant cloud platform; Database module for optimizing the database design of the heat source plant cloud platform.

[0012] According to the above technical solution, the verification and display module includes: User verification module for performing user login verification of the heat source plant cloud platform; Monitoring and display module for controlling data monitoring and display of the heat source plant cloud platform.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By setting up a load prediction module, an analysis and management module, and a verification and display module, the present invention solves the problem that the long short-term memory neural network can only obtain information from previous moments and cannot obtain information from subsequent moments, and realizes that the output result at each moment contains information from both past and future moments, making the processing efficiency and performance of the power load prediction data of the heat source plant more efficient and accurate. At the same time, by paying different degrees of attention to different load influencing factors, the final prediction result is more accurate, effectively reducing unnecessary resource waste. At the same time, when uploading data, it needs to be packaged according to the corresponding data packet format, making the data collection of the heat source plant more efficient and avoiding the generation of errors. Description of the Drawings

[0014] The drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a flowchart of a cloud platform control method based on business management provided in Embodiment 1 of the present invention; Figure 2 is a module composition diagram of a cloud platform control system based on business management provided in Embodiment 2 of the present invention. Detailed Embodiments

[0015] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0016] Embodiment 1: Figure 1 is a flowchart of a cloud platform control method based on business management provided in Embodiment 1 of the present invention. This embodiment can be applied to a cloud platform control system. This method can be executed by a cloud platform control system provided in Embodiment 1 of the present invention. The system consists of multiple software and hardware modules, such as Figure 1 shown. The method specifically includes the following steps: S101. Construct and manage a bidirectional long short-term memory neural network prediction model. Exemplarily, in the embodiment of the present invention, a bidirectional long short-term memory neural network prediction model is constructed for power load prediction management of a heat source plant, where the model is divided into two independent long short-term memory neural networks; since power load prediction is an important part of the energy consumption monitoring system of the heat source plant, accurate power load prediction can provide a scientific basis for the power planning, power dispatching, load control, and economic operation of the heat source plant. Therefore, through this step, a bidirectional long short-term memory neural network prediction model is constructed for power load prediction of the heat source plant. The forward-running long short-term memory neural network processes the data in the normal sequence, and the backward-running long short-term memory neural network processes the data in the reverse order. Finally, the obtained results are combined and used as the prediction result. In this way, the problem that the long short-term memory neural network can only obtain information at previous times and cannot obtain information at later times is solved, and the output result at each time contains information at both past and future times. In this way, the processing efficiency and performance of the power load prediction data of the heat source plant are more efficient and accurate.

[0017] S102. Perform weight assignment and data processing control on the constructed prediction model. Exemplarily, in the embodiment of the present invention, after the construction of the bidirectional long short-term memory neural network prediction model is completed, weight value assignment management of the power load influencing factors is performed through Attention; since Attention discovers the data that needs to be concerned by screening the data and assigns a larger weight to this type of data, the greater the influence value, the more attention can be obtained, thereby reducing waste of resources. Therefore, through this step, Attention is used in combination with the bidirectional long short-term memory neural network prediction model to perform short-term prediction of the power load of the heat source plant. By paying different degrees of attention to different load influencing factors, the final prediction result is more accurate and is conducive to subsequent monitoring management.

[0018] Identify the historical load data collected by the power system and perform abnormal data analysis and processing according to the identification results; since the historical load data collected by the power system is the most important basis for load prediction, its reliability is directly related to the accuracy of the final load prediction. However, due to various reasons, the historical load data usually has problems such as missing and abnormal data, which may be non-human reasons such as unstable collection equipment and non-human reasons such as system fault tripping. Therefore, through this step, the average value, range, and other data of the data around the missing value are used to replace the missing value, making the power load prediction data of the heat source plant more accurate.

[0019] S103. Perform cloud platform monitoring analysis control management of the heat source plant. Exemplarily, in the embodiment of the present invention, a multi-channel communication interface is designed in the data collector of the heat source plant, so that a data collection device can collect multiple devices at the same time, and the data collector is controlled to store the collected data in a set interval after conversion into a digital signal; since the main energy-consuming areas of the heat source plant mainly include: coal shed, coal conveying corridor, crushing building, boiler room, central control room, fan room, desulfurization room, water room, high-voltage and low-voltage distribution rooms, and staff living areas, etc., a large number of mechanical equipment and multiple power boxes are installed in each working area, and the electrical energy information of all equipment and power boxes needs to be collected. Therefore, through this step, to meet the actual needs, a multi-channel communication interface is designed in the data collector, so that a data collection device can collect multiple devices at the same time, effectively reducing unnecessary resource waste. At the same time, when uploading data, it needs to be packaged according to the corresponding data packet format, making the data collection of the heat source plant more efficient and avoiding errors.

[0020] Communication is established between the server and the client through the WebSocket protocol to implement a two-way transmission channel, and information is sent between the client and the server through this channel; in this step, WebSocket is a full-duplex communication network protocol located at the application layer based on the TCP protocol. When communication is established between the server and the client through the WebSocket protocol, a connection is established between the client and the server through the HTTP protocol. After this connection is established, a two-way transmission channel is implemented between the two through the WebSocket protocol, and information is sent between the client and the server through this channel, solving the problem of resource waste that occurs in both the previous short polling and long polling methods, and at the same time improving the communication rate between the two and reducing the server pressure.

[0021] The data information of the heat source plant cloud platform is stored and recorded by establishing a database. Different types of data are respectively established into data tables, and the data uploaded by the collector is stored according to the set location; in this step, the database is mainly designed around various equipment in the production process of the heat source plant. An equipment information table is established for the basic information of the equipment. This data table mainly includes information such as the name, number, status, and location of the equipment. By setting the number, the same type of equipment can be distinguished, which is convenient for staff to accurately query equipment information and improve the monitoring, analysis, operation, and processing efficiency of the heat source plant cloud platform.

[0022] S104. Perform verification display control management of the heat source plant cloud platform; Exemplarily, in the embodiment of the present invention, the staff of the heat source plant need to log in to the energy consumption monitoring cloud platform of the heat source plant through the registered account and password. When forgetting the password, they can choose to enter the password retrieval page to verify through the reserved mobile phone number and modify the password to prevent unauthorized personnel outside the plant from logging in; Control the display interface of the cloud platform of the heat source plant. The display interface includes the statistics of the total electricity consumption, the real-time ranking of energy consumption, the annual energy consumption comparison, important notices and to-do items, which facilitates the management personnel to see various information in the first time and process it in a timely manner.

[0023] Embodiment 2: Embodiment 2 of the present invention provides a cloud platform control system based on business management. Figure 2 It is a schematic diagram of the module composition of a cloud platform control system based on business management provided for this Embodiment 2. As Figure 2 shown, the system includes: A load prediction module, which is used to perform the prediction and control of the power load of the heat source plant; An analysis and management module, which is used to perform the analysis and management of the cloud platform of the heat source plant; A verification and display module, which is used to perform the verification and display control of the cloud platform of the heat source plant.

[0024] In some embodiments of the present invention, the load prediction module includes: A model construction module, which is used to perform the construction and management of the prediction model of the heat source plant; A weight allocation module, which is used to perform the factor weight allocation processing of the heat source plant; A processing control module, which is used to perform the processing and control of the data information of the heat source plant.

[0025] In some embodiments of the present invention, the analysis and management module includes: A data acquisition module, which is used to perform the data information acquisition control of the cloud platform of the heat source plant; A communication management module, which is used to perform the communication transmission processing of the cloud platform of the heat source plant; A database module, which is used to perform the database design optimization of the cloud platform of the heat source plant.

[0026] In some embodiments of the present invention, the verification and display module includes: A user verification module, which is used to perform the user login verification of the cloud platform of the heat source plant; A monitoring and display module, which is used to perform the data monitoring and display control of the cloud platform of the heat source plant.

[0027] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0028] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A cloud platform control method based on service management, characterized in that: Including: Construct and manage a bidirectional long short-term memory neural network prediction model; Perform weight allocation and data processing control on the constructed prediction model; Conduct monitoring, analysis, control, and management of the cloud platform of the heat source plant; Conduct verification, display, control, and management of the cloud platform of the heat source plant.

2. The cloud platform control method based on service management according to claim 1, wherein: The construction and management of the bidirectional long short-term memory neural network prediction model include: Construct a bidirectional long short-term memory neural network prediction model for power load prediction management of the heat source plant, where the model is divided into two independent long short-term memory neural networks.

3. A cloud platform control method based on service management according to claim 1, characterized in that: The weight allocation and data processing control for the constructed prediction model include: After completing the construction of the bidirectional long short-term memory neural network prediction model, use Attention to manage the weight value allocation of the factors affecting power load; Identify the historical load data collected by the power system and perform abnormal data analysis and processing according to the identification results.

4. A cloud platform control method based on service management according to claim 1, characterized in that: The monitoring, analysis, control, and management of the cloud platform of the heat source plant include: Design multiple communication interfaces in the data collector of the heat source plant to enable a single data acquisition device to collect multiple devices simultaneously, and control the data collector to store the collected data as converted digital signals in a set interval; Establish communication between the server and the client through the WebSocket protocol to achieve a two-way transmission channel, and use this channel for the client and the server to send information to each other; Store and record the data information of the cloud platform of the heat source plant by establishing a database. Separate data tables are established for different types of data, and the data uploaded by the collector is stored in the set location.

5. A cloud platform control method based on service management according to claim 1, characterized in that: The verification, display, control, and management of the cloud platform of the heat source plant include: The staff of the heat source plant need to log in to the energy consumption monitoring cloud platform of the heat source plant through the registered account and password. When forgetting the password, select to enter the password retrieval page to verify and modify the password through the reserved mobile phone number to prevent unauthorized personnel outside the plant from logging in; Control the display interface of the cloud platform of the heat source plant. The display interface includes the total electricity consumption statistics, real-time energy consumption ranking, annual energy consumption comparison, important notifications, and to-do items, facilitating managers to view various types of information in a timely manner and handle them promptly.

6. A cloud platform control system based on business management, characterized in that: Including: A load prediction module for controlling the power load prediction of the heat source plant; An analysis and management module for analyzing and managing the cloud platform of the heat source plant; A verification and display module for controlling the verification and display of the cloud platform of the heat source plant.

7. The cloud platform control system based on service management according to claim 6, characterized in that: The load prediction module includes: A model construction module for constructing and managing the prediction model of the heat source plant; A weight allocation module for processing the factor weight allocation of the heat source plant; A processing control module for processing and controlling the data information of the heat source plant.

8. A cloud platform control system based on service management according to claim 6, characterized in that: The analysis and management module includes: A data acquisition module for controlling the data information acquisition of the cloud platform of the heat source plant; A communication management module for processing the communication transmission of the cloud platform of the heat source plant; A database module for optimizing the database design of the cloud platform of the heat source plant.

9. A cloud platform control system based on service management according to claim 6, characterized in that: The verification and display module includes: A user verification module for verifying the user login of the cloud platform of the heat source plant; A monitoring and display module for controlling the data monitoring and display of the cloud platform of the heat source plant.

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