Intelligent load distribution management system and method for power plant water treatment system

Abstract

An intelligent load distribution management system and method for a power plant water treatment system. A mechanism and data integrated modeling means is provided to achieve automatic and precise water distribution and control and water abnormality early warning for a power plant water system. In addition, big data analytics, artificial intelligence, and other digital application technologies are used to complete intelligent upgrades of operational parameters of different stages of the water treatment system, including water intake, water production, process water reuse, recycling, consumption, and emission, to implement dynamic monitoring of operational parameters, and to construct a water balance dynamic monitoring model, an intelligent early warning model for prompting abnormal water usage, and a water automatic distribution and control model. Equilibrium, balance, and stable operation of different systems are ensured, the safety, economic efficiency, and environmental performance of the power plant water system are improved, and energy saving and emissions reduction, workforce reduction and productivity improvement, real‑time monitoring, safe maintenance, refined management, and efficiency and profitability growth are achieved.

Description

Intelligent load distribution management system and method of power plant water treatment system

[0001] The present application claims priority to the Chinese patent application No. 202410953210.6, filed on July 16, 2024, and entitled "Intelligent load distribution management system and method of power plant water treatment system", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application belongs to the technical field of smart power plants, in particular to an intelligent load distribution management system and method of power plant water treatment system. BACKGROUND

[0003] The treatment process of each water system in a power plant is a nonlinear system with many variables, large lag, dynamic changes, and many interference terms, which is a complex industrial process. The intelligent balancing and regulation of multi-region water production units in thermal power plants have the characteristics of involving multiple systems, wide distribution, complex system correlation, and large changes in supply and demand. Realizing the automation of each water treatment system is a necessary means to improve water resource utilization, reduce wastewater discharge, and reduce operating costs. Current research on water-saving in auxiliary network systems mainly focuses on full-plant zero discharge, water-saving schemes for each system, and dynamic water balance. Auxiliary networks mainly use instrument, control, and automation (ICA) technologies, forming a three-layer architecture of distributed control system (DCS), plant-level monitoring system (SIS), and information management system (MIS). This architecture can realize the basic functions of the water treatment process and has formed certain intelligent achievements, but there are still the following problems:

[0004] (1) Although most power plant water treatment automatic control systems are generally equipped with flow meters and other measurement and detection instruments, they only have data acquisition and simple control functions. The historical data accumulated during system operation cannot fully extract valuable information, and the data cannot be analyzed and predicted. In actual production processes, the experience of operating personnel is mainly relied on, and the real-time water consumption and actual water consumption cannot be mastered, making it impossible to realize online automatic statistics of water consumption;

[0005] (2) It is difficult to discover unreasonable water use in a timely manner. Some systems in power plants may have running, leaking, dripping, and leaking phenomena, which may cause high water consumption in power plants to some extent. Multiple reasons make it difficult to achieve the design requirements of actual water consumption and drainage capacity;

[0006] (3) Each system is isolated from each other, lacks information sharing functions, and has the phenomenon of "information island";

[0007] (4) The water supply and demand between each water system is complex, and there are situations of reused water, string water, and reused water, but each system operates independently and cannot automatically distribute and adjust the water volume according to the water demand, and there are situations of 'high-quality low use' of water resources due to temporary water sources, or situations of increased external discharge due to excessive water production in a short time but unable to reuse. SUMMARY

[0008] The power plant water treatment system load intelligent distribution management system and method provided by the application solve the problem that the accumulated historical data in the system operation process cannot fully mine valuable information and cannot analyze and predict data.

[0009] To achieve the above-mentioned purpose, the application provides the following technical solutions:

[0010] A power plant water treatment system load intelligent distribution management system, comprising:

[0011] An environment acquisition system for acquiring data in the water treatment system operating environment;

[0012] A DCS control system for acquiring water treatment system operation data and receiving control commands from a big data platform to control the water system;

[0013] An information acquisition system for receiving environment data collected by the environment acquisition system and operation data collected by the DCS control system, and transmitting to the big data platform;

[0014] A big data platform for displaying operation data and environment data, performing data analysis according to the environment data and operation data, performing intelligent early warning and obtaining optimal load distribution instructions according to the data analysis results, and sending the load distribution instructions to the DCS control system.

[0015] The big data platform displays operation data and environment data based on a multi-region water production unit operation parameter and water balance dynamic monitoring system, wherein the multi-region water production unit operation parameter and water balance dynamic monitoring system is based on the production processes of raw water pretreatment, reclaimed water pretreatment, boiler makeup water treatment, industrial wastewater treatment, and domestic water treatment. By extracting water quantity data, key control parameters are proposed, and a comprehensive water quantity and water quality demand is established.

[0016] The big data platform performs intelligent early warning based on a multi-region water production unit intelligent early warning model, wherein the multi-region water production unit intelligent early warning model predicts the normal value range of each control index and the reasonable change rate of multiple coupling indexes according to different stages and different working conditions of system operation, and issues a corresponding warning when the model prediction condition is exceeded.

[0017] The multi-region water production unit intelligent early warning model comprises system leakage early warning, system flow mismatch early warning, water supply system water cut-off early warning, wastewater quantity overrun early warning, high-quality low-use early warning, water intake or discharge quantity overrun early warning, desalted water consumption or unit power generation water intake quantity overrun early warning and imbalance rate overrun early warning.

[0018] System leakage early warning: when there is a difference between the total water quantity of equipment outlet water and the water tank inlet water quantity, and there is a leakage exceeding a threshold value, early warning is performed.

[0019] System flow mismatch early warning: early warning is performed when there is a difference between the front and rear stage flow of the equipment and the system.

[0020] Water supply system water cut-off early warning: early warning is performed when, according to the current equipment output operation, it is predicted that the water supply quantity will not meet the equipment operation requirement in the subsequent time.

[0021] Wastewater quantity overrun early warning: early warning is performed when, according to the current wastewater discharge quantity, it is predicted that the wastewater treatment system will not be able to timely process in the subsequent time.

[0022] High-quality low-use early warning: early warning is performed when the system operation has the condition of using reclaimed water for water supply, but still uses other high-quality water sources for water supply.

[0023] Water intake or discharge quantity overrun early warning: early warning is performed when the water intake or discharge quantity exceeds the previous operation average value or the set value.

[0024] Desalted water consumption or unit power generation water intake quantity overrun early warning: early warning is performed when the whole plant real-time desalted water consumption or unit power generation water intake quantity exceeds the standard value.

[0025] Imbalance rate overrun early warning: early warning is performed when the single whole plant real-time imbalance rate exceeds the standard value.

[0026] According to the environmental data and the operation data, the best load distribution instruction is obtained based on a linkage distribution control model, wherein the linkage distribution control model specifically comprises the following steps: a bottom-layer characteristic model is mined for water quantity influence parameters of each water production unit, a parameter, time and space correlation mechanism characteristic model among the units is established, on the basis of the original relative basis and independent operation sequence control of each unit, a deep optimization is completed for the control strategies of each unit and among the units, a horizontal linkage simulation control system of “model result output + automatic control” is constructed, and a load distribution mechanism is provided to provide clear load instructions for each system.

[0027] The application discloses a load intelligent distribution management method of a power plant water treatment system.

[0028] Compared with the prior art, the application has the following beneficial effects: the application provides a load intelligent distribution management system of a power plant water treatment system, provides a modeling means integrating mechanism and data, realizes automatic and accurate distribution control of water quantity of the power plant water system and water quantity abnormality early warning. Meanwhile, the application uses big data analysis, artificial intelligence and other digital application technologies to intelligently upgrade operation parameters of water taking, water making, water production and reuse, reuse, consumption and discharge of the water treatment system, realizes dynamic monitoring of the operation parameters, construction of a water quantity balance dynamic monitoring model, an intelligent early warning model of water use abnormality reminding and an automatic distribution control model of the water quantity, guarantees balanced, balanced and stable operation of each system, improves safe, economic and environmentally-friendly operation level of the power plant water system, realizes energy saving and emission reduction, reduces staff and increases efficiency, realizes real-time monitoring, safe maintenance, fine management and benefit growth. BRIEF DESCRIPTION OF DRAWINGS

[0029] Fig. 1 is a block diagram of a load intelligent distribution management system of a power plant water treatment system according to the application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme of the embodiments of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, not all the embodiments. The components of the embodiments of the application described and shown in the drawings can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the application without creative labor are within the scope of protection of the application.

[0032] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0033] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, or the orientation or position relationship of the product of the present application when it is usually placed, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, the terms "first", "second" and the like are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0034] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0035] In the description of the embodiments of the present application, it should be noted that unless otherwise explicitly specified and limited, if the terms "set", "mount", "connected", "connected" appear, they should be understood in a broad sense. For example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication between the two elements inside. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0036] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the drawings.

[0037] As shown in FIG. 1, the present application provides a load intelligent distribution management system of a power plant water treatment system, comprising:

[0038] An environment acquisition system is used to acquire data in the running environment of the water treatment system;

[0039] A DCS control system is used to acquire water treatment system running data and receive control commands of a big data platform to control the water system;

[0040] An information acquisition system is used to receive environment data acquired by the environment acquisition system and running data acquired by the DCS control system, and transmit them to the big data platform;

[0041] A big data platform is used to display running data and environment data, analyze data according to environment data and running data, intelligently warn and obtain optimal load distribution instructions according to the data analysis result, and send the load distribution instructions to the DCS control system.

[0042] The big data platform displays operation data and environment data based on a multi-region water production unit operation parameter and water balance dynamic monitoring system, wherein the multi-region water production unit operation parameter and water balance dynamic monitoring system is based on the production processes of raw water pretreatment, reclaimed water pretreatment, boiler makeup water treatment, industrial wastewater treatment, and domestic water treatment, extracts water quantity data, proposes key control parameters, and establishes a comprehensive water quantity and quality demand based on the operation data.

[0043] The optimal load distribution instruction is obtained based on an interlocking distribution control model according to the environment data and the operation data, wherein the interlocking distribution control model specifically includes the following steps: a bottom characteristic model is mined for water quantity influence parameters of each water production unit, a parameter, time, and space correlation mechanism characteristic model is established between units, a deep optimization of control strategies for each unit and between units is completed based on the original relative basis and independent operation sequence control of each unit, a horizontal interlocking simulation control system of “model result output + automatic control” is constructed, and a load distribution mechanism is provided to provide clear load instructions for each system.

[0044] The big data platform performs intelligent early warning according to the environment data and the operation data based on a multi-region water production unit intelligent early warning model, wherein the multi-region water production unit intelligent early warning model predicts the normal value range and reasonable change rate of each control index according to different stages and different working conditions of system operation, and issues a corresponding early warning when the model prediction condition is exceeded.

[0045] The multi-region water production unit intelligent early warning model includes system leakage early warning, system flow mismatch early warning, water supply system water cut-off early warning, wastewater quantity overrun early warning, high-quality low-use early warning, water intake or discharge quantity overrun early warning, desalted water consumption or unit power generation water intake quantity overrun early warning, and unbalance rate overrun early warning.

[0046] System leakage early warning: early warning is performed when there is a difference exceeding a threshold value between the total water quantity of equipment outlet water and the water tank inlet water, and there is a leakage exceeding a threshold value in between.

[0047] System flow mismatch early warning: early warning is performed when there is a difference between the flow of equipment and the flow of the system.

[0048] Water supply system water cut-off early warning: early warning is performed when it is predicted that the water supply quantity will not meet the equipment operation requirements at a subsequent time according to the current equipment output operation.

[0049] Wastewater quantity overrun early warning: early warning is performed when it is predicted that the wastewater treatment system will not be able to timely process at a subsequent time according to the current wastewater discharge quantity.

[0050] High-quality low-use early warning: early warning is performed when the system operation has conditions to supply water with reclaimed water, but other high-quality water sources are still used for water supply.

[0051] Water intake or discharge over-limit early warning: early warning when the water intake or discharge exceeds the previous average or set value.

[0052] Salt water consumption or unit power generation water intake over-limit early warning: early warning when the real-time salt water consumption or unit power generation water intake of the whole plant exceeds the standard value;

[0053] Unbalance rate over-limit early warning: early warning when the real-time unbalance rate of the whole plant exceeds the standard value.

[0054] Embodiment:

[0055] A load intelligent allocation management system of a power plant water treatment system based on big data analysis includes:

[0056] An information collection system can collect real-time operation parameters of the power plant water treatment system, operation parameters of the power plant main engine, environmental temperature, steam supply conditions, heating conditions, etc., and provide data support for the big data platform:

[0057] A big data analysis platform integrates operation parameters of the power plant water treatment system and a water balance dynamic monitoring model, an intelligent early warning model, and a water quantity linkage allocation control model. The best load allocation instruction is obtained after the data collected by the information collection system is analyzed and processed by the model, realizing the intelligentization of the load allocation of the power plant water treatment system and the intelligent early warning of water quantity abnormal conditions.

[0058] An environmental monitoring system is used for temperature, humidity, wind speed, etc. Data monitoring and data transmission to the information collection system.

[0059] A power plant DCS control system is used for data monitoring and control of the power plant water system, and data transmission to the information collection system.

[0060] Operation parameter dynamic monitoring and multi-region water production unit linkage allocation control system

[0061] The system is composed of high-performance servers, switches, host computers, etc. Through data extraction, the DCS data in the existing database server is transmitted to the high-performance server to form a set of operation parameter dynamic monitoring and multi-region water production unit linkage allocation control system architecture, realizing efficient and stable data transmission and intelligent analysis and application basic platform based on big data. Through the construction of the platform, the problem that the old platform cannot realize data analysis is solved.

[0062] The big data analysis platform includes the following contents:

[0063] Multi-region water production unit operation parameter and water balance dynamic monitoring system

[0064] The system is based on raw water pretreatment, reclaimed water pretreatment, boiler makeup water treatment, industrial wastewater treatment, domestic water treatment and other water system production processes. By extracting main water quantity data, key control parameters are proposed, and water quantity and water quality requirements are integrated to establish a multi-region water production unit operation parameter and water balance dynamic monitoring system. The system realizes online monitoring, predictive analysis, and state evaluation of key parameters such as equipment level, system level, and plant level flow, realizes the organic combination of production management and model control, and builds a multi-region water production unit operation parameter and dynamic water balance real-time management system. Through the construction of the system, first, it can directly find unreasonable water use and reduce high water consumption caused by running, leaking and dripping; second, in the process of establishing dynamic water balance, from the perspective of water quality, it can find that the plant has high-quality low use, such as a system whose water quality is better than the design value after changing the water source, which can be applied to the next level system through modification; or the cooling water of the circulating pump is used as industrial water source; or the flushing water of the unit drainage tank and acid and alkali wastewater are recycled separately; the above can provide modification ideas for the cascade utilization of water resources, reduce long-term operation cost; third, it realizes the quantitative management of water quantity, which is convenient for cost accounting of water intake, drainage, chemicals and other items.

[0065] Establishing a multi-region water production unit intelligent early warning model

[0066] Based on the system operation mechanism characteristics, the core key parameters related to water quantity of each water production unit are summarized as the main input variables to establish a multi-region water production unit intelligent early warning model. The model predicts the normal value range and reasonable change rate of each control index according to different stages and different working conditions of system operation. When the model prediction exceeds, it will issue a corresponding early warning, realizing the continuous monitoring and diagnosis of water use state of water production unit by intelligent early warning model instead of personnel monitoring, discovering abnormalities that cannot be found by people in time, improving water use rationality and reducing high-quality low use. Through the development of early warning model, data are connected and barriers between systems are broken down. Based on data sharing and data operation as a means, intelligent early warning is realized.

[0067] The representative models of the intelligent early warning model development include: system leakage early warning, that is, there is a large difference between the total water quantity of the device outlet and the water tank inlet, and there is a large leakage in the middle; system flow mismatch early warning, that is, there is a difference between the front and rear flows of the device and the system; water supply system water cut early warning, that is, according to the current device output operation, it is predicted that the water supply quantity cannot meet the requirement of subsequent device operation after a certain time, and there is a risk of water cut; wastewater quantity overrun early warning, that is, according to the current wastewater discharge quantity, it is predicted that the wastewater treatment system cannot be processed in time after a certain time, and the wastewater treatment device needs to be started as soon as possible; high-quality low-use early warning, that is, the current system operation has the condition of using reclaimed water for water supply, but still uses other high-quality water sources for water supply; water intake or discharge quantity overrun early warning, that is, the current water intake or discharge quantity exceeds the previous average value or the set value; desalted water consumption or unit power generation water intake overrun early warning, that is, the real-time desalted water consumption or unit power generation water intake of the whole plant exceeds the standard value; imbalance rate overrun early warning, that is, the real-time imbalance rate of the whole plant exceeds the standard value;

[0068] Establishing a multi-region water production unit water quantity linkage distribution and regulation model

[0069] The bottom layer characteristic model of the water quantity influence parameter of each water production unit is mined, the parameter, time and space correlation mechanism characteristic model between units is established, on the basis of the original relative basis and independent operation of each unit, the depth optimization of the control strategy of each unit and between units is completed, the horizontal linkage simulation control system of'model result output + automatic control' is constructed, the load distribution mechanism is provided, the clear load instruction is provided for each system, the intelligent water quantity dynamic regulation and control based on the operation condition of each unit is realized, the purpose of closed loop control is achieved, and finally the safe + energy saving operation mode is achieved.

[0070] The representative models of the linkage distribution and regulation model development include:

[0071] The load distribution prediction model of the make-up water system is constructed by analyzing the influence factors of the desalted water consumption of the whole plant, and the influence factors mainly include unit load, unit industrial steam supply condition, unit heating condition, unit peak shaving start and stop and the like. By accurately predicting the load demand of the make-up water system, the operation time and operation load of the make-up water system are reasonably arranged, so that the make-up water system is always in the best operation state.

[0072] The application also provides a load intelligent distribution management method of a power plant water treatment system, and the specific implementation scheme is as follows:

[0073] The key parameters of air temperature, wind speed, unit load, heat supply and steam supply condition, and system operation condition are collected from the environment monitoring system and the DCS control system by the information collection system, and then the information collection system provides the data to the big data platform, and multiple data analysis models are established in the big data platform, mainly including a power plant industrial water consumption analysis model, a power plant desalted water consumption analysis model, a power plant wastewater generation analysis model, and a power plant reused water consumption analysis model, etc. The data analysis model analyzes and processes the data collected by the information collection system, so as to predict the industrial water consumption, the desalted water consumption, the wastewater generation, and the reused water consumption, and then the predicted data is used for load distribution of each water treatment system, so that the intelligent load distribution management of the power plant water treatment system is realized. At the same time, in the data analysis and processing process of the data model, the running state of the current system can be compared with the calculated result, and if the deviation is large, the running personnel can be timely warned to find and solve problems in advance.

[0074] Although the embodiments of the present application are described above in combination with the drawings, the present application is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and guiding, but not limiting. Those skilled in the art can make many forms under the guidance of the specification without departing from the scope protected by the claims of the present application, and these all belong to the protection of the present application.

Claims

1. A load intelligent distribution management system of a power plant water treatment system, characterized by, The application relates to a water treatment system intelligent load distribution method and system. The application comprises: An environment acquisition system for collecting data in the running environment of a water treatment system; A DCS control system for collecting water treatment system running data and receiving control commands from a big data platform to control the water system; An information acquisition system for receiving environment data collected by the environment acquisition system and running data collected by the DCS control system and transmitting the data to the big data platform; 2. The load intelligent allocation management system of the power plant water treatment system according to claim 1, characterized in that, A big data platform for displaying running data and environment data, performing data analysis based on the environment data and the running data, performing intelligent early warning based on the data analysis result and obtaining optimal load distribution instructions and sending the load distribution instructions to the DCS control system.

3. The load intelligent allocation management system of the power plant water treatment system according to claim 1, characterized in that, The big data platform displays running data and environment data based on a multi-region water production unit operation parameter and water balance dynamic monitoring system, wherein the multi-region water production unit operation parameter and water balance dynamic monitoring system is based on the production processes of raw water pretreatment, reclaimed water pretreatment, boiler makeup water treatment, industrial wastewater treatment and domestic water treatment, extracts water quantity data, proposes key control parameters and comprehensively establishes water quantity and water quality requirements.

4. The load intelligent allocation management system of the power plant water treatment system according to claim 3, characterized in that, The big data platform performs intelligent early warning based on a multi-region water production unit intelligent early warning model, wherein the multi-region water production unit intelligent early warning model predicts the normal value range and reasonable change rate of each control index corresponding to a plurality of coupling indexes according to different stages and different working conditions of system operation, and issues a corresponding early warning when the model prediction condition is exceeded.

5. The load intelligent allocation management system of the power plant water treatment system according to claim 4, characterized in that, The multi-region water production unit intelligent early warning model comprises system leakage early warning, system flow mismatch early warning, water supply system water cut-off early warning, wastewater quantity overrun early warning, high-quality low-use early warning, water intake or discharge quantity overrun early warning, desalination water consumption or unit power generation water intake quantity overrun early warning and imbalance rate overrun early warning. System leakage early warning: early warning when there is a difference exceeding a threshold value between total water quantity of equipment outlet water and water tank inlet water, and there is a leakage exceeding a threshold value in between.

6. The load intelligent allocation management system of the power plant water treatment system according to claim 4, characterized in that, System flow mismatch early warning: early warning when there is a difference between the flow of equipment and the flow of the system. Water supply system water cut-off early warning: early warning when the water supply quantity cannot meet the equipment operation requirement in the subsequent time according to the current equipment output operation.

7. The load intelligent allocation management system of the power plant water treatment system according to claim 4, characterized in that, Wastewater quantity overrun early warning: early warning when the wastewater treatment system cannot timely process in the subsequent time according to the current wastewater discharge quantity. High-quality low-use early warning: early warning when the system operation has the condition of using reclaimed water for water supply, but still uses other high-quality water sources for water supply.

8. The load intelligent distribution management system of a power plant water treatment system of claim 1, wherein, Water intake or discharge quantity overrun early warning: early warning when the water intake or discharge quantity exceeds the previous average value or the set value. Desalination water consumption or unit power generation water intake quantity overrun early warning: early warning when the real-time desalination water consumption or unit power generation water intake quantity of the whole plant exceeds the standard value. Imbalance rate overrun early warning: early warning when the real-time imbalance rate of the whole plant exceeds the standard value.

9. The load intelligent allocation management system of the power plant water treatment system of claim 1, wherein, The optimal load distribution instruction is acquired based on the environmental data and the operation data, and the linkage distribution regulation model is used, wherein the linkage distribution regulation model is specifically that, the water quantity influence parameter of each water production unit is subjected to bottom layer characteristic model mining, a parameter, time and space correlation mechanism characteristic model among the units is established, on the basis of the original relative basis and independent operation sequence control of each unit, the deep optimization of the control strategy of each unit and among the units is completed, a horizontal linkage simulation control system of "model result output + automatic control" is constructed, and a load distribution mechanism is provided to provide clear load instructions for each system.

10. A method for intelligent load distribution management of a power plant water treatment system, based on the intelligent load distribution management system of a power plant water treatment system according to any one of claims 1-9, characterized in that, The environmental data and the operation data of the water treatment system are collected from the environmental monitoring system and the DCS control system through the information collection system, and then the information collection system provides the environmental data and the operation data to the big data platform. The big data platform analyzes the environmental data and the operation data, acquires the optimal load distribution instruction according to the analysis result, and uses the predicted data to perform load distribution on each water treatment system. Meanwhile, the big data platform performs intelligent early warning during the data analysis and processing.

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