An intelligent management system and method applied to a hydropower station cloud platform

Abstract

The application discloses an intelligent management system and method applied to a hydropower station cloud platform, relates to cloud platform intelligent management, divides areas of a hydropower station, obtains equipment quantity and area environment characteristics in the area, when the area of the hydropower station is expanded, obtains similar areas of the expanded area, analyzes production speed differences of equipment in the expanded area and the similar areas, judges adaptation conditions of hydropower equipment to the expanded area, analyzes adaptation hydropower equipment proportion of the expanded area, inspects a network of the expanded area, confirms non-adaptive equipment types of the expanded area after inspection, schedules the non-adaptive equipment according to adaptive states of equipment in other areas, sets a monitoring period after scheduling is completed, and periodically schedules hydropower equipment and inspects the network of the area, so that, through real-time analysis of equipment production speed fluctuation conditions in the equipment management link, the application can identify interference equipment with operation abnormity, and ensures stable operation of hydropower equipment.

Description

Technical Field

[0001] This invention relates to intelligent management of cloud platforms, specifically an intelligent management system and method applied to a hydropower station cloud platform. Background Technology

[0002] The existing management of new areas in hydropower stations lacks environmental adaptability analysis. When expanding the management area of ​​a hydropower station, traditional methods often directly apply general area management templates without in-depth analysis of the environmental characteristics of the new area. Furthermore, no correlation and comparison mechanism has been established between the new area and existing mature areas, making it impossible to optimize the configuration of the new area based on the management experience of similar areas. This often leads to a disconnect between equipment layout and actual environmental requirements, increasing trial-and-error costs in new area management and potentially affecting the initial operational stability of equipment due to environmental incompatibility. Equipment compatibility assessment and scheduling lack precision and scientific rigor. Currently, in hydropower station equipment management, the assessment of equipment and area compatibility relies heavily on manual experience, without combining real-time equipment operation data for systematic analysis. This makes it difficult to accurately distinguish whether equipment malfunctions stem from internal performance fluctuations or external factors. Moreover, when equipment incompatibility is detected, the scheduling process does not prioritize the spatial distance to the compatible area, often selecting a non-optimal compatible area for equipment transfer. This results in excessive resource consumption during scheduling and an inability to quickly restore the equipment to an efficient operating state. Cloud platform operation and maintenance lacks a long-term closed-loop management mechanism. The current operation and maintenance of hydropower station cloud platforms mostly adopts a passive "fix after problems occur" model, without setting up a mechanism for regular review of equipment scheduling effectiveness and network conditions in all areas. This makes it difficult to detect potential problems in equipment adaptability caused by environmental changes and subtle anomalies in network transmission in a timely manner. Problems can easily accumulate and trigger sudden failures, failing to form a complete operation and maintenance closed loop and affecting the long-term stable operation of the cloud platform. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent management system and method for use on a hydropower station cloud platform, in order to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent management method applied to a hydropower station cloud platform, comprising the following steps: S1. Collect regional information of hydropower stations, divide the hydropower stations into regions, and obtain the number of equipment and regional environmental characteristics within the region; S2. When expanding the area of ​​a hydropower station, analyze and obtain similar areas of the expanded area; S3. Compare and analyze the production speed of the expanded area with that of similar areas, analyze the difference in production speed of equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. S4. Analyze the proportion of compatible hydropower equipment in the expanded area, inspect the network in the expanded area, and confirm the types of equipment that are not compatible in the expanded area after inspection. S5. After confirming the type of equipment that is not suitable, schedule the unsuitable equipment according to the equipment suitability status of other areas. S6. After the scheduling is completed, a monitoring cycle is set to conduct periodic scheduling of water and electricity equipment and network maintenance in the area.

[0005] Furthermore, in step S1, after authorization, the regional information of the hydropower station is collected. This regional information includes: equipment model information, equipment location information, and regional environmental information. The number of hydropower equipment types M in the hydropower station is obtained from the equipment model information. The hydropower station is then divided into N regions. The number of M-type hydropower equipment in the nth region is obtained from the equipment location information. The number of M-type hydropower equipment in the nth region is {A}. n_1 A n_2 ,…,A n_m ,…,A n_M}, where n=1,2,…,N, A n_m A represents the number of water and electricity equipment of type m in the nth region. n_M This represents the number of M-type hydropower equipment in the n-th region. Real-time preprocessing and normalization of regional environmental information yields the regional environmental characteristics of N regions. The regional environmental characteristic of the n-th region is {B}. n_1 B n_2 ,…,B n_i ,…,B n_I}, where I represents the number of regional environmental features, B n_i This represents the environmental characteristics of the i-th region within the n-th region, providing a compliant and accurate foundation of data support for the intelligent management of the hydropower station cloud platform. It first collects key regional information through an authorization mechanism, ensuring the compliance and security of data acquisition, mitigating the risks of unauthorized information acquisition, and guaranteeing the legality of information sources. Subsequently, the collected information is sorted and standardized, defining the classification framework of hydropower equipment and the structured scope of management areas, clarifying the distribution of equipment in each region, and transforming regional environmental information into standardized features that can be directly used for subsequent analysis. This systematic processing avoids blind spots in subsequent management due to chaotic or missing basic data, providing a reliable basis for subsequent regional similarity comparisons and equipment compatibility judgments, effectively improving the starting point quality of the overall management process.

[0006] Furthermore, in step S2, when expanding the q-th region of the hydropower station, the I-th regional environmental characteristics of the q-th region are obtained, regional monitoring is performed on the q-th region, and the n-th region is compared with the q-th region to calculate the regional similarity C between the n-th region and the q-th region.n_q : ; Among them B q_i Let the environmental characteristics of the i-th region be represented by the q-th region. Substitute each characteristic into n=1,2,…,N to obtain the regional similarity {C} of the N regions to the q-th region. 1_q C 2_q ,…,C n_q ,…,C N_q If C n_q >C0, determine if the nth region is a similar region to the qth region, where C0 is a preset region similarity threshold; otherwise, determine if the nth region is a dissimilar region to the qth region, and thus obtain X similar regions of the qth region, numbering them as {D}. q_1 D q_2 ,…,D q_x ,…,D q_X}, where D q_x This provides a precise reference for the management of new areas in hydropower stations, effectively improving the efficiency of integrating new areas into the overall management system. When expanding into new areas, by comparing the environmental characteristics of the new and existing areas, similar areas with comparable environmental conditions are scientifically identified. This allows the management of new areas to directly draw on the mature experiences of similar areas, avoiding blind exploration from scratch. This similarity-based judgment ensures the adaptability of the new area management model to the actual environment, reduces trial-and-error costs, and enables the new area to quickly integrate with the existing management system, enhancing the coherence and rationality of overall management and laying a practical foundation for subsequent equipment configuration and scheduling.

[0007] In step S3, the regional status of the q-th region is analyzed in real time. In the q-th region, the number of type m hydropower equipment is Y. The production speed of the Y type m hydropower equipment is queried through the cloud platform as {E}. 1_m_q E 2_m_q ,…,E y_m_q ,…,E Y_m_q}, where E y_m_q Indicates device F y_m_q production speed, F y_m_q This represents the y-th type m-th hydroelectric equipment in the q-th region, calculated by device F. y_m_q Production speed difference G y_m_q : ; If G y_m_qIf the difference in production speed exceeds the preset threshold, the y-th hydropower equipment is judged to have a large fluctuation in production speed at the current time and is designated as an interfering device; otherwise, the y-th hydropower equipment is judged to have a small fluctuation in production speed at the current time and is designated as a reference hydropower equipment. Substitute y=1,2,…,Y one by one to obtain the reference production speed of the M-type hydropower equipment in the q-th region. The reference production speed of the m-th type hydropower equipment in the q-th region is the average of the production speeds of the reference hydropower equipment in the m-th type hydropower equipment in the q-th region. Analyze the production adaptability G of the m-th type of hydropower equipment in the q-th region. q_m The production adaptability of the m-th type of hydropower equipment in the q-th region is: the ratio of the reference production speed of the m-th type of hydropower equipment in the q-th region to the average reference production speed of the m-th type of reference hydropower equipment in the N regions. A production adaptability threshold G is set, which can be set to 0.8. If G... q_m If the value is greater than G, then the m-th type of hydropower equipment is determined to be a suitable hydropower equipment for the q-th region; otherwise, the m-th type of hydropower equipment is determined to be an unsuitable hydropower equipment for the q-th region. By tracking the equipment production speed in real time, the system distinguishes between interfering equipment with large fluctuations and stable reference equipment, effectively eliminating abnormal data interference and ensuring that the reference production speed used for analysis truly reflects the normal performance of the equipment. Based on this, the reference production speed of the equipment in the new region is compared with the average level of similar equipment in the existing region. The degree of matching between the equipment and the region is evaluated through quantitative assessment of production adaptability, avoiding the subjectivity of relying on experience-based judgment. This data-driven analysis method can objectively identify suitable and unsuitable equipment types, providing a reliable basis for subsequent targeted scheduling and improving the scientificity and accuracy of equipment-region matching judgment.

[0008] Furthermore, in step S4, m=1,2,…,M, the types of compatible hydropower equipment in the q-th region are obtained, where the number of compatible hydropower equipment types is Q1 and the number of incompatible hydropower equipment types is Q2. If Q1 / M is greater than the pre-set threshold for the proportion of compatible hydropower equipment in a region, it is determined that the Q2 type of hydropower equipment is not suitable for the q-th region; otherwise, it is determined that there is a cloud platform transmission risk in the q-th region. When it is determined that there is a cloud platform transmission risk in the q-th region, the network in the q-th region is inspected, and the network information in the q-th region is checked. The network information includes packet loss rate, bit error rate, and jitter coefficient. When the equipment compatibility of a certain region conforms to the normal environment matching law, the proportion of compatible equipment types is usually high; when there is a transmission risk in the cloud platform (such as packet loss, bit error, jitter), the monitoring data of equipment production speed will be distorted, resulting in abnormal compatibility calculation results, which is manifested as a significant decrease in the proportion of compatible equipment. Packet loss rate is the ratio of the number of data packets lost to the total number of data packets sent in the network during a maintenance cycle. The formula is number of lost packets / total number of sent packets, and the result is a decimal between 0 and 1 (or in percentage form), without units.

[0009] Bit error rate (BER) is the ratio of the number of erroneous bits (binary bits) during transmission to the total number of transmitted bits within a maintenance cycle. The formula is: number of erroneous bits / total number of bits, and the result is typically 10⁻. 6 ~10⁻ 9 A decimal of the order of magnitude, without units.

[0010] Jitter coefficient represents the ratio of the standard deviation of network transmission delay to the average delay during the maintenance cycle. The formula is standard deviation of delay / average delay. The result is a non-negative decimal and has no unit (eliminating the influence of the time unit of delay). If the network information in region q meets the pre-set threshold requirements, it is determined that the Q2 type of hydropower equipment is not suitable for region q; otherwise, after repairing the network in region q, the types of equipment that are not suitable for region q are re-analyzed. By using hierarchical judgment, the root cause of equipment incompatibility in the new region is accurately located, effectively avoiding misjudgment and processing deviations. First, the compatibility status of all types of equipment in the new region is comprehensively statistically analyzed. Based on the threshold of the proportion of compatible equipment, two possibilities are initially distinguished: equipment incompatibility itself and cloud platform transmission risks. When transmission risks are suspected, network problems are investigated by checking key network indicators, rather than directly determining equipment incompatibility, reducing misjudgments of equipment due to network anomalies. If the network indicators meet the standards, it is confirmed that the equipment is not suitable for the region; if the network does not meet the standards, the equipment compatibility is re-analyzed after repair. This logic of "first investigating external risks, then confirming equipment problems" ensures that each step of the judgment has a clear basis, avoiding blindly dispatching equipment and ensuring the targeted handling of subsequent problems, thus improving the accuracy and efficiency of management decisions.

[0011] Furthermore, in step S5, if the m-th type of hydropower equipment is suitable for the q-th region, the scheduling of the m-th type of hydropower equipment in the q-th region is completed; if the m-th type of hydropower equipment is not suitable for the q-th region, regions suitable for the m-th type of hydropower equipment in the N regions are called, and the region closest to the q-th region is selected for scheduling the m-th type of hydropower equipment. The m-th type of equipment is moved from the q-th region to the region closest to the q-th region. After the scheduling is completed, m=1,2,…,M is substituted one by one to complete the scheduling of the M-th type of hydropower equipment; for unsuitable equipment, the region that is suitable for this type of equipment and the closest region is selected from the existing regions for transfer, reducing resource consumption and time costs in the scheduling process. This on-demand scheduling method ensures that suitable equipment stays in the suitable region to exert its best performance, and avoids inefficient operation of unsuitable equipment in unsuitable environments by transferring unsuitable equipment nearby. The scheduling process comprehensively covers all types of equipment, allowing each type of equipment to be in a suitable environment, effectively improving the rationality of hydropower station equipment configuration and overall operating efficiency.

[0012] Furthermore, in step S6, after completing the scheduling of Class M hydropower equipment, hydropower equipment scheduling and network maintenance are carried out in all areas at preset monitoring intervals.

[0013] An intelligent management system for a hydropower station cloud platform includes: a regional information collection and division module, an expanded regional similarity analysis module, a production speed comparison analysis module, an adaptation ratio analysis module, an equipment scheduling module, and a periodic equipment monitoring module. The regional information collection and division module is used to collect regional information of the hydropower station, divide the hydropower station into regions, and obtain the number of equipment and regional environmental characteristics within the region. The expansion area similarity analysis module is used to analyze and obtain similar areas when expanding the area of ​​a hydropower station. The production speed comparison and analysis module is used to compare and analyze the production speed of the expanded area with that of similar areas, analyze the difference in production speed of equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. The adaptation ratio analysis module is used to analyze the adaptation ratio of hydropower equipment in the expansion area, inspect the network in the expansion area, and identify the types of equipment that are not suitable for the expansion area after inspection. The equipment scheduling module is used to identify unsuitable equipment types and then schedule the unsuitable equipment based on the adaptability status of other areas to the equipment. The periodic equipment monitoring module is used to set a monitoring cycle after the scheduling is completed, and to perform periodic scheduling of water and electricity equipment and network maintenance in the area.

[0014] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: Firstly, it enhances the precision and adaptability of regional management. In managing newly added regions, this method processes regional data, deeply analyzes regional environmental characteristics, and compares them with existing regions. This allows for the rapid identification of mature management regions with similar conditions to the new region, directly reusing equipment configuration and management experience from similar regions. This management model based on environmental feature matching avoids blind exploration when managing new regions, making regional division and equipment layout more aligned with actual environmental needs. Furthermore, through preprocessing and feature extraction of regional environmental information, it can more accurately grasp the environmental differences between different regions, providing a scientific basis for subsequent equipment configuration and effectively improving the overall coordination and adaptability of regional management.

[0015] On the one hand, optimizing the rationality and operational efficiency of equipment scheduling. In the equipment management stage, by analyzing fluctuations in equipment production speed in real time, abnormal and interfering equipment can be accurately identified, ensuring that the compatibility between equipment and the region is judged based on data from stable reference equipment. This accurate compatibility judgment can avoid efficiency losses caused by incompatible equipment operating in the same area for a long time. At the same time, when compatibility problems occur, by distinguishing between equipment incompatibility and network transmission risks, the network condition is checked first before addressing the equipment problem, reducing the time cost of problem localization. In addition, when scheduling across regions, priority is given to selecting the nearest compatible region, further reducing the resource consumption of equipment scheduling and improving the overall operating efficiency of equipment.

[0016] On the other hand, it ensures the long-term stability and risk controllability of system operation and maintenance. After equipment scheduling is completed, a comprehensive review of equipment and networks in all areas is conducted at fixed intervals. This allows for the timely detection of potential problems not exposed in previous management, such as fluctuations in equipment adaptability due to environmental changes and subtle anomalies in network transmission parameters. This periodic monitoring mechanism avoids sudden failures caused by the accumulation of problems. Furthermore, each maintenance check includes the rationality of equipment scheduling and network status checks, forming a closed loop of cloud platform intelligent management: "Management - Scheduling - Monitoring - Optimization." This ensures the entire cloud platform management system remains under control, effectively reducing long-term operational risks and guaranteeing the stable and long-term management of the hydropower station as a whole. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of an intelligent management system applied to a hydropower station cloud platform according to the present invention; Figure 2 This is a flowchart of an intelligent management method for a hydropower station cloud platform according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent management method applied to a hydropower station cloud platform, comprising the following steps: S1. Collect regional information of hydropower stations, divide the hydropower stations into regions, and obtain the number of equipment and regional environmental characteristics within the region; S2. When expanding the area of ​​a hydropower station, analyze and obtain similar areas of the expanded area; S3. Compare and analyze the production speed of the expanded area with that of similar areas, analyze the difference in production speed of equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. S4. Analyze the proportion of compatible hydropower equipment in the expanded area, inspect the network in the expanded area, and confirm the types of equipment that are not compatible in the expanded area after inspection. S5. After confirming the type of equipment that is not suitable, schedule the unsuitable equipment according to the equipment suitability status of other areas. S6. After the scheduling is completed, a monitoring cycle is set to conduct periodic scheduling of water and electricity equipment and network maintenance in the area.

[0020] In step S1, after authorization, regional information of the hydropower station is collected. This regional information includes equipment model information, equipment location information, and regional environmental information. The regional environmental information can be collected through a sensor array and includes, but is not limited to, ambient temperature, ambient humidity, and light intensity. The number of hydropower equipment types M in the hydropower station is obtained from the equipment model information. The hydropower station is then divided into N regions. The number of M-type hydropower equipment in the nth region is obtained from the equipment location information. The number of M-type hydropower equipment in the nth region is {A}. n_1 A n_2 ,…,A n_m ,…,A n_M}, where n=1,2,…,N, A n_m A represents the number of water and electricity equipment of type m in the nth region. n_MThis represents the number of M-type hydropower equipment in the n-th region. Real-time preprocessing and normalization of regional environmental information yields the regional environmental characteristics of N regions. The regional environmental characteristic of the n-th region is {B}. n_1 B n_2 ,…,B n_i ,…,B n_I}, where I represents the number of regional environmental features, B n_i B represents the environmental characteristics of the i-th region in the n-th region. n_i The value range is (0,1).

[0021] In step S2, when expanding the q-th region of the hydropower station, the I-th regional environmental characteristics of the q-th region are obtained, regional monitoring is performed on the q-th region, and the n-th region is compared with the q-th region to calculate the regional similarity C between the n-th region and the q-th region. n_q : ; Among them B q_i B represents the environmental characteristics of the i-th region in the q-th region. q_i The value range is (0,1]. Substituting each value into n=1,2,…,N, we obtain the regional similarity {C} of N regions to the q-th region. 1_q C 2_q ,…,C n_q ,…,C N_q If C n_q >C0, determine if the nth region is a similar region to the qth region, where C0 is a preset region similarity threshold; otherwise, determine if the nth region is a dissimilar region to the qth region, and thus obtain X similar regions of the qth region, numbering them as {D}. q_1 D q_2 ,…,D q_x ,…,D q_X}, where D q_x Let x be the x-th similar region of the q-th region.

[0022] In step S3, the regional status of the q-th region is analyzed in real time. In the q-th region, the number of type m hydropower equipment is Y. The production speed of the Y type m hydropower equipment is queried through the cloud platform as {E}. 1_m_q E 2_m_q ,…,E y_m_q ,…,E Y_m_q}, where E y_m_q Indicates device F y_m_q production speed, F y_m_q This represents the y-th type m-th hydroelectric equipment in the q-th region, calculated by device F. y_m_q Production speed difference Gy_m_q : ; If G y_m_q If the difference in production speed exceeds the preset threshold, the y-th hydropower equipment is judged to have a large fluctuation in production speed at the current time and is designated as an interfering device; otherwise, the y-th hydropower equipment is judged to have a small fluctuation in production speed at the current time and is designated as a reference hydropower equipment. Substitute y=1,2,…,Y one by one to obtain the reference production speed of the M-type hydropower equipment in the q-th region. The reference production speed of the m-th type hydropower equipment in the q-th region is the average of the production speeds of the reference hydropower equipment in the m-th type hydropower equipment in the q-th region. Analyze the production adaptability G of the m-th type of hydropower equipment in the q-th region. q_m The production adaptability of the m-th type of hydropower equipment in the q-th region is: the ratio of the reference production speed of the m-th type of hydropower equipment in the q-th region to the average reference production speed of the m-th type of reference hydropower equipment in the N regions. A production adaptability threshold G is set. If G... q_m If the value is greater than G, then the m-th type of hydropower equipment is determined to be a suitable hydropower equipment for the q-th region; otherwise, the m-th type of hydropower equipment is determined to be an unsuitable hydropower equipment for the q-th region.

[0023] In step S4, m = 1, 2, ..., M, the types of compatible hydropower equipment in the q-th region are obtained, where the number of compatible hydropower equipment types is Q1 and the number of incompatible hydropower equipment types is Q2. If Q1 / M is greater than the pre-set threshold for the proportion of compatible hydropower equipment in a region, then it is determined that the Q2 type of hydropower equipment is not suitable for the q-th region; otherwise, it is determined that there is a cloud platform transmission risk in the q-th region. When it is determined that there is a cloud platform transmission risk in the q-th region, the network of the q-th region is inspected, and the network information of the q-th region is checked. The network information includes packet loss rate, bit error rate, and jitter coefficient. If the network information of the q-th region meets the pre-set threshold requirements, it is determined that the Q2 type of hydropower equipment is not suitable for the q-th region; otherwise, after inspecting the network of the q-th region, the types of equipment that are not suitable for the q-th region are re-analyzed.

[0024] In step S5, if the m-th type of hydropower equipment is suitable for the q-th region, the scheduling of the m-th type of hydropower equipment in the q-th region is completed; if the m-th type of hydropower equipment is not suitable for the q-th region, the region suitable for the m-th type of hydropower equipment in the N regions is called, and the region closest to the q-th region is selected for scheduling of the m-th type of hydropower equipment. The m-th type of equipment is moved from the q-th region to the region closest to the q-th region. After the scheduling is completed, m=1,2,…,M is substituted one by one to complete the scheduling of the M-th type of hydropower equipment.

[0025] In step S6, after the scheduling of Class M hydropower equipment is completed, hydropower equipment scheduling and network maintenance are carried out in all areas at preset monitoring intervals.

[0026] An intelligent management system applied to a hydropower station cloud platform, the system comprising: a regional information collection and division module, an expanded regional similarity analysis module, a production speed comparison analysis module, an adaptation ratio analysis module, an equipment scheduling module, and a periodic equipment monitoring module; The regional information collection and division module is used to collect regional information of hydropower stations, divide hydropower stations into regions, and obtain the number of equipment and regional environmental characteristics within each region. The expansion area similarity analysis module is used to analyze and obtain similar areas when expanding the area of ​​a hydropower station. The production speed comparison and analysis module is used to compare and analyze the production speed of the expanded area with that of similar areas, analyze the differences in the production speed of the equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. The compatibility ratio analysis module is used to analyze the compatibility ratio of water and electricity equipment in the expanded area, to inspect the network in the expanded area, and to identify the types of equipment that are not compatible in the expanded area after inspection. The equipment scheduling module is used to identify unsuitable equipment types and then schedule the unsuitable equipment based on the suitability status of other areas. The periodic equipment monitoring module is used to set the monitoring cycle after the scheduling is completed, and to carry out periodic scheduling of water and electricity equipment and network maintenance in the area.

[0027] Example 1: Introducing cloud platform intelligent management methods, focusing on new area expansion and equipment optimization scheduling, the implementation work is carried out according to the following steps.

[0028] In the basic information collection and processing phase (corresponding to S1), the hydropower station first applies for data collection authorization from the cloud platform management system. After authorization is granted, technical personnel conduct a comprehensive survey of all equipment and regional information at the station: recording the models of various hydropower equipment, such as generator sets, transmission transformers, cooling water pumps, etc.; marking the installation location of each piece of equipment, such as the generator set area on the first floor of the plant, the control room on the second floor of the plant, and the area next to the transmission lines; and simultaneously collecting environmental information for each area, including water temperature, air humidity, generator room temperature, and wind conditions around the transmission lines. Subsequently, the system organizes the collected information: identifying the core types of equipment at the station, dividing the area into multiple management zones, and clarifying the distribution of different types of equipment within each zone; and preprocessing the environmental information, removing abnormal data, and converting environmental parameters of different dimensions into standardized feature data to provide a foundation for subsequent analysis.

[0029] When a hydropower station plans to expand to a new auxiliary power generation area (corresponding to S2), technicians first collect environmental characteristics of the new area, including water temperature, surrounding air humidity, and the temperature of the equipment installation site, while simultaneously activating real-time monitoring of the area. The system compares the environmental characteristics of the new area with those of the previously defined existing areas, analyzing the degree of similarity in environmental conditions. If an existing area and the new area have a high degree of matching in core environmental characteristics such as water temperature and humidity, it is determined to be a similar area to the new area, and subsequent management of the new area can refer to the equipment configuration experience of this similar area.

[0030] Entering the new area equipment analysis phase (corresponding to S3), technicians use the cloud platform to monitor the real-time operating status of various equipment within the new area. Taking the generator sets in the new area as an example, the system tracks the power generation speed of each generator set in real time. If the power generation speed of a generator set fluctuates frequently and significantly, the system identifies it as interfering equipment and excludes it from subsequent analysis; generator sets with stable operating speeds are used as reference equipment. The system calculates the average power generation of the reference equipment and compares this average with the average power generation of similar reference equipment in the existing areas of the entire station to assess the production adaptability of the new area's generator sets to the current environment.

[0031] During the compatibility troubleshooting phase (corresponding to S4), the system statistically analyzes the compatibility status of all types of devices within the new area. If the percentage of compatible device types reaches the preset requirement, it is determined that a small number of incompatible devices (such as some cooling water pumps) are simply not compatible with the new area environment. If the percentage of compatible devices is too low, the network status of the new area is checked first, including packet loss, bit error rate, and signal jitter in cloud platform data transmission. If the network indicators meet the operational requirements, it is still determined that the device is incompatible with the area. If there are network problems, technicians inspect the network cable interfaces and signal transmitters in the new area, and reassess the device compatibility after the inspection is completed.

[0032] During the equipment scheduling phase (corresponding to S5), for equipment adapted to the new area, such as a certain model of generator set, its installation, commissioning, and scheduling in the new area are directly completed to ensure normal operation. For incompatible equipment, such as some cooling water pumps, the system queries the existing areas of the entire station that are compatible with that type of pump, selects the nearest chilled water circulation area to the new area, and transfers the incompatible cooling water pumps to that area to avoid damage to the equipment due to long-distance transportation. After all types of equipment scheduling are completed, the system completes the collaborative management of the new area and all equipment in the station.

[0033] During the long-term operation and maintenance phase (corresponding to S6), the hydropower station formulates a regular monitoring plan. After equipment scheduling is completed, all areas of the station are inspected at fixed intervals: the operating status of equipment in each area is checked to confirm whether there are any compatibility changes; the network transmission status in each area is checked, and network anomalies are dealt with in a timely manner. Through periodic monitoring, it is ensured that all equipment in the station is in a stable operating state for a long time, and the cloud platform management system continues to play its role.

[0034] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary sensing device embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An intelligent management method applied to a hydropower station cloud platform, characterized in that: The method includes the following steps: S1. Collect regional information of hydropower stations, divide the hydropower stations into regions, and obtain the number of equipment and regional environmental characteristics within the region; S2. When expanding the area of ​​a hydropower station, analyze and obtain similar areas of the expanded area; S3. Compare and analyze the production speed of the expanded area with that of similar areas, analyze the difference in production speed of equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. S4. Analyze the proportion of compatible hydropower equipment in the expanded area, inspect the network in the expanded area, and confirm the types of equipment that are not compatible in the expanded area after inspection. S5. After confirming the type of equipment that is not suitable, schedule the unsuitable equipment according to the equipment suitability status of other areas. S6. After the scheduling is completed, a monitoring cycle is set to conduct periodic scheduling of water and electricity equipment and network maintenance in the area.

2. The intelligent management method applied to a hydropower station cloud platform according to claim 1, characterized in that: In step S1, after authorization, the regional information of the hydropower station is collected. This regional information includes equipment model information, equipment location information, and regional environmental information. The number of hydropower equipment types M in the hydropower station is obtained from the equipment model information. The hydropower station is then divided into N regions. The number of M-type hydropower equipment in the nth region is obtained from the equipment location information. The number of M-type hydropower equipment in the nth region is {A}. n_1 A n_2 ,…,A n_m ,…,A n_M }, where n=1,2,…,N, A n_m A represents the number of water and electricity equipment of type m in the nth region. n_M This represents the number of M-type hydropower equipment in the n-th region. Real-time preprocessing and normalization of regional environmental information yields the regional environmental characteristics of N regions. The regional environmental characteristic of the n-th region is {B}. n_1 B n_2 ,…,B n_i ,…,B n_I }, where I represents the number of regional environmental features, B n_i This represents the environmental characteristics of the i-th region in the n-th region.

3. The intelligent management method applied to a hydropower station cloud platform according to claim 2, characterized in that: In step S2, when expanding the q-th region of the hydropower station, the I-th regional environmental characteristics of the q-th region are obtained, regional monitoring is performed on the q-th region, and the n-th region is compared with the q-th region to calculate the regional similarity C between the n-th region and the q-th region. n_q : ； Among them B q_i Let the environmental characteristics of the i-th region be represented by the q-th region. Substitute each characteristic into n=1,2,…,N to obtain the regional similarity {C} of the N regions to the q-th region. 1_q C 2_q ,…,C n_q ,…,C N_q If C n_q >C0, determine that the nth region is a similar region to the qth region, where C0 is a preset threshold for the degree of similarity between regions; Otherwise, determine that the nth region is a dissimilar region of the qth region, and then obtain X similar regions of the qth region. Number the X similar regions of the qth region as {D}. q_1 D q_2 ,…,D q_x ,…,D q_X }, where D q_x Let x be the x-th similar region of the q-th region.

4. The intelligent management method applied to a hydropower station cloud platform according to claim 3, characterized in that: In step S3, the regional status of the q-th region is analyzed in real time. In the q-th region, the number of type m hydropower equipment is Y. The production speed of the Y type m hydropower equipment is queried through the cloud platform as {E}. 1_m_q E 2_m_q ,…,E y_m_q ,…,E Y_m_q }, where E y_m_q Indicates device F y_m_q production speed, F y_m_q This represents the y-th type m-th hydroelectric equipment in the q-th region, calculated by device F. y_m_q Production speed difference G y_m_q : ； If G y_m_q If the difference in production speed exceeds the preset threshold, the y-th hydroelectric device is judged to have a large fluctuation in production speed at the current time and is designated as an interfering device; otherwise, the y-th hydroelectric device is judged to have a small fluctuation in production speed at the current time and is designated as a reference hydroelectric device. Substituting y=1,2,…,Y into each value, the reference production speed of the M-type hydroelectric devices in the q-th region is obtained. The reference production speed of the m-th type hydroelectric device in the q-th region is the average of the production speeds of the reference hydroelectric devices in the m-th type hydroelectric devices in the q-th region.

5. The intelligent management method applied to a hydropower station cloud platform according to claim 4, characterized in that: Analyze the production adaptability G of the m-th type of hydropower equipment in the q-th region. q_m The production adaptability of the m-th type of hydropower equipment in the q-th region is: the ratio of the reference production speed of the m-th type of hydropower equipment in the q-th region to the average reference production speed of the m-th type of reference hydropower equipment in the N regions. A production adaptability threshold G is set. If G... q_m If the value is greater than G, then the m-th type of hydropower equipment is determined to be a suitable hydropower equipment for the q-th region; otherwise, the m-th type of hydropower equipment is determined to be an unsuitable hydropower equipment for the q-th region.

6. The intelligent management method applied to a hydropower station cloud platform according to claim 5, characterized in that: In step S4, m = 1, 2, ..., M, the types of compatible hydropower equipment in the q-th region are obtained, where the number of compatible hydropower equipment types is Q1 and the number of incompatible hydropower equipment types is Q2. If Q1 / M is greater than the pre-set threshold for the proportion of compatible hydropower equipment in a region, then it is determined that the Q2 type of hydropower equipment is not suitable for the q-th region; otherwise, it is determined that there is a cloud platform transmission risk in the q-th region. When it is determined that there is a cloud platform transmission risk in the q-th region, the network of the q-th region is inspected, and the network information of the q-th region is checked. The network information includes packet loss rate, bit error rate, and jitter coefficient. If the network information of the q-th region meets the pre-set threshold requirements, it is determined that the Q2 type of hydropower equipment is not suitable for the q-th region; otherwise, after inspecting the network of the q-th region, the types of equipment that are not suitable for the q-th region are re-analyzed.

7. The intelligent management method applied to a hydropower station cloud platform according to claim 6, characterized in that: In step S5, if the m-th type of hydropower equipment is suitable for the q-th region, the scheduling of the m-th type of hydropower equipment in the q-th region is completed; if the m-th type of hydropower equipment is not suitable for the q-th region, the region suitable for the m-th type of hydropower equipment in the N regions is called, and the region closest to the q-th region is selected for scheduling of the m-th type of hydropower equipment. The m-th type of equipment is moved from the q-th region to the region closest to the q-th region. After the scheduling is completed, m=1,2,…,M is substituted one by one to complete the scheduling of the M-th type of hydropower equipment.

8. The intelligent management method applied to a hydropower station cloud platform according to claim 6, characterized in that: In step S6, after the scheduling of Class M hydropower equipment is completed, hydropower equipment scheduling and network maintenance are carried out in all areas at preset monitoring intervals.

9. An intelligent management system applied to a hydropower station cloud platform, wherein the system is applied to the intelligent management method for a hydropower station cloud platform as described in any one of claims 1-8, characterized in that: The system includes: a regional information collection and division module, an expanded regional similarity analysis module, a production speed comparison analysis module, an adaptation ratio analysis module, an equipment scheduling module, and a periodic equipment monitoring module; The regional information collection and division module is used to collect regional information of the hydropower station, divide the hydropower station into regions, and obtain the number of equipment and regional environmental characteristics within the region. The expansion area similarity analysis module is used to analyze and obtain similar areas when expanding the area of ​​a hydropower station. The production speed comparison and analysis module is used to compare and analyze the production speed of the expanded area with that of similar areas, analyze the difference in production speed of equipment in the expanded area and similar areas, and determine the adaptability of the hydropower equipment to the expanded area. The adaptation ratio analysis module is used to analyze the adaptation ratio of hydropower equipment in the expansion area, inspect the network in the expansion area, and identify the types of equipment that are not suitable for the expansion area after inspection. The equipment scheduling module is used to identify unsuitable equipment types and then schedule the unsuitable equipment based on the adaptability status of other areas to the equipment. The periodic equipment monitoring module is used to set a monitoring cycle after the scheduling is completed, and to perform periodic scheduling of water and electricity equipment and network maintenance in the area.

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