A cloud edge collaboration-based pumped storage information price management system

Abstract

A cloud edge collaborative pumped storage information price management system includes a cloud component identification module that compares features to generate a component identification mapping set, an edge energy flow collection module that tracks operating states to generate an energy flow operating record set, a cloud edge price energy fusion module that correlates cost energy time series to generate a price energy corresponding information set, a partition information management module that analyzes response trends to develop a partition price management task set, and a hierarchical price signal release module that schedules and issues and archives to generate an information price summary table. Through component attribute feature extraction and comparison, data is uniformly encoded, energy flow state is dynamically collected to form a continuous time series chain, cost data and energy flow events are aligned to enhance cost matching, regional price energy coupling is realized to identify linkage, partition trend stability is classified to improve control accuracy, a periodic release mechanism ensures continuous and adaptive updates, and pumped storage construction price energy collaboration and dynamic balance are promoted.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and in particular to a cloud-edge collaborative pumped storage information price release and management system. Background Technology

[0002] The field of resource management technology encompasses a system of methodologies for coordinating and managing resource usage information in energy and infrastructure construction. Its core components include resource data acquisition, information transmission, statistical analysis, and price information management. This technology covers resource allocation methods based on data-driven and networked approaches. Through unified management of resource, equipment, and material data, it achieves structured and standardized information processing. In the energy sector, this technology is primarily applied to the operation, maintenance, and construction management of power, water conservancy, and energy storage projects. By constructing data integration mechanisms and collaborative computing structures, it provides fundamental information support for the entire project management process. With the advancement of new power systems, resource management technology is gradually evolving towards a multi-level networked computing architecture, forming a collaborative system characterized by centralized cloud processing and edge-response computing, thereby ensuring complete data acquisition and real-time updates.

[0003] Among them, the cloud-edge collaborative pumped storage information price release and management system refers to an equipment and material price information management system for the entire construction process of pumped storage projects. Addressing issues such as scattered data sources, delayed updates, inconsistent formats, and a lack of a unified release system for equipment and material price data, it establishes an architecture centered on centralized cloud computing and rapid edge processing. Its main tasks include collecting and classifying equipment and material data from various pumped storage projects, performing statistical analysis and summarization using standardized data formats, executing price calculations, trend analysis, and information integration through cloud computing nodes to generate standardized price information data, and completing real-time data updates and status transmission at the construction site through edge nodes to ensure data interaction and price information sharing between different areas. This system is based on a multi-layered data transmission link and achieves unified management and information price release of pumped storage equipment and material price data through cloud-edge collaboration.

[0004] Existing technologies suffer from a lack of information structure in data management, and the absence of attribute-based identification and classification mechanisms for engineering components. This results in insufficient correlation between on-site data, a lack of continuous temporal matching between equipment operation and energy consumption records, separation of cost information from energy flow status, unclear correspondence between cost changes and energy consumption fluctuations, information gaps between regional data, making it difficult to form targeted analyses based on operating conditions, and lagging price data adjustments in different zones, leading to asynchronous risk warnings and management responses. Fixed price update cycles lack linkage and matching with energy consumption characteristics, thus limiting the ability of pumped storage projects to dynamically perceive and regulate prices in complex construction scenarios. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud-edge collaborative pumped storage information price release and management system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a cloud-edge collaborative pumped storage information price release and management system, the system comprising: The cloud-based component identification module acquires information on the volume, material, and construction environment of water diversion tunnels, surge tanks, and factory buildings. It then calls a preset library to compare feature differences and categorizes them into price categories. The module extracts component elements, associates pricing items with category codes, and generates a component identification mapping set. The edge power flow acquisition module identifies the device categories and spatial distribution in the mapping set based on the components, tracks the power and load status of edge node devices, records the power flow signal waveform and matches the trajectory, organizes the power flow status change records, removes invalid data and retains the operation records, and generates a power flow operation record set. The cloud-edge-price-energy fusion module determines the cost range of each energy flow event based on the time-series characteristic data in the energy flow operation record set, extracts the details of manufacturing, transportation and material consumption costs, aligns the cost timestamps with the energy flow change points, retrieves overlapping segments to establish price-energy associations, and generates a price-energy corresponding information set. The partition information management module extracts price-energy linkage entries based on the regional index and price-energy ratio fluctuation of the price-energy corresponding information set, analyzes the cost response trend and compares it with the management benchmark, identifies the deviation type and classifies the level, determines the management priority, and generates a partition price management task set.

[0007] As a further embodiment of the present invention, the component identification mapping set includes a component category index, spatial location number, price classification label, structural element parameters, and standard attribute code; the energy flow operation record set includes equipment power sequence, load response characteristics, energy flow timing nodes, signal valid segments, and operation status identifiers; the price-energy correspondence information set includes cost matching segments, energy flow event numbers, time alignment segments, price-energy association indexes, and regional equipment mapping items; and the partitioned price management task set includes partition priority levels, data update labels, monitoring task sequences, execution cycle plans, and scheduling timing rules.

[0008] As a further aspect of the present invention, the cloud-based component identification module includes a volume density comparison submodule, a component difference classification submodule, and a mapping set generation submodule; The volume density comparison submodule acquires the volume, material density, and construction environment information of the water diversion tunnel section, surge tank, and powerhouse. In the cloud computing node, it calls the volume benchmark value, material density benchmark value, and environmental benchmark value in the standard attribute library, calculates the ratio difference between the volume information and the benchmark value, calculates the difference between the material density information and the benchmark value, and performs deviation judgment on the construction environment information according to the environmental benchmark value range to generate a component attribute difference set. The component difference classification submodule, based on the component attribute difference set, calls the price classification entries of the standard attribute library, compares the price classification thresholds of each category, determines the range distribution of specification difference and environmental difference, divides the type boundary according to the range, classifies the components into the corresponding price class, sorts out the comparison results between the classes in the same region, and generates a price distribution set. The mapping set generation submodule extracts the component spatial number and construction element information based on the price distribution set. According to the standard attribute library category coding rules, it performs number matching and order sorting on entity data and price entries of the same category and located in the same area, calculates the coverage relationship of each number in the area range and establishes an association index to generate a component identification mapping set.

[0009] As a further embodiment of the present invention, the edge energy flow acquisition module includes an energy flow signal acquisition submodule, a fluctuation trajectory matching submodule, and a state chain construction submodule; The energy flow signal acquisition submodule obtains the equipment category index and spatial distribution entries in the component identification mapping set, monitors the operating power status and load response status of the pump unit, water outlet pipeline and pressure regulating equipment at the edge nodes at the corresponding locations, records the energy flow signal waveform data under different operating conditions, calculates the packet loss ratio and signal drift amplitude based on the sampling packet loss rate, performs comparison and filtering with the sampling packet loss rate threshold and drift over-limit threshold, removes invalid signal segments, and generates an effective energy flow sequence set. The fluctuation trajectory matching submodule, based on the effective power flow sequence set, calls the device code, performs corresponding matching on the timing relationship between the power flow signal waveform and the device code, arranges the fluctuation trajectory in time order, calculates the power difference and response delay between adjacent signal points, and segments and organizes them according to the continuity interval of the power difference to generate a set of power flow fluctuation trajectories. The state chain construction submodule organizes the energy flow state change records in chronological order according to the energy flow fluctuation trajectory set, compares the state intervals of each device under continuous operating conditions, extracts the operating segment number related to regional pricing and establishes a time index sequence, calculates the length and time interval of each segment sequence, and generates an energy flow operation record set.

[0010] As a further embodiment of the present invention, the cloud-edge-price-energy fusion module includes a cost range positioning submodule, a time alignment matching submodule, and a price-energy index construction submodule.

[0011] The cost interval positioning submodule acquires the time-series feature data in the energy flow operation record set, performs interval screening and minimum time interval calculation for each event time according to the time range of energy flow events in the record set, determines the cost alignment time interval corresponding to each energy flow event, limits the cost data retrieval range using regional positioning identifiers, and integrates the equipment manufacturing cost details, transportation document records and material consumption documents within the retrieval range according to time sequence to generate a cost time interval set. The time alignment and matching submodule, based on the cost time interval set, calls the energy flow status change time point in the energy flow operation record set, compares the timestamp of the cost record with the energy flow change time point, determines whether the time difference is within the interval tolerance range, performs matching and alignment on the records located in the time intersection interval, stores the aligned record set by region and indexes it in order, and generates a time overlap segment set. The price-energy index construction submodule retrieves cost items and energy flow events with time correspondence based on the set of overlapping time segments. For the equipment dimension data of the inlet area, hydropower generation area and outlet area, it extracts the overlapping items under each area and calculates their proportion range in the whole time series. It then organizes the records according to the region and equipment dimension to build a price-energy correspondence index and generates a price-energy correspondence information set.

[0012] As a further aspect of the present invention, the process of determining whether the time difference is within the interval tolerance range specifically comprises: Based on the energy flow status change time point in the energy flow operation record set and the time stamp of the cost record in the cost time interval set, the time difference between the two is calculated. The time difference is judged according to the preset interval tolerance threshold. Entries with a time difference less than or equal to the interval tolerance threshold are marked as valid matching entries. The records corresponding to all valid matching entries are aggregated in the region. The process of region aggregation is specifically as follows: Based on the regional location identifier of each valid matching entry, time segments belonging to the same region are numbered sequentially, and sorting comparison is performed according to the continuity of the time numbers. The time interval between adjacent time segments is calculated and it is determined whether they are within the continuous interval judgment threshold. Adjacent segments with time intervals less than the continuous interval judgment threshold are merged into a unified segment, and the merged record set is re-indexed according to the regional index order.

[0013] As a further aspect of the present invention, the process of dividing and storing by region specifically includes: Based on the newly established index table, the time sequence index and energy flow status identifier are extracted. The cost record entries corresponding to each time sequence index are mapped to the energy flow event numbers of the same region. The event numbers are sorted in order to form a corresponding sequence between regions and energy flow events. The sorted sequence data is stored in the cloud distributed database in chronological order to establish the corresponding regional index mapping relationship.

[0014] As a further embodiment of the present invention, the partition information management module includes a partition fluctuation extraction submodule, a deviation state identification submodule, and a task cycle arrangement submodule; The partitioned fluctuation extraction submodule obtains the regional dimension index and price-energy ratio fluctuation records in the price-energy corresponding information set, extracts the price-energy linkage entries in the upper reservoir area, underground powerhouse area and switchgear substation area, calculates the price-energy ratio fluctuation range of each partition based on the time series interval, detects the ratio change amplitude in a continuous time period, compares the difference interval between the change amplitudes to calculate the continuous response quantity, and generates a partitioned fluctuation feature set. The deviation state identification submodule, based on the partition fluctuation feature set, calls the preset management benchmark range, compares the fluctuation amplitude of each partition with the upper and lower limits of the benchmark, determines the deviation direction and the length of the deviation interval, calculates the response stability based on the ratio change sequence in the continuous interval, performs hierarchical judgment on the state of each partition and identifies the response level, and generates a partition stability level set. The task cycle scheduling submodule extracts the level sorting results of each partition based on the partition stability level set, determines the price management priority order, matches data update cycle tags for different level partitions, calculates the task arrangement order and sets the execution time based on priority and update cycle, organizes the monitoring task records within the cycle range, and generates a partition price management task set.

[0015] As a further aspect of the present invention, the system further includes: The tiered price information release module determines the task distribution rhythm according to the data update cycle label and monitoring task sequence planned in the zonal price management task center, locates edge nodes, scans the network response status and task queue length of the target edge nodes, routes instructions to edge units, executes data distribution according to the list, tracks feedback status and archives it, and generates a master table for pumped storage information price release and management. The master table for the release and management of pumped storage information prices includes task execution records, node feedback status, information price data entries, periodic scheduling results, and cloud-based archive indexes.

[0016] As a further embodiment of the present invention, the hierarchical price information publishing module includes a periodic scheduling planning submodule, a task routing execution submodule, and a feedback and archiving submodule; The periodic scheduling and planning submodule obtains the data update period label and monitoring task order of the planned partition price management task set, calls the component to identify the region and equipment topology relationship in the mapping set, judges the task distribution rhythm based on the period label and divides the partition execution window, calculates the period length and window intersection interval, locates the physical edge node corresponding to each task partition, and establishes a partition-to-node mapping table in combination with the task order to generate a periodic task mapping set. The task routing execution submodule, based on the periodic task mapping set, starts the cloud publishing control center, scans the network response status and task queue length of the target edge nodes, calculates the task priority value according to the node response time and task occupancy, performs matching judgment according to the priority value and task order, routes the update instruction to the corresponding edge computing unit, executes the information price data distribution operation in the scheduling list in sequence, and generates a node scheduling result set. The feedback and archiving submodule monitors the reception and confirmation status of each edge node for update instructions and the written feedback content based on the node scheduling result set, calculates the feedback delay and confirmation ratio, compares the records with the status registration of the corresponding tasks in the cloud database, filters nodes whose delay exceeds the feedback threshold and re-identifies the feedback entries, integrates the synchronization records of all edge nodes and archives them according to the execution sequence, and generates a master table for pumped storage information price release and management.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by extracting the characteristics of component attributes and comparing them with standard benchmarks, unified coding and classification management of data at the spatial structure level are achieved, forming a traceable component mapping relationship. This ensures the consistency of information in price calculation and structural analysis. Dynamic acquisition of energy flow status enables equipment operation behavior and energy transfer to form a continuous time-series chain. Cost data is precisely aligned with energy flow events within a time period, enhancing the synchronization and matching degree of cost records. Coupling of price and energy information at the regional dimension enables the linkage identification of cost distribution and energy consumption structure. The stability level of regional response trends is used to distinguish and improve the target accuracy of cost control. The periodic information release mechanism keeps the price and energy consumption update process coherent and adaptive, promoting price and energy coordination and dynamic balance throughout the entire pumped storage construction process. Attached Figure Description

[0018] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of the cloud-based component recognition module of the present invention. Figure 3 This is a flowchart illustrating the acquisition process of the edge energy flow acquisition module of the present invention. Figure 4 This is a flowchart illustrating the acquisition process of the cloud-edge-price-energy fusion module of the present invention. Figure 5This is a flowchart illustrating the acquisition process of the partition information management module of the present invention. Figure 6 This is a flowchart illustrating the acquisition process of the tiered price information publishing module of this invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 This invention provides a technical solution: a cloud-edge collaborative pumped storage information price release and management system, the system comprising: The cloud-based component identification module, based on the distributed data processing capabilities of cloud computing nodes, acquires volume, material density, and construction environment information of water diversion tunnels, surge tanks, and power plants in pumped storage projects. It then calls upon a pre-set component standard attribute library in the cloud and compares the volume, material density, and construction environment information collected on-site with the benchmark entries in the standard attribute library item by item. This identifies the specification and environmental differences between the on-site component attributes and the standard benchmarks. Based on the type of difference, the module categorizes the components into the corresponding price categories, extracts the spatial number, structural elements, and matching standard items for each component, and associates entity information of the same category and within the same area with the corresponding cloud-based pricing entries based on the component category code in the standard attribute library. This establishes a traceable association list and generates a component identification mapping set. The edge energy flow acquisition module, based on the equipment category index and spatial distribution entries in the component identification mapping set, continuously tracks the operating power status and load response status of pumping units, water outlet pipes, and pressure regulating equipment at the edge nodes of the corresponding physical locations. It records the energy flow signal waveforms generated by each device under different operating conditions, matches the real-time fluctuation trajectory with the equipment code, organizes the energy flow status change records in chronological order, removes invalid data segments with sampling packet loss rate exceeding the threshold or signal drift exceeding the limit, and retains the operation records related to regional pricing, forming a device status chain with time sequence characteristics and generating an energy flow operation record set. The cloud-edge-price-energy fusion module determines the cost alignment time interval corresponding to each energy flow event based on the time-series characteristic data recorded in the energy flow operation log. It uses regional positioning identifiers to limit the scope of cost data retrieval, extracts the manufacturing cost details, transportation document records, and construction material consumption records associated with the corresponding equipment in the water inlet area, hydropower generation area, and water outlet area, aligns the occurrence timestamps of each cost record with the energy flow status change time points one by one, retrieves the time overlap segments of cost segments and energy consumption fluctuation segments, establishes corresponding associations between cost items with time correspondence and energy flow events, and organizes them into a price-energy linkage index divided by region and equipment dimensions to generate a price-energy correspondence information set. The zone information management module, based on the regional dimension index and price-energy ratio fluctuation records in the price-energy corresponding information set, extracts price-energy linkage entries in the upper reservoir zone, underground plant zone, and switchgear substation zone. It analyzes the cost response trend presented by the price-energy ratio fluctuation records of each zone in a continuous time period, compares the monitored response trend characteristics with the preset management benchmark range, identifies the deviation type and fluctuation status of each zone, classifies the stability level of the response trend, determines the price management priority of each zone based on the stability level, adapts the corresponding data update cycle labels to zones with different priorities, arranges the monitoring task sequence and execution time of the zones, and generates a zone price management task set. The tiered price information release module, based on the data update cycle labels and monitoring task sequence planned in the zoned price management task set, calls components to identify the regional and equipment topology relationships in the mapping set, determines the task distribution rhythm and zoned execution window according to the cycle labels, locates the physical edge nodes corresponding to each task zone, starts the cloud release control center, scans the network response status and task queue length of the target edge nodes, routes the integrated update instructions to the matching edge computing units according to cycle priority and task sequence rules, executes the information price data distribution operation in sequence according to the sorted scheduling list, tracks the reception confirmation and writing feedback of each edge node to the update instructions in real time, synchronously transmits all feedback status back to the cloud database for archiving, and generates a master table for pumped storage information price release and management.

[0025] The component identification mapping set includes component category index, spatial location number, price classification label, structural element parameters, and standard attribute code. The energy flow operation record set includes equipment power sequence, load response characteristics, energy flow time sequence nodes, valid signal segments, and operation status identifiers. The price-energy correspondence information set includes cost matching segments, energy flow event numbers, time alignment segments, price-energy association index, and regional equipment mapping items. The zone price management task set includes zone priority level, data update label, monitoring task sequence, execution cycle plan, and scheduling time point rules. The pumped storage information price release and management master table includes task execution records, node feedback status, information price data entries, cycle scheduling results, and cloud archive index.

[0026] Please see Figure 2 The cloud-based component identification module includes a volume density comparison submodule, a component difference classification submodule, and a mapping set generation submodule. The volume density comparison submodule acquires the volume, material density, and construction environment information of the water diversion tunnel section, surge tank, and powerhouse. In the cloud computing node, it calls the volume benchmark value, material density benchmark value, and environmental benchmark value in the standard attribute library, calculates the ratio difference between the volume information and the benchmark value, calculates the difference between the material density information and the benchmark value, and performs deviation judgment on the construction environment information according to the environmental benchmark value range to generate a component attribute difference set. The system acquires volumetric, material density, and construction environment information for the water diversion tunnel, surge tank, and powerhouse. A data receiving channel is established via cloud computing nodes to retrieve measured 3D point cloud volumetric data for the water diversion tunnel section of the pumped storage project from a distributed storage unit. This data was obtained through point cloud modeling using a geological laser scanner (such as Leica RTC360). Thickness and diameter parameters acquired by the surge tank's inner wall scanning radar (GPR) are extracted to calculate the solid volume. The total volume value is retrieved from the powerhouse concrete pouring record, and the accompanying material testing report XML interface data is parsed. The measured density values ​​of C35 concrete (2450 kg / m³) and surrounding rock support steel (7850 kg / m³) were extracted. Simultaneously, environmental logs uploaded via LoRa from the SHT35 temperature and humidity sensors deployed at the construction site were retrieved, showing an average relative humidity of 85% and an average temperature of 12 degrees Celsius inside the tunnel during construction. These parameters were temporarily stored in a processing queue in cloud memory. A connection was established with the standard attribute library, and the BIM design model (IFC structured file) for the corresponding engineering section was retrieved to extract the design volume benchmark value of 120 cubic meters per linear meter. Using meters as the volume benchmark, the national standard concrete density of 2400 kg per cubic meter is extracted as the material density benchmark. The standard humidity range for the construction environment is set to 40% to 60%, and the standard temperature range to 15°C to 25°C. Numerical calculation logic is initiated, calling floating-point subtraction and division instructions. Taking the measured volume of the water diversion tunnel section as 125 cubic meters per linear meter as an example, the formula (125 minus 120) divided by 120 is executed, yielding a positive volume deviation ratio of 0.0417. For the material density, a difference calculation is performed, calculating 2450 minus 2400 to obtain the positive density difference. For a value of 50, an interval comparison logic is executed for environmental information. If the measured humidity of 85% is greater than the upper limit of the standard interval of 60%, a humidity exceeding limit identifier 1 is generated. If the measured temperature of 12 degrees Celsius is less than the lower limit of the standard interval of 15 degrees Celsius, a low temperature identifier 1 is generated. If both temperature and humidity exceed the limits, it is marked as an extreme environment group. The calculated volume deviation ratio of 0.0417, density difference of 50, and environmental exceeding limit identifier are combined into a structured feature vector. The same operation is performed on the surge tank and the plant. All calculation results are hashed according to the component ID to generate a component attribute difference set.

[0027] The component difference classification submodule, based on the component attribute difference set, calls the price classification entries of the standard attribute library, compares the price classification thresholds of each category, determines the range distribution of specification difference and environmental difference, divides the type boundary according to the range, classifies the components into the corresponding price class, sorts out the comparison results between the classes in the same region, and generates a price distribution set. Based on the component attribute difference set, the engineering cost classification rule table stored in the cloud is loaded. Preset classification threshold parameters are read, and the upper limit of the volume deviation threshold for the first price tier is set to 0.02, the volume deviation threshold range for the second price tier is 0.02 to 0.05, and the lower limit of the volume deviation threshold for the third price tier is 0.05. The allowable fluctuation range of material density is set to ±20, and the weighting coefficient for the impact of environmental differences on price is set to 1.1. The feature vectors in the difference set are traversed, and the volume deviation ratio of the water diversion tunnel section calculated in the previous step (0.0417) is extracted. This is compared with 0.02, and 0.0417 is determined to be greater than 0.02. Further comparison with 0.05 is made, and 0.0417 is determined to be less than 0.05. Based on the interval logic, it is initially classified into the second price tier. If the volume and density determination results conflict, the volume deviation weight takes precedence. The density difference of 50 is extracted, and its absolute value is determined. If the fluctuation exceeds the allowable range of 20, a material addition flag is triggered. The environmental over-limit flag is read, and the humidity over-limit flag and the temperature low-temperature flag are detected as 1. Based on the environmental impact logic, it is determined that the component is in a complex construction environment. The price correction logic is called, and the basic rate of the initial second-class price tier is multiplied by the environmental weight coefficient of 1.1, thus determining that the final price tier of the water diversion tunnel section is the second-class complex type. For the surge tank component, if its volume deviation is 0.015 and all environmental flags are 0, it is directly classified into the first-class standard price tier. For the power plant component, if the volume deviation is 0.06, it is classified into the third-class special price tier. After completing the logical judgment of all components, the physical ID of the component is bound to the determined price tier label as a key-value pair. According to the geofence definition of the water diversion area, surge tank area, and power plant area, the binding results are grouped and sorted, duplicate classification records are removed, and a price distribution set is generated.

[0028] The mapping set generation submodule extracts the component spatial number and structural element information based on the price distribution set. According to the standard attribute library category coding rules, it performs number matching and order sorting on entity data and price items of the same category and located in the same area, calculates the coverage relationship of each number in the area range and establishes an association index to generate a component identification mapping set. Based on the price distribution set, the metadata parsing program is initiated, traversing each key-value pair in the set to extract the unique spatial ID of the component. For example, the water diversion tunnel section ID "YS-Tunnel-Sec-005" is extracted. Its structural element fields are parsed using the BIM database semantic parsing interface (based on RevitAPI), identifying two key structural items: "reinforced concrete lining" and "backfill grouting". The standard attribute library category code table maintained in the cloud is queried, retrieving the cloud standard code "C-LIN-RC-35" corresponding to "reinforced concrete lining" and the standard code "G-BF-01" corresponding to "backfill grouting". String matching is performed, using the spatial ID "YS-Tunnel-Sec-005" as the primary key, and attaching the standard codes "C-LIN-RC-35" and "G-BF-01" as foreign key attributes to this primary key, forming a logical mapping between entities and pricing items. Subsequently, the regional... A topological scan within the area was performed, using the BIM model spatial index file (IFC structured index) to identify that “YS-Tunnel-Sec-005” is spatially connected to “YS-Tunnel-Sec-004” and “YS-Tunnel-Sec-006”, and all belong to the second type of complex price tier. The linear coverage length of these consecutively numbered components within the water diversion area was calculated. For example, the total coverage length of 300 meters was obtained by accumulating the lengths of the three tunnel segments. A region-based chain index structure was established, and components belonging to the same category and spatially continuous were packaged into a pricing unit. The start and end numbers of the unit were recorded in the index table and associated with the corresponding cloud pricing entry pointers to ensure that each physical component can be traced back to a unique pricing standard through the index. After completing the association construction of all regions, the index table was serialized and stored in the cloud PostGIS spatial database to generate a component identification mapping set.

[0029] Please see Figure 3 The edge energy flow acquisition module includes an energy flow signal acquisition submodule, a fluctuation trajectory matching submodule, and a state chain construction submodule; The energy flow signal acquisition submodule obtains the equipment category index and spatial distribution entries in the component identification mapping set, monitors the operating power status and load response status of the pump unit, water outlet pipeline and pressure regulating equipment at the corresponding edge nodes, records the energy flow signal waveform data under different operating conditions, calculates the packet loss ratio and signal drift amplitude based on the sampling packet loss rate, performs comparison and filtering with the sampling packet loss rate threshold and drift over-limit threshold, removes invalid signal segments, and generates a set of valid energy flow sequences. The system retrieves the device category index and spatial distribution entries from the component identification mapping set. It then parses the JSON-formatted index file via an edge computing gateway, reading the device's unique identifier "Pump-Unit-01A" and its corresponding IP address range "192.168.10.x," along with its physical location coordinates "Zone-B-Level-2." This activates the intelligent power monitoring instruments and vibration sensors deployed within this IP address range. For the pump unit "Pump-Unit-01A," it reads its active power register value in real-time via the DL / T1391 protocol Modbus / TCP interface at a sampling frequency of 100Hz, for example, reading the current active power as 300MW. For the outlet pipe "Pipe-Out-03," it reads the analog current signal output from its pressure transmitter via the PLC4-20mA analog acquisition module, for example, reading the 20mA current value and converting it to the corresponding pressure value of 5.0MPa. For the pressure regulating device "Surge-Valve-02," it reads its opening feedback signal and servo motor load rate of 75% via the VFD communication register, continuously recording... The system records analog waveform data under different operating conditions, such as startup, full-load pumping, and shutdown. Continuously acquired waveform data is temporarily stored in a circular buffer at the edge nodes. Sampling quality verification logic is executed, and the total number of data packets within a unit time window is counted. For example, if 100 data points should be collected per second, and 95 data points are actually received, the packet loss ratio is calculated as (100 - 95) divided by 100, which equals 0.05. Simultaneously, the rate of change of values ​​between adjacent sampling points is calculated. If the power was 300MW in the previous second and suddenly changed to 350MW in the current second, the drift amplitude is calculated to be 50M. W reads the preset sampling packet loss rate threshold of 0.1 (i.e., 10%) and the drift over-limit threshold of 30MW, executes the numerical comparison logic, determines that the calculated packet loss ratio of 0.05 is less than the threshold of 0.1, determines that the drift amplitude of 50MW is greater than the threshold of 30MW, identifies the 350MW data point as an abnormal change, marks the data segment corresponding to the timestamp as invalid, and only retains continuous waveform segments that have not triggered the threshold alarm, such as retaining the stable power data from T1 to T100. The waveform data of each device that has passed the screening are repackaged according to the timestamp to generate an effective energy flow sequence set.

[0030] The fluctuation trajectory matching submodule, based on the effective power flow sequence set, calls the device code to perform corresponding matching on the timing relationship between the power flow signal waveform and the device code, arranges the fluctuation trajectory in time order, calculates the power difference and response delay between adjacent signal points, and segments and organizes them according to the continuity interval of the power difference to generate a set of power flow fluctuation trajectories. Based on the effective power flow sequence set, the device encoding table cached locally at the edge nodes is loaded. The device ID field attached to the header of each data record in the effective power flow sequence set is extracted; for example, the ID "Pump-Unit-01A" is extracted. Its corresponding standard device encoding "EQ-PUMP-300MW-REV" is retrieved from the encoding table. Timing alignment is performed, and the timestamp range of the power flow sequence is read, for example, " =1000, =2000”, all voltage, current, and vibration waveform data collected within this interval by a high-speed acquisition unit (such as NI-DAQ) with synchronized GPS timing are strongly bound to the device code, forming a tagged time-series data block. All matched data blocks are sorted in ascending order of timestamps to construct a continuous time axis. The sorted sequence is then traversed to extract adjacent sampling points. and power values and Perform differential operations to calculate the power difference. Read the response delay counter value and record the time difference from when the control command is issued to when the power change is acquired. The power difference continuity judgment threshold is set to 5MW. If the power difference of 10 consecutive sampling points is less than 5MW, it is judged as a steady state interval and merged into a waveform segment. If the power difference of 3 consecutive sampling points is greater than 5MW, it is judged as a transient response interval and cut into a separate waveform segment. For example, the fluctuation interval from 280MW to 285MW is marked as the "loading stage" and the 285MW stable operation interval is marked as the "full load stage". Each segmented trajectory is assigned a unique segment ID to generate a set of energy flow fluctuation trajectories.

[0031] The state chain construction submodule organizes the energy flow state change records in chronological order based on the energy flow fluctuation trajectory set, compares the state intervals of each device under continuous operating conditions, extracts the operating segment number related to regional pricing and establishes a time index sequence, calculates the length and time interval of each segment sequence, and generates an energy flow operation record set. Based on the energy flow fluctuation trajectory set, the query interface of a time series database (such as InfluxDB) is invoked to read the metadata of each waveform segment sequentially according to the timestamp index. The status label of each waveform segment is extracted, such as "loading stage", "full load stage" or "unloading stage". The status change detection logic is executed to compare the status label of the current waveform segment with the label of the previous waveform segment. If the previous segment was "stop and wait" and the current segment has changed to "start loading", a status change event is recorded and the time point of the change is marked. Traverse the trajectory data of all devices, extract all state intervals of the pump unit "Pump-Unit-01A" within the continuous operation cycle, such as "start-up interval [T0-T10]", "stable interval [T11-T500]", and "shutdown interval [T501-T520]", read the definition of the pricing rule for the area in the component identification mapping set, identify the "peak-valley electricity price-flat segment" pricing rule corresponding to the "stable interval", and identify the "ancillary service-frequency regulation" pricing rule corresponding to the "start-up interval". If an undefined state appears in the interval, mark it as an empty state and keep the index. Filter out the operating segment numbers that have a clear correspondence with the pricing rule, remove the standby segments that have no pricing association, establish a B+ tree index structure with timestamp as the key value, insert the filtered segment numbers into the index tree, and calculate the duration of each index node. For example, calculating the duration of the stationary interval. Seconds, calculate the time interval between this interval and the previous pricing interval. The calculated segment number, start and end time, duration and interval parameters are encapsulated into structured records and archived by device dimension to generate an energy flow operation record set.

[0032] Please see Figure 4 The cloud-edge-price-energy fusion module includes a cost range positioning submodule, a time alignment matching submodule, and a price-energy index construction submodule.

[0033] The cost interval positioning submodule acquires the time-series characteristic data in the energy flow operation record set. Based on the time range of energy flow events in the record set, it performs interval screening and minimum time interval calculation for each event time to determine the cost alignment time interval corresponding to each energy flow event. It uses regional positioning identifiers to limit the cost data retrieval range and integrates the equipment manufacturing cost details, transportation document records, and material consumption documents within the retrieval range according to the time sequence to generate a cost time interval set. Obtain the time-series characteristic data from the energy flow operation record set, start the cloud data analysis engine, read each energy flow event record from the record set, and extract its start and end timestamp parameters (derived from database log trigger records), for example... Extracting a specific water pumping event start time With end time Set the minimum time interval constant to Seconds, perform interval expansion operation, and Forward correction ; Will Corrected backwards Construct the revised cost alignment time interval Extract the associated zone location identifier "Zone-ID-02" (representing the underground factory area) of the record, call the RESTful API interface of the Enterprise Resource Planning (ERP) system, input "Zone-ID-02" as the WHERE clause filter condition of the SQL query statement, limit the search scope to only the financial data tables to which this zone belongs, traverse the manufacturing cost details table under this zone, and extract the generation time of the equipment depreciation record. Iterate through the transport document record table to extract the receipt time of the large transformer arrival transport documents. Iterate through the material consumption forms to extract the lubricating oil requisition and issuance time. All retrieved fee records are sorted by timestamp. Sort the data in ascending order, generate a temporary cost sequence, execute time window filtering logic, and timestamp each cost record. Alignment range with correction Perform numerical comparisons and determine Established, depreciation expense records retained, judgment made. Established, retain transport document records, and determine If the condition is not met, remove the material consumption record, and repackage the selected and retained cost details with their corresponding time intervals and region IDs to generate a cost time interval set.

[0034] The time alignment and matching submodule, based on the cost time interval set, calls the energy flow status change time point in the energy flow operation record set, compares the timestamp of the cost record with the energy flow change time point, determines whether the time difference is within the interval tolerance range, performs matching and alignment on the records located in the time intersection interval, stores the aligned record set by region and indexes it in order, and generates a time overlap segment set; The process of determining whether the time difference is within the tolerance range is as follows: Based on the energy flow status change time point in the energy flow operation record set and the time stamp of the cost record in the cost time interval set, the time difference between the two is calculated. The time difference is judged according to the preset interval tolerance threshold. Entries with a time difference less than or equal to the interval tolerance threshold are marked as valid matching entries. The records corresponding to all valid matching entries are aggregated in the region. The process of region aggregation is as follows: Based on the regional location identifier of each valid matching entry, time segments belonging to the same region are numbered sequentially, and sorting comparison is performed according to the continuity of time numbers. The time interval between adjacent time segments is calculated and it is determined whether they are within the continuous interval judgment threshold. Adjacent segments with time intervals less than the continuous interval judgment threshold are merged into a unified segment, and the merged record set is re-indexed according to the regional index order. The process of partitioning storage by region is as follows: Based on the newly established index table, extract the time sequence index and energy flow status identifier, map the cost record entries corresponding to each time sequence index to the energy flow event number of the same region, sort according to the event number order to form a corresponding sequence between regions and energy flow events, and store the sorted sequence data in the cloud distributed database in chronological order to establish the corresponding regional index mapping relationship; Based on the cost time interval set, the energy flow state change time points in the energy flow operation record set are traversed to extract the state change time point of a certain turbine unit from "no load" to "power generation". (Time synchronization via NTP network, deviation <10ms), read the timestamp of a specific equipment maintenance fee record within the fee time interval. Calculate the absolute time difference between the two. Seconds, read the preset interval tolerance threshold. Seconds (i.e., 5 minutes) are used to perform a numerical comparison operation and determine the result. Once the time difference is confirmed to be within acceptable limits, the maintenance fee record is marked as a "valid match" and associated with a "change point". The tag "" if the timestamp of another record is The calculated difference is 800 seconds, and the judgment is... If a match is found to be invalid, it is marked as invalid. After the initial screening, the region aggregation logic is executed to extract all records marked as "valid match" with a region identifier of "Zone-ID-02" and assign temporary numbers according to their timestamps. Calculate adjacent numbered records and time interval ,For example The timestamp is 161250. The value is 161300, the calculation interval is 50 seconds, and the threshold is determined by reading the continuous interval. Seconds, judgment Then and Treating them as consecutive events, perform a merge operation to create a new unified segment. Its time range covers and The union of, if The timestamp is 161600, and Decision made after 300 seconds. The merger will then be interrupted. As a new group, based on the merged segment information, the index table is rebuilt, and All cost details within the section (such as...) Corresponding maintenance fees The corresponding material cost is mapped to the energy flow event number "Event-Gen-005"; Following the hierarchical structure of "Zone-ID-02->Event-Gen-005->Cost-List", the data is serialized into a JSON object and written to the corresponding partition of the cloud NoSQL database to generate a set of time-overlapping fragments.

[0035] The price-energy index construction submodule retrieves cost items and energy flow events with time correspondence based on the time overlap fragment set. For the equipment dimension data of the water intake area, hydropower generation area and water outlet area, it extracts the overlapping items under each area and calculates their proportion range in the whole time series. It organizes the records according to the area and equipment dimension to build a price-energy comparison index and generates a price-energy correspondence information set. Based on the set of overlapping time segments, a multidimensional data analysis process is initiated. For the three core physical areas—the intake area, the hydroelectric power generation area, and the outlet area—three independent price-energy analysis matrices are initialized. The overlapping segment records in the database are traversed, and the total cost associated with the "Event-Gen-005" event under the hydroelectric power generation area is extracted from the ERP cost accounting module. Yuan, extract the duration of the energy flow corresponding to the event. Second; and the estimated total runtime of the device throughout its entire lifecycle. Seconds, execute the percentage calculation logic; Calculate the weight of the energy flow event throughout the entire time series. This weight is used as the key factor for index sorting. For gate operation events in the inlet area, if they are associated with high hydraulic oil replacement costs, the corresponding unit time energy consumption cost is calculated. According to the combined key value of "region ID + equipment ID + energy flow event ID", the calculated cost amount, time ratio, unit energy consumption cost and other indicators are structured and assembled to establish a bidirectional query index. That is, the cost can be queried by equipment, and the energy flow event can be queried by cost. The organized index data is stored in columnar storage format to generate a price-energy corresponding information set.

[0036] Please see Figure 5 The partition information management module includes a partition fluctuation extraction submodule, a deviation state identification submodule, and a task cycle arrangement submodule; The partitioned fluctuation extraction submodule obtains the regional dimension index and price-energy ratio fluctuation records from the price-energy corresponding information set, extracts the price-energy linkage entries in the upper reservoir area, underground powerhouse area and switchgear substation area, calculates the price-energy ratio fluctuation range of each partition based on the time series interval, detects the ratio change amplitude in a continuous time period, compares the difference interval between the change amplitudes to calculate the continuous response quantity, and generates a partitioned fluctuation feature set. Obtain the regional dimension index and price-energy ratio fluctuation records from the price-energy corresponding information set. Read the metadata of the three core zones—upper reservoir area, underground powerhouse area, and switchgear substation area—through the database connection interface. Traverse the main transformer equipment in the underground powerhouse area and extract its hourly price-energy ratio data sequence (calculated from price yuan / energy kWh) for the past 24 hours. For example, extract the sequence... Set the time series analysis window to Within an hour, perform an extreme value search operation within a sliding window to calculate the maximum ratio value within each window. With minimum ratio value According to the formula Calculate the fluctuation range; if within a certain window... The fluctuation range is then 0.1. Further, a first-order difference operation is performed on the entire sequence to calculate the ratio change amplitude between adjacent time points. ; For example Extract the amplitude values ​​of two adjacent changes and Calculate the absolute value of their difference. As a second-order change; like If so, the trend of change is determined to be stable; like If so, then a mutation is determined to exist; Set a continuous response determination counter If the second-order change exceeds the preset threshold of 0.05 five times consecutively, the counter value is incremented, and this period is marked as a "critical response period," with the alarm log updated synchronously. The standard deviation of the ratio sequence during this period is calculated. ,like If the calculated maximum value of the fluctuation range, the average value of the second-order change, the continuous response count value, and other parameters are encapsulated into a feature vector, the above calculation process is repeated for the upper reservoir area and the switch substation area to generate a partitioned fluctuation feature set.

[0037] The deviation state identification submodule, based on the partition fluctuation feature set, calls the preset management benchmark range, compares the fluctuation amplitude of each partition with the upper and lower limits of the benchmark, determines the deviation direction and the length of the deviation interval, calculates the response stability based on the ratio change sequence in the continuous interval, performs hierarchical judgment on the state of each partition and identifies the response level, and generates a partition stability level set. Based on the partitioned fluctuation feature set, the management baseline parameter table in the cloud configuration management database is loaded, and the preset baseline fluctuation limit is read. Compared with the lower limit of the benchmark fluctuation It iterates through the feature vectors of each zone, extracts the maximum fluctuation range of 0.25 for the underground factory area, executes numerical comparison logic, and determines... The deviation direction is identified as "positive over-limit", and the fluctuation range value of -0.15 in the switchgear substation area is extracted for judgment. The deviation direction is identified as "negative over-limit". For the upper reservoir area fluctuation value of 0.05, it is determined to be within the baseline range. The duration of the deviation is extracted; for example, if the over-limit state in the underground powerhouse area lasted for four time windows (12 hours), the coefficient of variation calculation logic is invoked based on the ratio change sequence within the continuous interval to calculate... ,in Standard deviation The mean (calculated using SQL aggregate functions) is the value for the underground factory area. Switching substation area Set stability grading thresholds: Level 1 stability ( ), Level 2 Attention ( Level III early warning ( The calculated CV value is compared with the threshold to determine the underground power plant area as "Level 3 Warning", the switchgear substation area as "Level 2 Attention", and the upper reservoir area as "Level 1 Stable". The status level identifiers (Level-1, Level-2, Level-3) of each zone are written into the status record table to generate a zone stability level set.

[0038] The task cycle orchestration submodule extracts the level sorting results of each partition based on the partition stability level set, determines the price management priority order, matches data update cycle tags for different level partitions, calculates the task arrangement order and sets the execution time based on priority and update cycle, organizes the monitoring task records within the cycle range, and generates a partition price management task set. Based on the partition stability level set, the task scheduling algorithm engine (based on Celery distributed task queue or K8sCronJob) is started to extract the status level identifiers of all partitions, execute priority sorting logic, and set the sorting rule as: Level 3 Alert > Level 2 Attention > Level 1 Stable. A priority list is generated according to this rule: [Underground Power Plant Area, Switchgear Substation Area, Upper Reservoir Area]. For the first partition in the list, "Underground Power Plant Area" (Level 3 Alert), the high-frequency update cycle label "Cycle-High" is matched, and the data update interval is set to 15 minutes. For "Switchgear Substation Area" (Level 2 Attention), the medium-frequency update cycle label "Cycle-Medium" is matched, and the interval is set to 1 hour. For "Upper Reservoir Area" (Level 1 Stable), the low-frequency update cycle label "Cycle-Low" is matched, and the interval is set to 24 hours. The task execution time is calculated based on the current system time. Based on this, generate an execution schedule for the underground plant area for the next 24 hours: A plan is generated for the switchgear substation area: Check the resource conflicts at each task's time point. If two high-priority tasks request execution within the same millisecond, the latter task will be slightly delayed by 100 milliseconds to ensure that there is no deadlock in the scheduling queue. Bind the arranged timetable with the corresponding monitoring instruction code (such as "Scan-Vibration" or "Check-Temp") to generate a partitioned price management task set.

[0039] Please see Figure 6 The tiered pricing information publishing module includes a periodic scheduling and planning submodule, a task routing and execution submodule, and a feedback and archiving submodule. The periodic scheduling and planning submodule obtains the data update period labels and monitoring task order of the partitioned price management task set, calls the component to identify the region and equipment topology relationship in the mapping set, judges the task distribution rhythm based on the period label and divides the partition execution window, calculates the period length and window intersection interval, locates the physical edge node corresponding to each task partition, and establishes a partition-to-node mapping table in combination with the task order to generate a periodic task mapping set. The system retrieves the data update cycle labels and monitoring task order from the centralized planning of zoned price management tasks. For example, it extracts the high-frequency cycle label "Cycle-High" (15 minutes) for the underground power plant area and the medium-frequency cycle label "Cycle-Medium" (1 hour) for the switchgear substation area. It reads the equipment topology tree structure (derived from the XML topology document exported from BIM) stored in the component identification mapping centralized storage, and parses out that the underground power plant area contains sub-nodes such as "Pump-Unit-01A" and "Pump-Unit-01B", and the switchgear substation area contains sub-nodes such as "Trans-Sub-02". It then performs time-slice calculations, dividing the next 24 hours into 96 15-minute time slots. For high-frequency tasks, it evenly distributes them into each time slot; for medium-frequency tasks, it distributes them into every fourth time slot (i.e., at the top of each hour). It calculates the number of tasks overlapping in each time slot. For example, at 14:00, both high-frequency and medium-frequency update tasks exist simultaneously; the system calculates the load overlap coefficient at that time. The system determines that the current time is a "high-load window." At 14:15, there are only high-frequency tasks, so it is determined to be a "low-load window." Based on the IP mapping table in the network topology database, the system looks up the edge node IP "192.168.10.55" corresponding to "Pump-Unit-01A" and the edge node IP "192.168.20.102" corresponding to "Trans-Sub-02." The task IDs are bound to the target IP addresses one by one. For conflicting tasks within the high-load window, the system rearranges the concurrent tasks on the same physical node according to the task sequence number based on the preset serialization rules, generating an execution sequence such as "Node-55:[Task-A,Task-B]". A hash mapping table containing time slot IDs, physical node IPs, and task sequences is constructed to generate a periodic task mapping set.

[0040] The task routing and execution submodule, based on the periodic task mapping set, starts the cloud release control center, scans the network response status and task queue length of the target edge nodes, calculates the task priority value based on the node response time and task occupancy, performs matching judgment according to the priority value and task order, routes the update instructions to the corresponding edge computing units, executes the information price data distribution operation in the scheduling list in sequence, and generates a node scheduling result set. Based on the periodic task mapping set, the message queue listening service of the cloud publishing control center is started, and ICMP probe packets (Ping command) or MQTTPing requests are sent to the target edge nodes in the list. The network round-trip time (RTT) of each node is scanned and recorded. For example, the RTT of node A is measured to be 20ms and the RTT of node B is 150ms. At the same time, the real-time task queue length reported by the edge node agent is queried to obtain the current number of pending tasks of node A as 5 and node B as 50. According to the formula Calculate the priority value, where Given the queue length, calculate the weight of node A. Calculate the weight of node B. Execute the matching and judgment logic, and compare... and ,determination The update command is routed to node A for priority execution. For node B, a congestion control strategy is initiated to postpone the issuance of non-critical commands. The integrated JSON format information price update package (containing the latest rates and time period rules) is published to a specific topic on node A via the MQTT protocol. The status of node A is recorded as "Sending" and node B as "Pending" in the cloud scheduling list. Subsequent tasks in the list are processed in sequence until all commands have been routed and distributed. The final scheduling status (success, failure, suspension) of each node is written to the memory result set to generate a node scheduling result set.

[0041] The feedback and archiving submodule monitors the reception and confirmation status of each edge node for update instructions and the written feedback content based on the node scheduling result set, calculates the feedback delay and confirmation ratio, compares the records with the status registration of the corresponding tasks in the cloud database, filters nodes whose delay exceeds the feedback threshold and re-identifies the feedback entries, integrates the synchronization records of all edge nodes and archives them according to the execution time sequence, and generates a master table for pumped storage information price release and management. Based on the node scheduling result set, start the status polling daemon process, listen to the MQTT protocol acknowledgment channel (ACK, based on QoS Level 1 mechanism), capture the receive acknowledgment messages returned by each edge node in real time, and extract the timestamps from the messages. (Based on cloud host NTP time synchronization) and write status code, for example, receiving a message from node A at... The returned status code "2000K" (originating from an HTTP POST receipt) indicates the time the command was sent. Calculate feedback delay Seconds, count the number of confirmed nodes in this batch release. With the total number of nodes Calculate the confirmation ratio Set feedback delay threshold Seconds, perform numerical comparison and determination. Mark the feedback as "normal". If the feedback delay of a certain node C is 15 seconds, then determine... If a timeout occurs, it is marked as "timeout delay," and the historical status of the task is retrieved from the database. If a timeout occurs three times consecutively, an alarm is triggered and an alarm trigger table is generated. All collected feedback data, including node ID, task ID, distribution time, confirmation time, delay data, status code, and hash value of the written content, are merged and organized according to the execution sequence. The data of timeout nodes and normal nodes are stored in different log partitions. The organized full data is persistently stored in the historical archive table of the cloud relational database, generating a master table for pumped storage information price release and management.

[0042] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cloud-edge collaborative pumped storage information pricing management system, characterized in that, The system includes: The cloud-based component identification module acquires information on the volume, material, and construction environment of water diversion tunnels, surge tanks, and factory buildings. It then calls a preset library to compare feature differences and categorizes them into price categories. The module extracts component elements, associates pricing items with category codes, and generates a component identification mapping set. The edge power flow acquisition module identifies the device categories and spatial distribution in the mapping set based on the components, tracks the power and load status of edge node devices, records the power flow signal waveform and matches the trajectory, organizes the power flow status change records, removes invalid data and retains the operation records, and generates a power flow operation record set. The cloud-edge-price-energy fusion module determines the cost range of each energy flow event based on the time-series characteristic data in the energy flow operation record set, extracts the details of manufacturing, transportation and material consumption costs, aligns the cost timestamps with the energy flow change points, retrieves overlapping segments to establish price-energy associations, and generates a price-energy corresponding information set. The partition information management module extracts price-energy linkage entries based on the regional index and price-energy ratio fluctuation of the price-energy corresponding information set, analyzes the cost response trend and compares it with the management benchmark, identifies the deviation type and classifies the level, determines the management priority, and generates a partition price management task set.

2. The pumped storage information price release and management system based on cloud-edge collaboration as described in claim 1, characterized in that: The component identification mapping set includes component category index, spatial location number, price classification label, structural element parameters, and standard attribute code. The energy flow operation record set includes equipment power sequence, load response characteristics, energy flow time sequence nodes, signal valid segments, and operation status identifier. The price-energy correspondence information set includes cost matching segment, energy flow event number, time alignment segment, price-energy association index, and regional equipment mapping item. The zoned price management task set includes zoned priority level, data update label, monitoring task sequence, execution cycle plan, and scheduling timing rules.

3. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 1, characterized in that: The cloud-based component identification module includes a volume density comparison submodule, a component difference classification submodule, and a mapping set generation submodule; The volume density comparison submodule acquires the volume, material density, and construction environment information of the water diversion tunnel section, surge tank, and powerhouse. In the cloud computing node, it calls the volume benchmark value, material density benchmark value, and environmental benchmark value in the standard attribute library, calculates the ratio difference between the volume information and the benchmark value, calculates the difference between the material density information and the benchmark value, and performs deviation judgment on the construction environment information according to the environmental benchmark value range to generate a component attribute difference set. The component difference classification submodule, based on the component attribute difference set, calls the price classification entries of the standard attribute library, compares the price classification thresholds of each category, determines the range distribution of specification difference and environmental difference, divides the type boundary according to the range, classifies the components into the corresponding price class, sorts out the comparison results between the classes in the same region, and generates a price distribution set. The mapping set generation submodule extracts the component spatial number and construction element information based on the price distribution set. According to the standard attribute library category coding rules, it performs number matching and order sorting on entity data and price entries of the same category and located in the same area, calculates the coverage relationship of each number in the area range and establishes an association index to generate a component identification mapping set.

4. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 1, characterized in that: The edge energy flow acquisition module includes an energy flow signal acquisition submodule, a fluctuation trajectory matching submodule, and a state chain construction submodule; The energy flow signal acquisition submodule obtains the equipment category index and spatial distribution entries in the component identification mapping set, monitors the operating power status and load response status of the pump unit, water outlet pipeline and pressure regulating equipment at the edge nodes at the corresponding locations, records the energy flow signal waveform data under different operating conditions, calculates the packet loss ratio and signal drift amplitude based on the sampling packet loss rate, performs comparison and filtering with the sampling packet loss rate threshold and drift over-limit threshold, removes invalid signal segments, and generates an effective energy flow sequence set; The fluctuation trajectory matching submodule, based on the effective power flow sequence set, calls the device code, performs corresponding matching on the timing relationship between the power flow signal waveform and the device code, arranges the fluctuation trajectory in time order, calculates the power difference and response delay between adjacent signal points, and segments and organizes them according to the continuity interval of the power difference to generate a set of power flow fluctuation trajectories. The state chain construction submodule organizes the energy flow state change records in chronological order according to the energy flow fluctuation trajectory set, compares the state intervals of each device under continuous operating conditions, extracts the operating segment number related to regional pricing and establishes a time index sequence, calculates the length and time interval of each segment sequence, and generates an energy flow operation record set.

5. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 1, characterized in that: The cloud-edge-price-energy fusion module includes a cost range positioning submodule, a time alignment matching submodule, and a price-energy index construction submodule. The cost interval positioning submodule acquires the time-series feature data in the energy flow operation record set, performs interval screening and minimum time interval calculation for each event time according to the time range of energy flow events in the record set, determines the cost alignment time interval corresponding to each energy flow event, limits the cost data retrieval range using regional positioning identifiers, and integrates the equipment manufacturing cost details, transportation document records and material consumption documents within the retrieval range according to time sequence to generate a cost time interval set. The time alignment and matching submodule, based on the cost time interval set, calls the energy flow status change time point in the energy flow operation record set, compares the timestamp of the cost record with the energy flow change time point, determines whether the time difference is within the interval tolerance range, performs matching and alignment on the records located in the time intersection interval, stores the aligned record set by region and indexes it in order, and generates a time overlap segment set. The price-energy index construction submodule retrieves cost items and energy flow events with time correspondence based on the set of overlapping time segments. For the equipment dimension data of the inlet area, hydropower generation area and outlet area, it extracts the overlapping items under each area and calculates their proportion range in the whole time series. It then organizes the records according to the region and equipment dimension to build a price-energy correspondence index and generates a price-energy correspondence information set.

6. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 5, characterized in that: The process of determining whether the time difference is within the interval tolerance range is as follows: Based on the energy flow status change time point in the energy flow operation record set and the time stamp of the cost record in the cost time interval set, the time difference between the two is calculated. The time difference is judged according to the preset interval tolerance threshold. Entries with a time difference less than or equal to the interval tolerance threshold are marked as valid matching entries. The records corresponding to all valid matching entries are aggregated in the region. The process of region aggregation is specifically as follows: Based on the regional location identifier of each valid matching entry, time segments belonging to the same region are numbered sequentially, and sorting comparison is performed according to the continuity of the time numbers. The time interval between adjacent time segments is calculated and it is determined whether they are within the continuous interval judgment threshold. Adjacent segments with time intervals less than the continuous interval judgment threshold are merged into a unified segment, and the merged record set is re-indexed according to the regional index order.

7. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 5, characterized in that: The process of dividing storage by region is specifically as follows: Based on the newly established index table, the time sequence index and energy flow status identifier are extracted. The cost record entries corresponding to each time sequence index are mapped to the energy flow event numbers of the same region. The event numbers are sorted in order to form a corresponding sequence between regions and energy flow events. The sorted sequence data is stored in the cloud distributed database in chronological order to establish the corresponding regional index mapping relationship.

8. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 1, characterized in that: The partition information management module includes a partition fluctuation extraction submodule, a deviation state identification submodule, and a task cycle arrangement submodule. The partitioned fluctuation extraction submodule obtains the regional dimension index and price-energy ratio fluctuation records in the price-energy corresponding information set, extracts the price-energy linkage entries in the upper reservoir area, underground powerhouse area and switchgear substation area, calculates the price-energy ratio fluctuation range of each partition based on the time series interval, detects the ratio change amplitude in a continuous time period, compares the difference interval between the change amplitudes to calculate the continuous response quantity, and generates a partitioned fluctuation feature set. The deviation state identification submodule, based on the partition fluctuation feature set, calls the preset management benchmark range, compares the fluctuation amplitude of each partition with the upper and lower limits of the benchmark, determines the deviation direction and the length of the deviation interval, calculates the response stability based on the ratio change sequence in the continuous interval, performs hierarchical judgment on the state of each partition and identifies the response level, and generates a partition stability level set. The task cycle scheduling submodule extracts the level sorting results of each partition based on the partition stability level set, determines the price management priority order, matches data update cycle tags for different level partitions, calculates the task arrangement order and sets the execution time based on priority and update cycle, organizes the monitoring task records within the cycle range, and generates a partition price management task set.

9. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 1, characterized in that: The system also includes: The tiered price information release module determines the task distribution rhythm according to the data update cycle label and monitoring task sequence planned in the zonal price management task center, locates edge nodes, scans the network response status and task queue length of the target edge nodes, routes instructions to edge units, executes data distribution according to the list, tracks feedback status and archives it, and generates a master table for pumped storage information price release and management. The master table for the release and management of pumped storage information prices includes task execution records, node feedback status, information price data entries, periodic scheduling results, and cloud-based archive indexes.

10. The pumped storage information price release and management system based on cloud-edge collaboration according to claim 9, characterized in that: The hierarchical price information publishing module includes a periodic scheduling and planning submodule, a task routing and execution submodule, and a feedback and archiving submodule. The periodic scheduling and planning submodule obtains the data update period label and monitoring task order of the planned partition price management task set, calls the component to identify the region and equipment topology relationship in the mapping set, judges the task distribution rhythm based on the period label and divides the partition execution window, calculates the period length and window intersection interval, locates the physical edge node corresponding to each task partition, and establishes a partition-to-node mapping table in combination with the task order to generate a periodic task mapping set. The task routing execution submodule, based on the periodic task mapping set, starts the cloud publishing control center, scans the network response status and task queue length of the target edge nodes, calculates the task priority value according to the node response time and task occupancy, performs matching judgment according to the priority value and task order, routes the update instruction to the corresponding edge computing unit, executes the information price data distribution operation in the scheduling list in sequence, and generates a node scheduling result set. The feedback and archiving submodule monitors the reception and confirmation status of each edge node for update instructions and the written feedback content based on the node scheduling result set, calculates the feedback delay and confirmation ratio, compares the records with the status registration of the corresponding tasks in the cloud database, filters nodes whose delay exceeds the feedback threshold and re-identifies the feedback entries, integrates the synchronization records of all edge nodes and archives them according to the execution sequence, and generates a master table for pumped storage information price release and management.

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