Multi-source energy management system

Abstract

This application discloses a multi-source energy management system. The multi-source energy management system includes: a data access terminal for receiving or collecting multi-source energy data, converting the multi-source energy data according to a preset format to obtain target energy data; a data processing terminal for calculating indicators based on the target energy data, obtaining several types of indicator data, and storing these indicator data in a database; the indicator types include at least two of the following: total energy consumption, carbon emissions, year-on-year comparison, and month-on-month comparison; and a data display terminal for receiving multi-dimensional search conditions input by the user, retrieving data from the database based on these conditions, obtaining corresponding search results, and displaying them on the data display terminal; wherein the search results include at least the corresponding indicator data. Through the above method, the problems of difficult multi-source data fusion, inaccurate traceability, and unintuitive interaction in related energy monitoring technologies can be solved.

Description

Technical Field

[0001] This application relates to the field of energy data management technology, and in particular to multi-source energy management systems. Background Technology

[0002] Driven by the "dual carbon" goals and the digital transformation of industry, smart energy monitoring has become a necessity for industrial enterprises. It not only meets policy requirements for real-time energy consumption data uploads but also helps enterprises reduce costs and increase efficiency. However, existing technologies have significant shortcomings: traditional manual meter reading results in data lag and large errors; single-energy monitoring systems create data silos, making integrated analysis impossible; while multi-energy platforms can centralize data, they lack multi-dimensional cross-referencing capabilities, leading to time-consuming queries and difficulty in tracing total energy consumption back to specific equipment, making it difficult to pinpoint energy consumption anomalies. Furthermore, visualization relies on static charts, failing to intuitively present energy flow, exhibiting poor interactivity, and the system's concurrency and stability are insufficient for large-scale applications, hindering the refined upgrading of energy management. Summary of the Invention

[0003] The multi-source energy management system provided in this application can solve the problems of difficulty in multi-source data fusion, inaccurate traceability, and unintuitive interaction in related energy monitoring technologies.

[0004] This application provides a multi-source energy management system, which includes: a data access terminal for receiving or collecting multi-source energy data, converting the multi-source energy data according to a preset format to obtain target energy data; the multi-source energy data includes at least two types of data from electrical parameters, flow rates, equipment status, historical energy consumption records, and equipment ledgers; a data processing terminal for calculating indicators based on the target energy data, obtaining several types of indicator data, and storing the several types of indicator data in a database; the indicator types include at least two of total energy consumption, carbon emissions, year-on-year comparisons, and month-on-month comparisons; and a data display terminal for receiving multi-dimensional search conditions input by the user, retrieving data from the database based on the multi-dimensional search conditions, obtaining corresponding search results, and displaying them on the data display terminal; wherein the search results include at least the corresponding indicator data.

[0005] The data access terminal is also used to collect electrical parameters and equipment status according to a first time period, and to collect flow rate values ​​according to a second time period; wherein the first time period is shorter than the second time period.

[0006] The data access terminal is also used to perform integrity verification on multi-source energy data after receiving or collecting it. For multi-source energy data that fails verification, a retransmission operation is triggered, and the integrity of the retransmitted multi-source energy data is verified again. If the verification still fails, a failure mark is made.

[0007] The data processing unit is also used to input the flow value into the Long Short-Term Memory (LSTM) network to obtain the target flow value after optimization by the LSM network output, and to calculate the total energy consumption using the energy conversion factor and the target flow value; as well as to calculate carbon emissions, year-on-year and month-on-month changes using the flow value.

[0008] The data processing terminal is also used to store electrical parameters and equipment status in a cache, and to store flow values, historical energy consumption records and equipment ledgers in a database.

[0009] The data display terminal is also used to transform multi-dimensional search conditions into structured query statements through Boolean queries, perform partitioned indexing based on the Elasticsearch inverted index library, and accelerate hierarchical retrieval using prefix indexes to obtain search results.

[0010] The data display terminal is also used to calculate the energy consumption ratio of each level by tracing back or down from the nodes in the search results after obtaining the search results through the relationship between parent and child nodes; to judge whether the indicators in the search results exceed the standard, and to mark the level of exceeding the standard and to indicate the reason for exceeding the standard by associating historical data; and / or the data display terminal is also used to respond to the user's comparison operation after obtaining the search results, and to display the comparison data between the nodes selected by the comparison operation on the data display terminal; wherein the comparison data includes at least two of the following: the absolute value difference, the relative difference, and the total energy consumption ratio between the indicator data.

[0011] The data display terminal is also used to visualize the energy consumption ratio, exceedance level, and related historical data of each level, as well as the reasons for exceedance and comparative data, according to the visualization configuration parameters. The visualization configuration parameters include gradient color threshold, line width mapping rules, and interface layout template.

[0012] The data display terminal is also used to draw a tree topology diagram using WebGL. The arrow line width represents the transmission volume, the node color corresponds to the energy consumption level, and the real-time energy consumption, carbon emissions, and year-on-year comparison are marked next to the nodes. Nodes that exceed the standard are highlighted with a preset color font. The comparison data is converted into bar charts / line charts for display. The exceedance level of nodes that exceed the standard is also displayed.

[0013] The data display terminal is also used to respond to user clicks on nodes, asynchronously load the child node data corresponding to the clicked node, and highlight and query trend data within the first preset time period; to respond to user clicks on the station thumbnail, load the 3D layout; and to respond to user selection of hovering device nodes, pop up a floating window to display the energy consumption curve, equipment parameters, and over-limit prompts within the first preset time period.

[0014] The beneficial effects of this application's embodiments are as follows: Unlike existing technologies, the multi-source energy management system provided in this application utilizes a data access terminal to receive or collect multi-source energy data, converts the multi-source energy data according to a preset format to obtain target energy data; the multi-source energy data includes at least two types of data from electrical parameters, flow rates, equipment status, historical energy consumption records, and equipment ledgers; and utilizes a data processing terminal to calculate indicators based on the target energy data, obtaining several types of indicator data, and storing these indicators in a database; the indicator types include at least two of total energy consumption, carbon emissions, year-on-year comparisons, and month-on-month comparisons; and utilizes a data display terminal to receive multi-dimensional search conditions input by the user, searches the database based on these conditions, obtains corresponding search results, and displays them on the data display terminal; wherein the search results include at least the corresponding indicator data, which can solve the problems of difficult multi-source data fusion, inaccurate traceability, and unintuitive interaction in related energy monitoring technologies. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of an embodiment of the multi-source energy management system provided in this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] Driven by the "dual carbon" goals and the digital transformation of industry, smart energy monitoring has become a necessity for industrial enterprises. It not only meets policy requirements for real-time energy consumption data uploads but also helps enterprises reduce costs and increase efficiency. However, existing technologies have significant shortcomings: traditional manual meter reading results in data lag and large errors; single-energy monitoring systems create data silos, making integrated analysis impossible; while multi-energy platforms can centralize data, they lack multi-dimensional cross-referencing capabilities, leading to time-consuming queries and difficulty in tracing total energy consumption back to specific equipment, making it difficult to pinpoint energy consumption anomalies. Furthermore, visualization relies on static charts, failing to intuitively present energy flow, exhibiting poor interactivity, and the system's concurrency and stability are insufficient for large-scale applications, hindering the refined upgrading of energy management.

[0019] Based on this, the multi-source energy management system provided in this application receives or collects multi-source energy data using a data access terminal, converts the multi-source energy data according to a preset format to obtain target energy data; the multi-source energy data includes at least two types of data from electrical parameters, flow values, equipment status, historical energy consumption records, and equipment ledgers; and uses a data processing terminal to calculate indicators based on the target energy data to obtain several types of indicator data, and stores these indicators in a database; the indicator types include at least two of total energy consumption, carbon emissions, year-on-year comparisons, and month-on-month comparisons; and uses a data display terminal to receive multi-dimensional search conditions input by the user, and searches the database according to the multi-dimensional search conditions to obtain corresponding search results, which are then displayed on the data display terminal; wherein, the search results include at least the corresponding indicator data, which can solve the problems of difficult multi-source data fusion, inaccurate traceability, and unintuitive interaction in related energy monitoring technologies. See any of the following embodiments for specific technical solutions.

[0020] See Figure 1 , Figure 1 This is a schematic diagram of an embodiment of the multi-source energy management system provided in this application. The multi-source energy management system 100 includes: a data access terminal 10, a data processing terminal 20, and a data display terminal 30.

[0021] The data access terminal 10 is used to receive or collect multi-source energy data, and convert the multi-source energy data according to a preset format to obtain target energy data; the multi-source energy data includes at least two types of data from electrical parameters, flow values, equipment status, historical energy consumption records, and equipment ledgers.

[0022] In some embodiments, electrical parameters may include voltage, current, and power. Flow values ​​may be the flow rates of water, electricity, gas, etc. For example, energy metering instruments such as smart meters, water meters, and gas flow meters can be used to collect the corresponding flow values. These data can be collected by the corresponding energy metering instruments during the use of water, electricity, and gas by the corresponding devices. Similarly, these devices will also report their device status to the data access terminal 10 through corresponding communication interfaces. For example, the device status may include running and shutting down. The device can provide feedback based on its actual status. For example, during device operation, the device can report its device status as running. During device shutdown, the device can report its device status as shut down.

[0023] In some embodiments, multi-source energy data can be provided by different data endpoints, such as OT (Operational Technology) and IT (Information Technology).

[0024] The raw data that the OT terminal can provide includes electrical parameters (voltage, current, power), flow values, and equipment status (running / shutdown) output by energy metering instruments (smart meters, water meters, gas flow meters, etc.) (in hexadecimal / digital signals in Modbus TCP / OPCUA protocol format).

[0025] The raw data that the IT side can provide includes historical energy consumption records and equipment ledgers in the enterprise energy management system database. The corresponding data format is JSON / MySQL tables.

[0026] For the preset format, you can set it to JSON format. The corresponding fields in this JSON format can include device ID, timestamp, energy type, original value, and unit.

[0027] Based on this, a multi-protocol unified parsing engine can be used to convert Modbus TCP / OPC UA protocol data from the OT side and HTTP / HTTPS interface data from the IT side into a unified JSON format. In other words, multi-source energy data can be converted according to a preset format to obtain the target energy data.

[0028] In some embodiments, the data access terminal 10 is further configured to collect electrical parameters and device status according to a first time period, and to collect flow rate values ​​according to a second time period; wherein the first time period is shorter than the second time period.

[0029] In some embodiments, multi-source energy data contains data of different frequencies. For example, high-frequency data such as electrical parameters and equipment status, and low-frequency data such as water and gas flow rates. Due to the different frequencies, differentiated data collection is required. For instance, high-frequency data (electrical parameters, equipment status) can be collected by polling at 1s / 0.5s intervals, while low-frequency data (water, gas) can be collected using a strategy of "actively uploading when the change exceeds 0.5% + 5s minimum guarantee collection". That is, when the change in low-frequency data exceeds a preset percentage, data is actively uploaded to the data access terminal 10. Flow rates can also be collected synchronously according to a second time period.

[0030] In some embodiments, the data access terminal 10 is further configured to perform integrity verification on the multi-source energy data after receiving or collecting it, trigger a retransmission operation for the multi-source energy data that fails the verification, and perform integrity verification on the retransmitted multi-source energy data again. If the verification still fails, a failure mark is made.

[0031] For integrity verification, the CRC32 algorithm can be used to verify each frame of data. If it fails, three retransmissions are triggered. If it still fails, it is marked as "invalid data" (failure marking).

[0032] In some embodiments, the number of retransmissions can be set according to actual needs. For example, triggering 2 retransmissions, triggering 1 retransmission, triggering 5 retransmissions, etc.

[0033] Based on this, after following the above method, the output is the raw data (target energy data in JSON format) that has been standardized and verified, including valid data (verification status = valid) and invalid data logs (retained for 24 hours).

[0034] By adopting the above methods, the "data silo" problem caused by incompatible data formats between OT and IT ends is solved, ensuring that multi-source energy data can be processed uniformly. At the same time, differentiated collection and verification ensure the real-time performance (no delay for high-frequency data) and integrity (efficiency ≥99.9%) of the data, providing reliable input for subsequent processing.

[0035] In some embodiments, the data processing terminal 20 is used to perform indicator calculations based on target energy data to obtain several types of indicator data, and store the several types of indicator data in a database; the indicator types include at least two of the following: total energy consumption, carbon emissions, year-on-year comparison, and month-on-month comparison.

[0036] In some embodiments, the data processing terminal 20 is also used to input the flow value into the long short-term memory network to obtain the target flow value after the long short-term memory network output optimization, and to calculate the total energy consumption using the energy conversion factor and the target flow value; and to calculate carbon emissions, year-on-year and month-on-month using the flow value.

[0037] In some embodiments, the data processing terminal 20 is also used to store electrical parameters and device status in a cache, and to store flow values, historical energy consumption records and device ledgers in a database.

[0038] In some embodiments, after receiving the target energy data, the data processing terminal 20 uses auxiliary parameters, such as energy conversion factor, carbon emission factor, and equipment rated threshold, to calculate the corresponding indicators.

[0039] After receiving the target energy data, the data processing terminal 20 will perform preprocessing operations, such as removing null values ​​and duplicate values ​​(removing duplicates by device ID + timestamp) and unifying the units of measurement (e.g., unifying the unit of electricity to kWh).

[0040] And anomaly marking for target energy data. For example, using the 3σ criterion (exceeding the mean ± 3 standard deviations) to identify fluctuation anomalies, and combining this with equipment rated thresholds (such as the upper limit of motor power) to identify over-range anomalies, and marking the anomaly type.

[0041] After the above processing, the indicators are calculated.

[0042] For example, total energy consumption can be predicted using an LSTM (Long Short-Term Memory) neural network, and then weighted and summed using energy conversion factors. For instance, 1 kWh of electricity equals 0.1229 kg of standard coal. A Long Short-Term Memory network includes an input layer, embedding layer, LSTM hidden layers, fully connected layers, and training parameters.

[0043] In some embodiments, target energy data (flow values) are input into a Long Short-Term Memory (LSTM) network, which outputs optimized target flow values, such as water, electricity, and gas. These values ​​are then weighted and summed using the energy conversion factors corresponding to water, electricity, and gas respectively, to obtain the total energy consumption.

[0044] Please refer to the following formula for details: .in, This represents the total energy consumption at time t. This represents the optimized i-type flow value output by the Long Short-Term Memory (LSTM) network. This represents the energy conversion factor corresponding to type i.

[0045] like Valid data: LSTM only smooths out fluctuations; This represents the target energy data of type i at time t.

[0046] like For minor omissions (such as retransmission failures marked as "to be completed"): . This represents the predicted value output by the LSTM. That is, in When there are only a few missing values, the predicted values ​​output by the LSTM are used for filling.

[0047] Carbon emissions can be calculated as "activity data × (70% regional factor + 30% enterprise measured factor)". For example, carbon emissions from electricity = 0.6101 tCO2 / MWh × electricity consumption.

[0048] Derivative metrics can include year-on-year and month-on-month comparisons. For example, year-on-year (compared to the same period last year) and month-on-month (compared to the previous month) can be calculated automatically.

[0049] The data generated by the above process can be stored separately. For example, data from the past hour can be stored in Redis (electricity parameters expire in 4 seconds, status expires in 2 seconds), and historical data can be stored in MySQL (separated by energy type and month, retained for 3 years). The data can be synchronized in real time through a publish-subscribe model.

[0050] Using the above method, structured indicator data (including node ID, timestamp, energy consumption value, carbon emissions, year-on-year / month-on-month comparison, and anomaly markers) can be output and stored in Redis cache and MySQL database.

[0051] By using the above methods, data from both the OT and IT ends are transformed into "energy indicators" that can be directly used for analysis. Invalid data is eliminated through anomaly labeling, calculation accuracy is ensured through LSTM models and standard coefficients, and real-time performance and historical data query efficiency are balanced through hierarchical storage, providing "ready-to-use" data for retrieval and analysis.

[0052] In some embodiments, the data display terminal 30 is used to receive multi-dimensional search conditions input by the user, and to search the database according to the multi-dimensional search conditions to obtain the corresponding search results, which are then displayed on the data display terminal 30; wherein, the search results include at least the corresponding indicator data.

[0053] In some embodiments, the data display terminal 30 is also used to convert multi-dimensional search conditions into structured query statements through Boolean queries, perform partitioned indexing based on the Elasticsearch inverted index library, and accelerate hierarchical retrieval using prefix indexes to obtain search results.

[0054] In some embodiments, users input search criteria through the interface. These criteria can include energy type, time range, statistical dimensions, indicator type, node level, etc. For example, users can select "electric energy + recent 7 days + region A" from a dropdown menu on the data display terminal 30's interface. The data display terminal 30 converts these search criteria into structured query statements. Then, based on the Elasticsearch inverted index, it locates the partition index by "energy type + time" and combines multiple conditions through Boolean queries (e.g., "energy = electricity AND level = 3").

[0055] To improve query efficiency, prefix indexes can be used to accelerate hierarchical retrieval. For example, "Region A - Department B" can be quickly matched, and duplicate query results within 1 minute can be cached to ensure a time lag of ≤1 second.

[0056] Then, after retrieving the results, the results are integrated. For example, sorted by node ID and timestamp, a JSON result set containing pagination information is generated.

[0057] The data display terminal 30 then outputs search results that match the user's multi-dimensional search criteria. These results may include indicators such as node ID, timestamp, energy consumption / carbon emissions / year-on-year comparison, etc. The data display terminal 30 supports exporting these search results to Excel.

[0058] By employing the above methods, the problem of low efficiency in traditional single-dimensional retrieval is solved. Through multi-condition combination and index optimization, users can quickly locate target data with "specific time + region + energy + level", shortening data acquisition time (efficiency improved by 5 times) and providing accurate input for subsequent analysis.

[0059] In some embodiments, the data display terminal 30 is further configured to, after obtaining the search results, trace upwards or downwards from the nodes in the search results through the association of parent and child nodes to calculate the energy consumption ratio of each level; and to judge whether the indicators in the search results exceed the standard, and mark the level of exceeding the standard and associate historical data to prompt the reason for exceeding the standard; and / or the data display terminal 30 is further configured to, after obtaining the search results, respond to the user's comparison operation, and display the comparison data between the nodes selected by the comparison operation on the data display terminal 30; wherein, the comparison data includes at least two of the absolute value difference, relative difference and total energy consumption ratio between indicator data.

[0060] In some embodiments, the multi-source energy management system 100 provided in this application also provides a multi-level association analysis function. For example, data association is performed using the above-mentioned search results and corresponding auxiliary data. For example, the auxiliary data can be a node hierarchy relationship table, such as the parent-child ID mapping of "Region A → Department B"; the auxiliary data can be an energy consumption standard threshold table, such as "Department B's monthly energy consumption ≤ 50000kWh".

[0061] Specifically, hierarchical traceability can be implemented. For example, by associating parent and child NodeIDs, nodes in the search results can be traced upwards to total energy consumption (Level 1) or downwards to equipment (Level 5) to calculate the energy consumption percentage of each level (e.g., equipment energy consumption accounts for 15% of the department's energy consumption). In some embodiments, the hierarchical relationship can be as follows: Enterprise - Region / Factory - Department / Workshop - Production Line / Station - Equipment, etc. The 5 levels are arranged from largest to smallest.

[0062] It also enables multi-node comparison functionality. For example, users can select 2-5 peer nodes (such as 2 departments) to extract indicator values ​​and calculate absolute differences, relative differences (growth rates), and total energy consumption percentages. For instance, after displaying search results, if a user wants to view energy consumption comparisons between certain departments, they can first select "Group - South China Production Area - Department - Production Line - Equipment," which will automatically pop up the interface. Then, the "Departments" will include Department A, Department B, Department C, etc., and users can select Department A and Department C for comparison.

[0063] It also enables the function of judging whether an item exceeds the standard. For example, it compares the node indicators with the standard threshold, marks the level of exceeding the standard (general / serious), and suggests the cause by associating historical data. For example, "Equipment M101 energy consumption suddenly increased". In some embodiments, the display screen corresponding to the data display terminal 30 is used to display the energy management of "enterprise-region / factory-department / workshop-production line / station-equipment". It can show the specific situation of energy consumption. Therefore, the indicators that these five levels of nodes will definitely have are: actual energy consumption value (water, electricity), carbon emissions, year-on-year growth rate (comparisons involve daily, weekly, monthly, and yearly), month-on-month growth rate, continuous exceeding duration, etc. (the specifics will vary and be customized according to customer requirements, but the overall indicators are the same).

[0064] Secondly, different nodes will focus on different indicators; The "enterprise" node mainly focuses on indicators such as energy consumption per unit of output (total energy consumption / industrial output) and carbon intensity (carbon emissions / output).

[0065] The "regional" node mainly focuses on indicators such as the regional energy consumption ratio (energy consumption in the region / total energy consumption of enterprises) and energy consumption per unit area.

[0066] The "department" node mainly focuses on indicators such as per capita energy consumption and energy consumption balance of production lines (maximum line energy consumption / minimum line energy consumption).

[0067] The "production line" node mainly focuses on indicators such as energy consumption per unit of product (total energy consumption / output) and average equipment operating load rate.

[0068] The "equipment" node mainly focuses on indicators such as real-time load rate (actual power / rated power), number of start-stop cycles, and frequency of abnormal fluctuations.

[0069] Then, the data display terminal 30 outputs analysis results (JSON format) including hierarchical traceability links (such as "total energy consumption → area A → department B → equipment M101"), multi-node comparison data, and over-limit warning information.

[0070] The above methods solve the problem that traditional monitoring cannot trace the source of energy consumption. The five-level node system enables full-link positioning "from total energy consumption to equipment". By comparing and judging the exceedance, the cause of the anomaly is identified, providing in-depth information on "where the problem is and why it happened" for decision-making.

[0071] In some embodiments, the data display terminal 30 is also used to visualize the energy consumption ratio of each level, the level of exceeding the standard, and the reasons for exceeding the standard and the comparison data of the associated historical data according to the visualization configuration parameters; wherein, the visualization configuration parameters include gradient color threshold, line width mapping rules, and interface layout template.

[0072] In some embodiments, the data display terminal 30 is also used to draw a tree topology diagram using WebGL; wherein the arrow line width represents the transmission volume, the node color corresponds to the energy consumption level, and real-time energy consumption, carbon emissions, and year-on-year comparison are marked next to the nodes, nodes exceeding the standard are highlighted with a preset color font, and the comparison data is converted into a bar chart / line chart for display; and the exceeding level of the exceeding nodes is displayed.

[0073] In some embodiments, after outputting the analysis results (tracing links, comparing data, and early warning information), visualization can be presented by combining visualization configuration parameters (gradient color threshold, line width mapping rules, and interface layout templates). Visualization can include the following operations: flow chart rendering, indicator overlay, auxiliary display, and adaptation optimization.

[0074] The flow graph rendering mainly uses WebGL to draw a tree topology graph. The arrow line width represents the transmission volume (line width = energy consumption value / 1000), and the node color (blue → yellow → red) corresponds to the energy consumption level (≤30% / 30%-70% / ≥70% of the rated value).

[0075] The indicator overlay mainly involves labeling real-time energy consumption, carbon emissions, year-on-year comparisons, and other indicators next to the nodes, with nodes exceeding the standards highlighted in red.

[0076] The auxiliary display mainly involves converting the comparative data into bar charts / line charts and embedding them at the end of the interface; warning information is displayed according to level (critical warning pop-up window, general warning label).

[0077] The adaptation optimization mainly involves automatically adjusting the size of elements based on the screen size (55-120 inches) to ensure a resolution of ≥3840×2160 and a page switching latency of ≤0.5s.

[0078] The data display terminal 30 mentioned above can be a central control screen, allowing the visualized content to be displayed intuitively on the central control screen interface. The central control screen interface can display a central flow diagram, a top search area, and a terminal auxiliary area, and supports multi-size adaptation.

[0079] By employing the above methods, the problem of traditional charts being "abstract and difficult to understand" is solved. Dynamic flow diagrams and gradient colors visually present energy transmission paths and load status, overlaid with indicators and early warning information, allowing managers to "understand the entire energy picture at a glance" and shortening decision-making time. For example, decision-making time can be reduced by 60%.

[0080] In some embodiments, the data display terminal 30 is also used to respond to a user clicking a node, asynchronously load the child node data corresponding to the clicked node, and highlight and query the trend data within a first preset time period; respond to a user clicking a station thumbnail, load the three-dimensional layout; and respond to a user selecting a hover device node, pop up a floating window to display the energy consumption curve, device parameters, and over-limit prompts within the first preset time period.

[0081] In some embodiments, the data display terminal 30 also provides interactive feedback and closed-loop functions. For example, users can operate on the visual interface, such as clicking nodes, hovering over devices, or clicking on station thumbnails. During this process, it is necessary to preload node details database (device model, location), station 3D model data, etc.

[0082] When a user clicks on a parent node, the child node data is loaded asynchronously (without refreshing the page). When a node is selected, it is highlighted and its trend data for the past 24 hours is displayed.

[0083] When a user clicks on the station's thumbnail, the 3D layout is loaded via NodeID association.

[0084] When the user selects and hovers over a device node, a pop-up window displays a 10-minute energy consumption curve, device parameters, and warnings of exceeding limits.

[0085] Furthermore, the system in this application has stability assurance functions, and can support more than 1,000 concurrent users through server clusters and load balancing. Each module is deployed independently and automatically restarts in case of an anomaly.

[0086] Based on this, after the user performs an operation, the data display terminal 30 can interactively respond with the results (sub-node data, trend charts, floating window information, 3D models) and update the visualization interface display.

[0087] By adopting the above methods, the problem of "one-way display and lack of interaction" in traditional systems is solved. Through flexible interaction, users can view details in depth (such as the real-time status of the device), forming a closed loop of "viewing-analysis-operation-feedback", while ensuring system stability (availability ≥99.9%) in high-concurrency scenarios.

[0088] In summary, the multi-source energy management system 100 of this application can utilize the data access terminal 10 to process the initial input of scattered raw data from the device end (OT) and system end (IT) through multi-protocol parsing, differentiated acquisition, and integrity verification. This process outputs standardized data with a unified format and passing verification. This standardized data, along with auxiliary parameters such as energy conversion factors and carbon emission factors, is provided to the data processing terminal 20. At the data processing terminal 20, after data cleaning, anomaly marking, core indicator calculation, and hierarchical storage, structured energy consumption, carbon emission, and other indicator data are output and stored in the cache and database. At the data display terminal 30, this stored structured indicator data, along with user-defined search conditions, constitutes new input. After condition parsing, index matching, and efficiency optimization, it outputs accurate search results that meet user needs. The results are as follows: The search results, combined with auxiliary data such as node hierarchy and energy consumption standard thresholds, are processed through hierarchical tracing, multi-node comparison, and exceedance judgment to output in-depth analysis results containing source tracing links, comparative data, and early warning information. The analysis results, along with visualized configuration parameters, are processed through flow diagram rendering, indicator overlay, and adaptation optimization to output an intuitive and easy-to-understand central control screen interface. User operation signals on the screen, along with equipment details, 3D models, and other data, are processed through node interaction response, jump feedback, and stability assurance to output updated interface content (such as sub-node data, trend charts, and 3D models). The new data generated by these interactions (such as abnormal equipment information that users are interested in) can be fed back to the data processing or analysis stage, forming a complete closed loop of data access → processing → application → interactive feedback → data reprocessing.

[0089] The multi-source energy management system 100 of this application has the following beneficial effects: 1. Efficiency Improvement: Multi-dimensional retrieval improves data acquisition efficiency by 5 times (latency ≤ 1 second), and automated processing saves 30% of manpower.

[0090] 2. Enhanced real-time performance: Electrical parameters refresh ≤ 4 seconds, and abnormal response speed is improved by more than 5 times.

[0091] 3. Accurate traceability: The five-level node system enables full-chain traceability from total energy consumption to equipment, with an anomaly location accuracy rate of ≥98%.

[0092] 4. Visual and intuitive: Gradient color flow chart + multiple indicators overlay, reducing decision-making time by 60%.

[0093] 5. High stability: Supports simultaneous browsing by 1000+ users, with system availability ≥99.9%.

[0094] In summary, the multi-source energy management system 100 of this application achieves real-time, accurate, and intuitive monitoring of energy flow by standardizing and integrating multi-source energy data, performing efficient multi-dimensional retrieval (time lag ≤ 1 second), tracing the entire chain of five-level nodes (anomaly location accuracy ≥ 98%), providing gradient color visualization (reducing decision-making time by 60%), and employing a high-concurrency and stable architecture (supporting access for 1000+ users, availability ≥ 99.9%). This improves the efficiency and reliability of smart energy monitoring across the entire chain from data processing to decision-making.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0096] If the integrated units in the other embodiments described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processing circuit component (processor) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A multi-source energy management system, characterized in that, The multi-source energy management system includes: The data access terminal is used to receive or collect multi-source energy data, and convert the multi-source energy data according to a preset format to obtain target energy data; the multi-source energy data includes at least two types of data from electrical parameters, flow rates, equipment status, historical energy consumption records, and equipment ledgers; The data processing terminal is used to calculate indicators based on the target energy data, obtain several types of indicator data, and store the several types of indicator data in the database; the indicator types include at least two of the following: total energy consumption, carbon emissions, year-on-year comparison, and month-on-month comparison. The data display terminal is used to receive multi-dimensional search conditions input by the user, and to search the database according to the multi-dimensional search conditions to obtain the corresponding search results, which are then displayed on the data display terminal; wherein, the search results include at least the corresponding indicator data.

2. The multi-source energy management system according to claim 1, characterized in that, The data access terminal is also used to collect the electrical parameters and the device status according to a first time period, and to collect the flow rate value according to a second time period; wherein the first time period is shorter than the second time period.

3. The multi-source energy management system according to claim 1 or 2, characterized in that, The data access terminal is also used to perform integrity verification on the multi-source energy data after receiving or collecting it, trigger a retransmission operation for the multi-source energy data that fails the verification, and perform integrity verification on the retransmitted multi-source energy data again. If the verification still fails, a failure mark is made.

4. The multi-source energy management system according to claim 1, characterized in that, The data processing terminal is also used to input the flow value into the long short-term memory network to obtain the optimized target flow value output by the long short-term memory network, and to calculate the total energy consumption using the energy conversion factor and the target flow value; And the carbon emissions, year-on-year and month-on-month changes are calculated using the flow rate values.

5. The multi-source energy management system according to claim 1, characterized in that, The data processing terminal is also used to store the electrical parameters and the device status in a cache, and to store the flow rate value, the historical energy consumption record and the device ledger in the database.

6. The multi-source energy management system according to claim 1, characterized in that, The data display terminal is also used to convert the multi-dimensional search conditions into structured query statements through Boolean queries, perform partitioned indexing based on the Elasticsearch inverted index library, and accelerate hierarchical retrieval using prefix indexes to obtain the search results.

7. The multi-source energy management system according to claim 1, characterized in that, The data display terminal is also used to, after obtaining the search results, trace back upstream or downstream from the nodes in the search results through the association of parent and child nodes to calculate the energy consumption ratio of each level; and to judge whether the indicators in the search results exceed the standard, mark the level of exceeding the standard and associate historical data to prompt the reason for exceeding the standard. And / or the data display terminal is also used to, after obtaining the search results, in response to the user's comparison operation, display comparison data between the nodes selected in the comparison operation on the data display terminal; wherein, the comparison data includes at least two of the absolute value difference, relative difference, and total energy consumption ratio between indicator data.

8. The multi-source energy management system according to claim 7, characterized in that, The data display terminal is also used to visualize the energy consumption ratio of each level, the level of exceeding the standard, the reasons for exceeding the standard and the comparison data according to the visualization configuration parameters; wherein, the visualization configuration parameters include gradient color threshold, line width mapping rules and interface layout template.

9. The multi-source energy management system according to claim 8, characterized in that, The data display terminal is also used to draw a tree topology diagram using WebGL; wherein the arrow line width represents the transmission volume, the node color corresponds to the energy consumption level, and real-time energy consumption, carbon emissions, and year-on-year comparison are marked next to the nodes, nodes exceeding the standard are highlighted with a preset color font, and the comparison data is converted into a bar chart / line chart for display; and the exceeding level of the exceeding nodes is displayed.

10. The multi-source energy management system according to claim 8 or 9, characterized in that, The data display terminal is also used to respond to user clicks on nodes, asynchronously load the child node data corresponding to the clicked node, and highlight and query trend data within a first preset time period; In response to a user clicking on the site thumbnail, load the 3D layout; In response to the user selecting to hover over a device node, a pop-up window displays the energy consumption curve, device parameters, and over-limit warnings for the first preset time period.

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