Heat supply scheduling system and method based on one station and one day

Abstract

The invention discloses a heat supply dispatching system and method based on one station and one day, and belongs to the technical field of heat supply dispatching.Firstly, according to the heat supply area, the heat index, the heating outdoor calculation temperature and the real-time meteorological factors of a heat supply station, an optimized heat load calculation formula is adopted, and the heat supply demand per hour is accurately predicted; an intelligent platform integrated with a heat supply network management system is constructed, meteorological and operation data are automatically collected, and a daily heat supply plan is generated; the scheduling center formulates and audits a plan and then issues the plan to each heating station for execution, and the platform synchronously monitors the execution process and collects feedback information; when the air temperature suddenly changes or the load is abnormal, the platform dynamically adjusts a plan according to an early warning mechanism, hydraulic balance is achieved by adjusting flow and pressure, and operation is optimized in cooperation with a small-flow and large-temperature-difference strategy; after the heat supply cycle is finished, the platform evaluates the operation effect and provides optimization suggestions; according to the method, the heat supply precision, the energy-saving efficiency and the system response capability can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat supply scheduling, and in particular to a heat supply scheduling system and method based on one station per day. Background Art

[0002] As a crucial component of urban infrastructure, the scientific and rational nature of heating system scheduling directly impacts heating quality and energy efficiency. With the acceleration of urbanization and the intensification of energy shortages, higher requirements are being placed on the refined management of heating system scheduling.

[0003] Currently, the scheduling plans for heating systems often rely on empirical formulas and fixed patterns. For example, Professor Shi Zhaoyu of Tsinghua University proposed that only when the circulation flow of the secondary network is the design flow, the water supply temperature of the secondary network is a single-valued function of the outdoor temperature. However, in actual operation, the flow of most secondary networks in my country operates under high-flow conditions. Automatic control according to the secondary network water supply temperature will inevitably lead to overheating of the secondary network and energy waste. In addition, the existing heating system scheduling plan lacks a precise calculation and dynamic adjustment mechanism for the heat load, and is unable to adjust the heating parameters in a timely manner according to factors such as actual outdoor temperature and weather conditions, resulting in unstable heating quality and serious energy waste.

[0004] The existing technology has the following shortcomings:

[0005] The main shortcomings of the existing heating system scheduling plan include: First, the adjustment is not timely. Due to the complexity and thermal inertia of the heating system, the secondary temperature supply adjustment is difficult to respond to the changes in outdoor temperature and user demand in real time, resulting in large fluctuations in indoor temperature and affecting user comfort; second, there is a lack of precise control. The adjustment equipment and technology of some heat exchange stations are relatively backward, and precise control of secondary heating cannot be achieved. There are situations where the temperature adjustment range is too large or too small, resulting in energy waste or insufficient heating; third, hydraulic imbalance. The hydraulic balance of each branch in the heating system is difficult to ensure, resulting in different heat intake for users in the areas under the jurisdiction of different heat exchange stations. Even if secondary temperature adjustment is performed, it may be impossible for all users to reach the ideal indoor temperature due to hydraulic imbalance, affecting the uniformity and overall effect of heating. Summary of the Invention

[0006] The purpose of the present invention is to provide a heating scheduling system and method based on one station per day to solve the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a heating scheduling method based on one station per day, comprising:

[0008] The hourly heat load demand is calculated using the optimized heat load formula based on the heating area, heat index, outdoor calculated temperature and real-time meteorological factors of the heating station;

[0009] Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans;

[0010] The dispatch center formulates the plan and issues it for execution after review by the technical department;

[0011] Each heating station performs specific heating tasks, and the platform monitors and provides feedback simultaneously;

[0012] In the event of sudden temperature changes or abnormal demand, the platform will adjust the plan based on early warnings, achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining a small flow and large temperature difference strategy;

[0013] After the heating cycle ends, the platform evaluates the operating results and provides optimization suggestions.

[0014] Preferably, the heat load calculation includes:

[0015] Obtain basic data of the target heating station, including heating area, thermal index, heating outdoor design temperature and real-time outdoor average temperature;

[0016] Calculate the base heat load value based on the acquired data, set the base heat load value as the product of the heating area and the heat index, and multiply it by a correction factor based on the outdoor temperature difference. The correction factor reflects the relationship between the indoor set temperature and the real-time temperature.

[0017] The base heat load value is further corrected based on meteorological factors, including light intensity, wind speed level and snowfall conditions. A light correction factor, wind correction factor and snowfall correction factor are introduced respectively, and their effects are superimposed in a multiplicative manner to form the final hourly heat load value.

[0018] Preferably, the method for determining the correction coefficient based on the outdoor temperature difference is:

[0019] Set the standard temperature difference as the difference between the indoor comfort temperature and the heating design temperature;

[0020] The actual temperature difference between the real-time average temperature and the design temperature is converted to the standard temperature difference to obtain the proportional factor.

[0021] Preferably, the calculation of the meteorological factor correction factor includes:

[0022] Collect the current hour's light data and generate a light correction factor based on the set sunlight intensity and the standard value;

[0023] Determine the corresponding wind correction factor according to the wind speed level table;

[0024] Determine whether there is snowfall, set the snowfall correction factor to a preset value, and adjust it according to the amount of snow;

[0025] Multiply the three correction factors of sunlight, wind and snowfall to form a total correction coefficient, and finally multiply it by the base value of heat load to obtain the required heat load per hour.

[0026] Preferably, when the temperature changes suddenly or the user's heat demand changes abnormally, adjusting the heating plan includes:

[0027] The intelligent platform monitors the external temperature fluctuations and user-side heat demand fluctuations in real time. When the temperature change rate or heat load deviation exceeds the set threshold, the scheduling warning mechanism is triggered.

[0028] The platform automatically generates heating parameter adjustment suggestions based on the preset scheduling response model, including increasing or decreasing the supply water temperature, starting a backup pump group, or changing the circulating water flow rate strategy;

[0029] The dispatch center reviews the platform's suggestions and pushes the revised one-station-a-day heating plan to the relevant thermal power stations for implementation within 10 minutes, ensuring that the heating system responds quickly to external changes.

[0030] Preferably, the adjustment of hydraulic balance includes: the platform collects the actual flow and target flow data of each thermal branch in the system, and automatically identifies the unbalanced branches; by adjusting the primary valve opening of each heat exchange station and the variable frequency of the secondary circulation pump, the flow rate is fine-tuned to achieve dynamic flow balance among all branches; during the adjustment process, the pressure difference is maintained stable first, and the hydraulic disturbance is limited by setting the maximum allowable pressure fluctuation range to ensure the safety of system operation and the temperature control accuracy of the user end.

[0031] Preferably, the implementation of the small flow and large temperature difference operation strategy includes:

[0032] The platform calculates the minimum feasible circulation flow rate based on the current heat load demand and the system's heating capacity;

[0033] Adjust the frequency of the primary network main pump to control the circulation flow within the minimum range, and at the same time increase the water supply temperature setting value to the maximum allowable temperature threshold;

[0034] Synchronously adjust the outlet temperature of the secondary network heat exchange equipment and the return water control at the user end to ensure that the supply and return water temperature difference is maintained above 15 degrees Celsius, thereby effectively reducing pump consumption while ensuring heat delivery.

[0035] Preferably, the optimization operation includes:

[0036] Based on the operational feedback data of the thermal power station after adjustment, the execution effect of the dispatch results is evaluated in real time; if the system still has hydraulic imbalance or temperature fluctuation exceeds the limit after adjustment, the platform triggers secondary optimization dispatch to further fine-tune the branch flow distribution; after the operation period, the dispatch response process will be recorded in the database to achieve self-learning and continuous optimization.

[0037] The present invention also provides a heating scheduling system based on one station per day, comprising:

[0038] Heat load forecasting module: Calculates hourly heat load demand using an optimized heat load formula based on the heating area, heat index, outdoor heating temperature, and real-time meteorological factors of the heating station;

[0039] Intelligent scheduling platform construction module: Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans;

[0040] Scheduling decision module: The scheduling center formulates plans and issues them for execution after review by the technical department;

[0041] Execution control and feedback module: Each heating station performs specific heating tasks, and the platform synchronously monitors and provides feedback;

[0042] Emergency Adjustment and Hydraulic Optimization Module: In the event of sudden temperature changes or abnormal demand, the platform will adjust the early warning plan to achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining a small flow and large temperature difference strategy;

[0043] Operation evaluation and continuous optimization module: After the heating cycle ends, the platform evaluates the operation effect and provides optimization suggestions.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] 1. This invention significantly enhances the intelligence and responsiveness of the heating system by introducing a precise calculation and dynamic scheduling mechanism for heat loads based on a "one-station-a-day" schedule. By integrating real-time meteorological data, building thermal parameters, and user feedback, it achieves refined hourly heat load forecasts, addressing the lag and instability inherent in traditional scheduling based on experience and fixed patterns. This ensures precise, on-demand heating and more stable room temperatures, significantly improving user comfort and satisfaction.

[0046] 2. The integrated intelligent platform of this invention monitors, evaluates, and provides feedback throughout the entire operation process, establishing a complete closed-loop scheduling system. In the face of sudden temperature changes or abnormal loads, the system automatically issues warnings and adjusts plans in real time. Through a low-flow, high-temperature-difference strategy and hydraulic balance regulation, it optimizes heat transfer efficiency and reduces energy consumption. Post-operation performance evaluation and model self-learning mechanisms further enhance the system's adaptive capabilities, enabling continuous optimization of heating operations and energy conservation and consumption reduction, resulting in significant economic benefits and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 This is a mind map of the method of the present invention.

[0049] Figure 2 This is a mind map of the system modules of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] Example 1, please refer to Figure 1 As shown, the heat supply scheduling method based on one station per day described in this embodiment includes:

[0052] The hourly heat load demand is calculated using the optimized heat load formula based on the heating area, heat index, outdoor calculated temperature and real-time meteorological factors of the heating station;

[0053] Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans;

[0054] The dispatch center formulates the plan and issues it for execution after review by the technical department;

[0055] Each heating station performs specific heating tasks, and the platform monitors and provides feedback simultaneously;

[0056] When the temperature suddenly changes or demand is abnormal, adjust the plan based on the platform's early warning;

[0057] Achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining small flow and large temperature difference strategy;

[0058] After the heating cycle ends, the platform evaluates the operating results and provides optimization suggestions.

[0059] In the present invention's "one-station-a-day" heating scheduling method, accurate calculation of heat load is the prerequisite and key to achieving on-demand heating. To address the existing issues of inaccurate heat load calculations and the inability to dynamically respond to meteorological changes, this invention proposes a heat load optimization calculation method that comprehensively considers multiple factors, ensuring real-time and accurate scheduling plans.

[0060] The first step in this heat load calculation method is to obtain the basic data of the heating station. The basic data of the heating station mainly includes the heating area, thermal index, calculated outdoor heating temperature, and real-time average outdoor temperature. Among them, the heating area refers to the total building area that the heating station is responsible for heating, and the unit is usually square meters; the thermal index is a conventional parameter that measures the amount of heat required per unit area under standard climatic conditions, generally expressed in watts per square meter; the calculated outdoor heating temperature is the minimum outdoor temperature value referenced when designing the heating system, which is usually determined by local climatic conditions and has regional differences; and the real-time average outdoor temperature is automatically collected through the meteorological interface or temperature sensor, reflecting the actual external temperature environment during operation.

[0061] The second step is to construct a base value for heat load based on the above data. This base value is determined in the following way: first, the heating area is multiplied by the heat index to obtain the standard heat load value; then, in order to reflect the impact of real-time temperature on heating demand, a temperature difference correction factor is introduced. This correction factor uses the difference between the indoor set temperature (for example, 20 degrees Celsius) and the real-time outdoor average temperature as the numerator, and the difference between the indoor set temperature and the heating design temperature as the denominator to form a ratio. This ratio reflects the ratio of the current temperature difference to the design temperature difference, and is used to dynamically correct the intensity of the heat load. For example, when the outdoor average temperature is 0 degrees Celsius, the design temperature is minus 12 degrees Celsius, and the room temperature is set to 20 degrees Celsius, the temperature difference correction factor is (20 minus 0) divided by (20 minus minus 12), that is, 20 divided by 32, which is approximately 0.625.

[0062] The third step is to introduce meteorological correction factors to further improve the calculation accuracy of the heat load. Considering that the meteorological environment is not only reflected in temperature changes but also affected by factors such as sunlight, wind speed, and snowfall, this invention introduces three external correction factors: sunlight correction factor, wind correction factor, and snowfall correction factor.

[0063] The light correction factor reflects the impact of sunlight intensity on building heat gain. During sunny days, buildings absorb heat through daylighting, reducing reliance on heating systems. Therefore, when light intensity is above a set threshold, the light correction factor is less than 1, for example, between 0.9 and 0.95. On cloudy days or at night, the correction factor is close to or equal to 1, indicating that no adjustment to heating supply is required based on sunlight.

[0064] The wind correction factor primarily accounts for the effect of wind speed on heat loss from the building envelope. Higher wind speeds increase heat loss through convection, necessitating an increase in heating supply to maintain room temperature. The wind correction factor is typically set based on wind speed levels. For example, in calm or light wind conditions, the factor might be set to 1. However, when wind speeds reach level 5 or 6, the correction factor might be increased to 1.1 to 1.2.

[0065] The snowfall correction factor adjusts for the impact of snowfall on a building's insulation and heating demand. During snowy weather, snow covering a roof creates a certain insulating layer, which helps retain heat and, in some cases, may reduce heating demand. However, if snowfall is heavy and accompanied by extreme weather conditions such as low temperatures and high humidity, heating demand can increase significantly. Therefore, this factor can range from 0.95 to 1.3, depending on factors such as snow depth and external humidity.

[0066] Ultimately, the hourly heat load calculation is the product of the aforementioned base heat load value and three correction factors: the hourly heat load is equal to the base heat load value multiplied by the temperature difference correction factor, the sunlight correction factor, the wind correction factor, and the snowfall correction factor. This calculation method not only considers the static characteristics of the building and heating system but also dynamically introduces correction parameters that reflect real-time changes in the external environment, greatly improving the accuracy of heat load assessment.

[0067] To facilitate implementation, the present invention integrates this heat load calculation module into the intelligent heating network platform. This platform automatically acquires the required parameters and completes the calculations through real-time interaction with the Meteorological Bureau's data interface and on-site temperature and humidity sensors, anemometers, and other equipment. Heating planners no longer need to manually estimate heat loads. Instead, the system directly generates specific hourly heating targets for each heating station, providing the data foundation for subsequent "one-station-one-day" heating plans.

[0068] This heat load optimization calculation method not only improves the scientific and timely nature of heat supply scheduling, but also provides strong technical support for precise heat supply and energy conservation and consumption reduction. During the trial operation, feedback from relevant heating companies showed that this method effectively reduced fluctuations in heating energy consumption, improved user room temperature stability, and achieved a transition from "experience-based heating" to "data-driven heating."

[0069] In this invention's "One-station-per-day heating scheduling method," the core functionality is achieved through the construction of an intelligent heating network platform integrated with the heating network operation and management system to achieve automated heating plan development and scientific scheduling. This platform not only provides multiple functions, including data collection, processing, analysis, and command generation, but also seamlessly integrates with existing heating companies' scheduling processes, enabling comprehensive information-based and intelligent management.

[0070] The intelligent platform first includes a comprehensive data acquisition module. This module integrates multiple external interfaces and sensing terminals, including a meteorological data interface, a building heat data interface, and heating system operation monitoring equipment. The meteorological data interface is used to obtain real-time meteorological parameters, including average external temperature, wind speed, sunlight intensity, and snowfall forecasts. These parameters are provided by national or local meteorological service platforms and can be updated hourly or more frequently to ensure that the scheduling basis is timely and forward-looking.

[0071] The platform also monitors key operational data, such as supply and return water temperatures, circulation flow rates, and operating pressures, in real time across both the primary and secondary networks through flowmeters, thermometers, and pressure sensors deployed within the heating stations. This data is uploaded to the platform via a local area network or wireless communication, where it is centrally stored and processed in the backend, providing dynamic operational parameter support for planning.

[0072] Based on the collected meteorological and operational data, the intelligent platform further enters the plan generation module. This module, combined with the aforementioned heat load calculation method, quantifies the heating demand of each heating station in each period over the next 24 hours on an hourly basis, automatically forming a preliminary "one-station-one-day" heating plan. Specifically, based on hourly forecasts of meteorological factors such as temperature, wind speed, sunlight, and snowfall, combined with the building's thermal characteristics and the current system operating status, the module calculates the total required heating supply per hour and breaks it down into specific operating parameters, such as the water supply temperature setpoint and the circulation pump start-stop strategy.

[0073] Once the plan is generated, the intelligent platform automatically submits the preliminary plan to the dispatch center module. Dispatch center staff can view the plan details on the platform interface, including daily heat load forecast curves for each station, recommended temperature settings for each time period, and circulating pump operating condition distribution. Dispatchers review, fine-tune, or identify risk points based on experience and actual operational needs. If necessary, mandatory parameters or event response strategies (such as increasing heat intensity in cold wave emergencies) can be inserted based on manual judgment to ensure the flexibility and adaptability of the system plan.

[0074] After confirmation by the dispatcher, the plan is submitted to the production technology department for review. This technical review focuses on whether the planned parameters meet the equipment's safe operating range, whether the plan is logically consistent with the higher-level control system, and whether it meets local hydraulic balance requirements. Once approved, the plan is marked as "official" by the system and automatically converted into control instructions by the platform. These instructions are then pushed to the execution terminals at each thermal power station via the SCADA or BAS system.

[0075] During actual operation, the intelligent platform also oversees plan execution and analyzes data feedback. It collects operational data every few minutes and compares it with planned setpoints. If problems such as excessive temperature deviation, insufficient flow, or hydraulic imbalance are detected, an early warning module is automatically triggered and a recommended adjustment strategy is generated for the dispatch center's reference. Furthermore, the platform records the issuance and execution of all dispatch instructions, providing historical data for future operational evaluation and scheduling optimization.

[0076] To ensure the long-term effectiveness and continuous optimization of the plan, the platform also features a historical data analysis and self-learning module. Based on scheduling execution data from multiple heating cycles, this module employs machine learning algorithms to continuously adjust the heat load forecasting model, improving prediction accuracy. It also identifies frequent anomalies during operation and makes preemptive predictions, providing a more scientific basis for future scheduling plans.

[0077] Through this construction, the intelligent platform not only achieves a high level of integration and intelligence, but also balances the flexibility of automation and human intervention, significantly improving the accuracy, responsiveness, and execution of heating plans. Compared to traditional planning methods that rely on manual experience, the platform described in this invention enables a new model of comprehensive intelligent heating scheduling based on data, centered on prediction, and driven by scheduling, offering significant technological advancement and application value.

[0078] Practical applications have shown that after deploying this intelligent platform in multiple heating companies, heating efficiency has been significantly improved, the imbalance between heat supply and demand has been significantly alleviated, indoor temperature stability has been enhanced, user satisfaction has increased, and energy consumption and operating costs have decreased, reflecting good economic and social benefits.

[0079] To ensure the stability and responsiveness of the heating system under complex weather conditions and uncertain user demand, the heat scheduling method described in this invention incorporates a dynamic scheduling response mechanism based on an intelligent platform. This mechanism encompasses four core components: early warning identification, plan adjustment, hydraulic balance restoration, and optimization of low-flow, high-temperature-difference operation. This mechanism not only provides intelligent predictive capabilities but also enables rapid and precise system adjustments in the event of sudden weather events or large-scale user demand anomalies, thereby ensuring heating quality and energy efficiency.

[0080] The first step is a dynamic early warning identification mechanism. The intelligent platform receives external meteorological information in real time, including key indicators such as temperature, wind speed, humidity, and snowfall, and continuously monitors the heat load feedback from the user end, such as indoor temperature fluctuations and abnormal return water temperature of the heat exchange station. When the external temperature changes by more than the set threshold (for example, 3 degrees Celsius) within 1 hour, or the user's heat load exceeds the predicted value by more than 10%, the system automatically triggers the early warning mechanism. The platform's built-in early warning model identifies the heat supply and demand imbalance problems that may be caused by different types of sudden changes based on past scheduling history and operating experience, and pushes them to the dispatch center for confirmation.

[0081] The second step is to dynamically adjust the heating plan. After the early warning is confirmed, the platform calls the internal parameter optimization module to automatically generate new scheduling suggestions based on the current heat load forecast error of each heating station and the system operating status. Suggestions include: increasing or decreasing the water supply temperature set point of a certain heating station, adjusting the circulation pump frequency to change the flow rate, switching backup heat source equipment, etc. These suggestions are presented to the dispatch center staff for review through the platform interface. After review and confirmation, the platform will re-issue the revised "one station, one day" heating plan to each execution terminal within 10 minutes to ensure the timeliness and accuracy of the scheduling response.

[0082] The third step is intelligent hydraulic balance adjustment. In heating systems, each branch heat exchange station is prone to flow imbalance and pressure instability when the heat load changes, affecting heating uniformity and system efficiency. To address this issue, the present invention constructs a hydraulic balance adaptive adjustment model. The platform monitors the supply and return water pressure differential of each branch in real time, as well as the deviation between the actual flow rate and the target value. By controlling the opening of the regulating valve on the primary side of the heat exchange station and the variable frequency of the secondary network circulation pump, it achieves precise control of the flow rate of each branch.

[0083] For example, if the supply and return water temperature difference in a branch is consistently low and the room temperatures of heat users are generally substandard, the platform will identify that branch flow may be low and automatically recommend increasing the opening of the branch's regulating valve or reducing the opening of valves in other branches to redistribute hydraulic pressure. During the regulation process, the system prioritizes maintaining stable mainline pressure, with a maximum allowable pressure fluctuation range of 10 kPa. If this range is exceeded, some regulation actions will be temporarily suspended to prevent system water hammer or pressure disturbances.

[0084] The fourth step is to implement an optimized operating strategy for low flow and large temperature difference. Based on a systematic analysis of the energy efficiency of the heating system, this invention proposes the operating principle of "reducing flow and increasing temperature difference." While ensuring heating quality, reducing the amount of hot water circulating can significantly reduce pump consumption while improving the heat transfer efficiency per unit of hot water. To this end, the platform dynamically sets the minimum feasible circulation flow based on the heat load calculation results and controls the actual flow rate to approach this value by adjusting the main pump frequency.

[0085] At the same time, the system adjusts the supply water temperature setting to the maximum allowable upper limit (such as 110 degrees Celsius), and cooperates with the optimized return water temperature setting, with the goal of maintaining the supply and return water temperature difference above 15 degrees Celsius. For example, under outdoor temperature conditions of minus 5 degrees Celsius, the system sets the supply water temperature to 105 degrees Celsius, and expects the return water temperature to be maintained below 90 degrees Celsius to maximize the heat transfer efficiency per unit flow. During the execution process, the platform also continuously detects the temperature control feedback at the end of the heat user to avoid the extreme phenomenon of overheating or not heating some users due to excessive temperature differences.

[0086] Furthermore, the present invention incorporates a secondary optimization and system learning mechanism. After each operational adjustment, the platform records and archives the scheduling results and execution performance data, establishing a historical event response database. The next time a similar weather or load anomaly occurs, the platform can quickly access historical response models for more efficient prediction and decision-making. Furthermore, based on continuously updated data samples, the platform optimizes scheduling parameter setting logic through integrated machine learning models, continuously improving response accuracy and adjustment effectiveness.

[0087] In summary, the dynamic scheduling mechanism of the present invention integrates real-time monitoring, intelligent early warning, autonomous optimization, and system learning capabilities, achieving a shift from a reactive to proactive heat supply scheduling model. Practice has proven that this mechanism significantly improves system stability and energy efficiency in special scenarios such as cold waves, centralized cooling, and peak heating periods. It provides a new technological path for sustainable operation for heating companies and has broad prospects for widespread application.

[0088] In the "one-station-one-day" heating scheduling method described in this paper, post-heating cycle evaluation and optimization are crucial steps in ensuring the system's continued efficient operation. This step primarily relies on the intelligent heating network platform to comprehensively analyze operational data collected throughout the heating cycle, assess the effectiveness of the scheduling plan, identify existing problems and deficiencies, and, based on the evaluation results, provide optimization recommendations for the next cycle.

[0089] First, the platform summarizes and analyzes all key operating parameters during the heating cycle, including the total daily heat supply of each heating station, actual supply and return water temperatures, circulation flow rate trends, hydraulic balance of each branch, as well as user return water temperature, complaint records, and thermal balance index. By comparing the deviation between planned and actual values, the platform can accurately assess the accuracy and execution effectiveness of the scheduling plan. For example, if a heating station experiences consistently low supply water temperatures and an increase in user complaints, this may indicate that the regional heat load forecast is too low or the execution parameters are unreasonable.

[0090] Secondly, the platform uses data mining and statistical regression techniques to model the relationship between weather changes, heat load fluctuations, and energy consumption, identifying key factors that affect heating efficiency. Furthermore, through system modeling, it can reveal the dispatch response patterns under different weather conditions, providing a basis for the subsequent development of more detailed planning strategies.

[0091] After the assessment is complete, the platform automatically generates an operational evaluation report, including system thermal efficiency analysis, energy utilization changes, anomaly statistics, equipment operational stability scores, and heat user satisfaction analysis during the operational cycle. For typical issues identified, such as areas of severe hydraulic imbalance, time periods with significant thermal load forecast deviations, and nodes frequently triggering dispatch warnings, the platform provides targeted optimization recommendations, including parameter adjustment, heat exchange station equipment modification recommendations, and local dispatch strategy revisions.

[0092] Furthermore, the platform automatically feeds evaluation results back into the scheduling model, enabling automatic correction and updating of model parameters, forming a closed-loop mechanism of "prediction-execution-evaluation-optimization." This mechanism continuously improves the accuracy and intelligence of heat supply scheduling, enabling more efficient and energy-efficient operation in subsequent heating cycles and enhancing the sustainable operation of the heating system.

[0093] Example 2, please refer to Figure 2 As shown, the heat supply scheduling system based on one station per day described in this embodiment includes:

[0094] Heat load forecasting module: Calculates hourly heat load demand using an optimized heat load formula based on the heating area, heat index, outdoor heating temperature, and real-time meteorological factors of the heating station;

[0095] Intelligent scheduling platform construction module: Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans;

[0096] Scheduling decision module: The scheduling center formulates plans and issues them for execution after review by the technical department;

[0097] Execution control and feedback module: Each heating station performs specific heating tasks, and the platform synchronously monitors and provides feedback;

[0098] Emergency Adjustment and Hydraulic Optimization Module: In the event of sudden temperature changes or abnormal demand, the platform will adjust the early warning plan to achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining a small flow and large temperature difference strategy;

[0099] Operation evaluation and continuous optimization module: After the heating cycle ends, the platform evaluates the operation effect and provides optimization suggestions.

[0100] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A heating scheduling method based on one station per day, characterized by: include: The hourly heat load demand is calculated using the optimized heat load formula based on the heating area, heat index, outdoor calculated temperature and real-time meteorological factors of the heating station; Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans; The dispatch center formulates the plan and issues it for execution after review by the technical department; Each heating station performs specific heating tasks, and the platform monitors and provides feedback simultaneously; In the event of sudden temperature changes or abnormal demand, the platform will adjust the plan based on early warnings, achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining a small flow and large temperature difference strategy; After the heating cycle ends, the platform evaluates the operating results and provides optimization suggestions.

2. The method for heat supply scheduling based on one station per day according to claim 1, characterized in that: The heat load calculation includes: Obtain basic data of the target heating station, including heating area, thermal index, heating outdoor design temperature and real-time outdoor average temperature; Calculate the base heat load value based on the acquired data, set the base heat load value as the product of the heating area and the heat index, and multiply it by a correction factor based on the outdoor temperature difference. The correction factor reflects the relationship between the indoor set temperature and the real-time temperature. The base heat load value is further corrected based on meteorological factors, including light intensity, wind speed level and snowfall conditions. A light correction factor, wind correction factor and snowfall correction factor are introduced respectively, and their effects are superimposed in a multiplicative manner to form the final hourly heat load value.

3. The method for heat supply scheduling based on one station per day according to claim 2, characterized in that: The method for determining the correction coefficient based on the outdoor temperature difference is: Set the standard temperature difference as the difference between the indoor comfort temperature and the heating design temperature; The actual temperature difference between the real-time average temperature and the design temperature is converted to the standard temperature difference to obtain the proportional factor.

4. The method for heat supply scheduling based on one station per day according to claim 3, characterized in that: The calculation of meteorological correction factors includes: Collect the current hour's light data and generate a light correction factor based on the set sunlight intensity and the standard value; Determine the corresponding wind correction factor according to the wind speed level table; Determine whether there is snowfall, set the snowfall correction factor to a preset value, and adjust it according to the amount of snow; Multiply the three correction factors of sunlight, wind and snowfall to form a total correction coefficient, and finally multiply it by the base value of heat load to obtain the required heat load per hour.

5. The method for heat supply scheduling based on one station per day according to claim 1, characterized in that: When the temperature suddenly changes or the user's heat demand changes abnormally, the adjustment of the heating plan includes: The intelligent platform monitors the external temperature fluctuations and user-side heat demand fluctuations in real time. When the temperature change rate or heat load deviation exceeds the set threshold, the scheduling warning mechanism is triggered. The platform automatically generates heating parameter adjustment suggestions based on the preset scheduling response model, including increasing or decreasing the supply water temperature, starting a backup pump group, or changing the circulating water flow rate strategy; The dispatch center reviews the platform's suggestions and pushes the revised one-station-a-day heating plan to the relevant thermal power stations for implementation within 10 minutes, ensuring that the heating system responds quickly to external changes.

6. The method for heat supply scheduling based on one station per day according to claim 5, characterized in that: The hydraulic balance adjustment includes: the platform collects the actual flow and target flow data of each thermal branch in the system and automatically identifies unbalanced branches; by adjusting the primary valve opening and the frequency conversion frequency of the secondary circulation pump of each heat exchange station, the flow rate is fine-tuned to achieve dynamic flow balance among all branches; during the adjustment process, the pressure difference is maintained stable first, and the hydraulic disturbance is limited by setting the maximum allowable pressure fluctuation range to ensure the safety of system operation and the temperature control accuracy of the user end.

7. The method for heat supply scheduling based on one station per day according to claim 6, characterized in that: The implementation of the small flow and large temperature difference operation strategy includes: The platform calculates the minimum feasible circulation flow rate based on the current heat load demand and the system's heating capacity; Adjust the frequency of the primary network main pump to control the circulation flow within the minimum range, and at the same time increase the water supply temperature setting value to the maximum allowable temperature threshold; Synchronously adjust the outlet temperature of the secondary network heat exchange equipment and the return water control at the user end to ensure that the supply and return water temperature difference is maintained above 15 degrees Celsius, thereby effectively reducing pump consumption while ensuring heat delivery.

8. The method for heat supply scheduling based on one station per day according to claim 7, characterized in that: The optimization operation includes: Based on the operational feedback data of the thermal power station after adjustment, the execution effect of the dispatch results is evaluated in real time; if the system still has hydraulic imbalance or temperature fluctuation exceeds the limit after adjustment, the platform triggers secondary optimization dispatch to further fine-tune the branch flow distribution; after the operation period, the dispatch response process will be recorded in the database to achieve self-learning and continuous optimization.

9. A heating scheduling system based on one station per day, used to implement the heating scheduling method based on one station per day according to any one of claims 1 to 8, characterized in that: include: Heat load forecasting module: Calculates hourly heat load demand using an optimized heat load formula based on the heating area, heat index, outdoor heating temperature, and real-time meteorological factors of the heating station; Intelligent scheduling platform construction module: Build an intelligent platform integrated with the heat network management system to automatically collect meteorological and operational data and generate daily heating plans; Scheduling decision module: The scheduling center formulates plans and issues them for execution after review by the technical department; Execution control and feedback module: Each heating station performs specific heating tasks, and the platform synchronously monitors and provides feedback; Emergency Adjustment and Hydraulic Optimization Module: In the event of sudden temperature changes or abnormal demand, the platform will adjust the early warning plan to achieve hydraulic balance by adjusting flow and pressure, and optimize operation by combining a small flow and large temperature difference strategy; Operation evaluation and continuous optimization module: After the heating cycle ends, the platform evaluates the operation effect and provides optimization suggestions.

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