Multi-heat-source heating load optimal distribution method, system and storage medium

Abstract

This invention provides a method, system, and storage medium for optimizing the allocation of heating loads from multiple heat sources, belonging to the field of load allocation technology. Specifically, it includes: determining the overlap between the load scheduling periods of a heating area and the load scheduling periods of different matching heating areas; determining overlapping scheduling periods based on the overlap; identifying the heating areas within the heating area based on the distribution data of the overlapping scheduling periods in the heating area's load scheduling periods and the load scheduling regions within the overlapping scheduling periods; and determining the identified heating areas based on the overlap between the identified heating areas and the matching heating areas of different heat sources, thereby reducing the difficulty of load regulation processing.

Description

Technical Field

[0001] This invention belongs to the field of load distribution technology, and particularly relates to a method, system and storage medium for optimizing the distribution of heating loads from multiple heat sources. Background Technology

[0002] Current load allocation methods for multi-heat source heating systems mainly rely on mathematical programming or heuristic rules based on thermodynamic models, which have significant limitations when dealing with complex dynamic scenarios: these methods are mostly static or quasi-static optimizations, which cannot adapt in real time to uncertainties such as sudden changes in meteorological conditions, random fluctuations in user heating behavior, time-of-day changes in energy market prices, and sudden equipment failures, resulting in optimization results that often deviate from the optimal operating conditions in actual operation.

[0003] Meanwhile, as the scale of heating systems expands and the proportion of renewable energy increases, the computational complexity of traditional methods increases dramatically, making it difficult to meet the real-time requirements of online scheduling. This results in a situation where the economic efficiency, reliability, and environmental friendliness of heating system operation are difficult to balance.

[0004] Therefore, there is an urgent need for a method, system, and storage medium for optimizing the distribution of heating loads from multiple heat sources. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for optimizing the allocation of heating load from multiple heat sources, which includes: S1 acquires the heating data of the heat source, uses the heating data to determine the heat loss data of the heating network between the heat source and different heating areas, and combines the load adjustment data of the heating areas to determine the matching heating areas of the heat source. S2 determines the matching heating area data and, in conjunction with the distribution data of the load scheduling period of the matching heating area, determines that the load adjustment strategy of the heating area needs to be optimized, and then proceeds to the next step. S3 determines the overlap between the load scheduling period of the heating area and the load scheduling period of different matching heating areas, determines the overlapping scheduling period based on the overlap, and identifies the heating area in the heating area based on the distribution data of the overlapping scheduling period in the load scheduling period of the heating area and the load scheduling area in the overlapping scheduling period. S4 determines the load scheduling processing strategy for the identified heating area under different heat sources based on the overlap of the load scheduling periods between the identified heating area and the matching heating areas of different heat sources.

[0006] The beneficial effects of this invention are as follows: In multi-heat-source, multi-region heating systems, the most suitable heating region is intelligently and dynamically matched to each heat source. The core logic is to achieve a dual match between "transmission efficiency" and "dispatch demand." That is, priority is given to heating regions that 1) have low heat transmission losses (low network heat loss rate) and 2) have stable or considerable load regulation needs (load dispatch periods exist), forming a matching relationship with them. This maximizes energy utilization efficiency, fully utilizes the regulation capabilities of heat sources, supports load balancing of the power grid or system, and also lays the foundation for further reducing the difficulty of load dispatch processing.

[0007] Based on the established efficient "heat source-matching area", secondary diagnosis and potential assessment are carried out on the unmatched heating areas in the system. The aim is to identify those areas that were missed due to the initial screening, but whose actual scheduling behavior shows that they have high collaborative value or unique functions. This provides precise targets for subsequent system optimization and ultimately reduces the complexity of multi-heat source collaborative scheduling. Specifically, by analyzing the overlap of scheduling time between unmatched areas and efficient matching areas, optimization resources are precisely directed to the most valuable unmatched areas.

[0008] Furthermore, the heating data of the heat source includes the heating time periods between the heat source and different heating areas.

[0009] Furthermore, the heat loss data of the heating pipeline network between the heat source and different heating areas is determined based on the heat loss rate of the heating pipeline network between the heat source and different heating areas.

[0010] Furthermore, the load regulation data for the heating area is determined based on the load scheduling period of the heating area and the distribution data of the load scheduling period on different dates.

[0011] Furthermore, the method for determining the matching heating area of ​​the heat source is as follows: Using the heat loss data of the heating network between the heat source and the heating area, the heat loss rate of the heating network between the heat source and the heating area is determined, and the heating areas with a heat loss rate less than a preset heat loss rate threshold are identified as potential matching areas. Based on the load regulation data of the potential matching pipeline network, determine the load scheduling period of the potential matching area; The matching heating area of ​​the heat source is determined based on the load scheduling period of the potential matching area and the potential matching area data of the heat source.

[0012] Furthermore, the method for determining the matching coefficient is as follows: Based on the number of overlapping load scheduling periods between the identified heating area and the matching heating area of ​​the heat source, and the proportion of these overlapping periods in the load scheduling period of the matching heating area, the basic matching coefficient between the identified heating area and the matching heating area is determined. The matching coefficient is determined by summing the basic matching coefficients of the identified heating area and the matching heating area.

[0013] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for optimizing the allocation of heating loads from multiple heat sources when running the computer program.

[0014] Thirdly, the present invention provides a computer storage medium storing a computer program, which, when executed in a computer, causes the computer to execute the aforementioned method for optimizing the allocation of heating loads from multiple heat sources.

[0015] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of a method for optimizing the allocation of heating load from multiple heat sources; Figure 2 This is a flowchart illustrating the method for determining the heating area based on the matching of heat sources; Figure 3 This is a flowchart of a method for optimizing load regulation strategies for heating areas. Detailed Implementation

[0019] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0020] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0021] Example 1 like Figure 1 As shown, this application provides a method for optimizing the allocation of heating load from multiple heat sources, specifically including: S1 acquires the heating data of the heat source, uses the heating data to determine the heat loss data of the heating network between the heat source and different heating areas, and combines the load adjustment data of the heating areas to determine the matching heating areas of the heat source. S2 determines the matching heating area data and, in conjunction with the distribution data of the load scheduling period of the matching heating area, determines that the load adjustment strategy of the heating area needs to be optimized, and then proceeds to the next step. S3 determines the overlap between the load scheduling period of the heating area and the load scheduling period of different matching heating areas, determines the overlapping scheduling period based on the overlap, and identifies the heating area in the heating area based on the distribution data of the overlapping scheduling period in the load scheduling period of the heating area and the load scheduling area in the overlapping scheduling period. S4 determines the load scheduling processing strategy for the identified heating area under different heat sources based on the overlap of the load scheduling periods between the identified heating area and the matching heating areas of different heat sources.

[0022] Furthermore, the heating data of the heat source includes the heating time periods between the heat source and different heating areas.

[0023] Furthermore, the heat loss data of the heating pipeline network between the heat source and different heating areas is determined based on the heat loss rate of the heating pipeline network between the heat source and different heating areas.

[0024] Furthermore, the load regulation data for the heating area is determined based on the load scheduling period of the heating area and the distribution data of the load scheduling period on different dates.

[0025] Specifically, such as Figure 2 As shown, the method for determining the matching heating area of ​​the heat source is as follows: The fundamental goal of this method is to intelligently and dynamically match the most suitable heating area for each heat source in a multi-heat-source, multi-region heating system. Its core logic is to achieve a dual match between "transmission efficiency" and "dispatch demand." That is, it prioritizes heating areas that 1) have low heat transmission losses (low network heat loss rate) and 2) have stable or considerable load regulation demand (existing load scheduling periods), thus establishing a matching relationship with them. This maximizes energy utilization efficiency while fully leveraging the regulation capabilities of the heat sources, supporting load balancing of the power grid or system.

[0026] S11 uses the heat loss data of the heating pipe network between the heat source and the heating area to determine the heat loss rate of the heating pipe network between the heat source and the heating area, and takes the heating area with a heat loss rate less than a preset heat loss rate threshold as a potential matching area. Heat loss rate: refers to the percentage of heat energy lost during the process of transporting heat from the heat source to the heating area through the pipeline network. It is a key efficiency indicator for measuring the insulation performance and transmission distance of the pipeline network. The lower the heat loss rate, the higher the transmission efficiency.

[0027] Potential matching areas: These are areas selected from all heating areas whose heat loss rate in the connecting pipeline network to the current heat source is lower than a preset threshold. These areas are initially qualified in terms of "transmission efficiency".

[0028] This is the efficiency threshold screening for matching. The primary goal of a heating system is to efficiently deliver heat energy. If the heat loss in the pipeline is too high (e.g., exceeding 15%), even if the heat source is highly efficient, the energy efficiency at the end user will be very low, resulting in huge energy waste. This step first eliminates areas where the delivery efficiency is uneconomical due to aging pipelines or excessively long distances, ensuring that subsequent matching is based on efficient delivery.

[0029] Specific examples: Suppose there is a heat source A that provides heat to five different heating zones (R1 to R5). The heat loss rates of the pipe network between each zone and heat source A are calculated as follows: R1: 8%, R2: 12%, R3: 18%, R4: 9%, R5: 20%. A preset heat loss rate threshold of 15% is set. Zones R1 (8%), R2 (12%), and R4 (9%) with heat loss rates less than 15% are selected as potential matching zones. R3 and R5 are eliminated due to excessively high heat loss rates.

[0030] S12 determines the load scheduling period of the potential matching area based on the load adjustment data of the potential matching pipeline network; Load scheduling period: refers to the time period during which a heating area needs to actively adjust its load (such as peak shaving and valley filling) based on grid instructions, time-of-use electricity pricing, or its own heating consumption patterns. For example, increasing heating storage during off-peak electricity prices at night, or reducing heating load during peak electricity consumption periods during the day.

[0031] This is a functional value assessment for matching. Modern heating systems are not only unidirectional heating systems, but also important flexible and adjustable resources. Identifying the load scheduling periods of a region is to identify the "potential window" for that region to participate in system coordinated regulation. A region with clear and stable scheduling needs means, for the heat source, that it can undertake more important system regulation functions, and its matching value is higher.

[0032] Specific examples (continuous S11): Analyze the historical load data of the three potential matching regions (R1, R2, R4). Findings: Area R1: Typically, heat storage load adjustment is required between 22:00 and 6:00 the next day.

[0033] Region R2: Load reduction adjustments are only required between 17:00 and 19:00 on a few days with extreme weather warnings, with no obvious stable pattern.

[0034] Area R4: Load reduction is usually required between 12:00 and 14:00 on weekdays, and the pattern is stable.

[0035] These time periods are their respective "load scheduling periods," representing the window of opportunity when they can be scheduled by the heat source.

[0036] S13 uses the load scheduling period of the potential matching area and the potential matching area data of the heat source to determine the matching heating area of ​​the heat source.

[0037] It should be noted that if the number of potential matching areas of the heat source is less than the preset threshold for the number of matching areas, then all potential matching areas are determined to belong to the matching heating area of ​​the heat source.

[0038] If the number of potential matching areas is insufficient, and the number of eligible areas after heat loss screening is already small (e.g., less than two), it indicates that the efficient coverage of the heat source is limited. In this case, there is no need for further stringent scheduling demand screening; all these valuable, efficient delivery areas should be included in the matching process to maximize the basic heating value of the heat source. This is a pragmatic strategy based on resource scarcity.

[0039] It is also understood that if the number of potential matching regions of the heat source is not less than a preset threshold for the number of matching regions, it includes: S131 Based on the load scheduling period data of the potential matching area, determine whether the potential matching area has a load scheduling period on different dates. If so, determine that the potential matching area belongs to the matching heating area of ​​the heat source. If not, proceed to step S132. S132 determines the average total duration of the load scheduling period of the potential matching area on different dates based on the load scheduling period of the potential matching area. When the average total duration of the load scheduling period of the potential matching area on different dates is greater than a preset duration threshold, the potential matching area is determined to belong to the matching heating area of ​​the heat source.

[0040] When multiple candidate regions for efficient heat transfer exist, it is necessary to select the best among them, choosing those regions that can maximize the value of heat source regulation.

[0041] Step S131: Prioritize matching "demand-stable" regions: If a region has load scheduling periods on different dates (such as R1 and R4 in the example), it indicates that its scheduling demand is normalized and predictable. Matching with such regions allows heat sources to form stable and reliable regulation capabilities, contributing the most to system balance, and therefore should be prioritized for matching.

[0042] Step S132: Secondary matching of “significant demand” regions: If a region’s scheduling demand is not present every day, but once it occurs, it lasts for a long time (the average total duration is greater than the preset threshold, such as 4 hours), although the demand in such regions is not frequent, if the average load adjustment period on different dates is greater than the preset duration threshold, it is worth including in the matching.

[0043] Continuing with the previous example, heat source A has 3 potential matching regions (R1, R2, R4). Assuming the preset threshold for the number of matching regions is 2, since 3 > 2, we proceed to the refined screening process (S131).

[0044] S131 Judgment: Regions R1 (daily nighttime heat storage) and R4 (daily midday load reduction) both have load scheduling periods on different dates, therefore they are directly determined as matched heating regions. Region R2 is proceeding to S132 due to irregular scheduling periods (no).

[0045] S132 Decision: Assume that on a few days when scheduling is required, the average daily scheduling duration for region R2 is 1 hour. The preset duration threshold is 2 hours. Since 1 < 2, region R2 fails to meet this condition.

[0046] Final conclusion: The matching heating areas for heat source A are R1 and R4.

[0047] This method embodiment constructs a progressive matching model from physical efficiency to system function, and its core value lies in: It achieves a balance between energy efficiency and system value: the traditional static matching based on "closest distance and least heat loss" is upgraded to dynamic intelligent matching that takes into account both "pipeline efficiency" and "regional regulation potential", enabling the heating network to call only a portion of the heat source when adjusting the load, thus reducing the difficulty of load adjustment.

[0048] It improves the predictability and reliability of heat source scheduling capabilities: by prioritizing the matching of areas with stable scheduling periods, it ensures that heat sources can obtain predictable and sustainable adjustment tasks, which is conducive to the heat source itself (such as cogeneration units) to formulate better operation plans.

[0049] Specifically, such as Figure 3 As shown, the optimization of the load regulation strategy for the heating area needs to be determined, specifically including: The fundamental goal of this method is to construct a self-sensing and self-evaluating decision-triggered mechanism to determine whether the current load regulation strategy based on the "heat source-region" matching relationship is still efficient and whether systematic optimization is needed. Its core logic lies in monitoring "mismatch" phenomena during system operation: when a large number of load regulation tasks have to be undertaken by non-matched (i.e., regions with high heat loss) regions, it means that the system is operating in an inefficient state, or that the initial matching relationship is no longer suitable for the new scheduling requirements, thus triggering a global optimization procedure.

[0050] S21 uses the matching heating area data to determine the proportion of the matching heating area in the heating network, and uses it as the proportion of the matching area. Matching area percentage: This refers to the proportion of all heating areas covered by the heating network that have established an efficient matching relationship with a heat source (low heat loss rate and stable scheduling needs) out of the total number of areas. It reflects the coverage breadth of the current matching strategy.

[0051] This is a macro-level indicator for evaluating the efficiency of the system infrastructure. If the proportion of matched areas is very low (e.g., <30%), it indicates that most areas are in a state of "inefficient transmission" or "no stable scheduling relationship," and the overall energy efficiency of the system is weak. In this case, load regulation still requires the simultaneous use of multiple heat sources, making regulation more difficult. This itself constitutes a strong reason for global strategy optimization, aiming to expand the coverage of efficient matching from the root.

[0052] Suppose a heating network covers 10 heating zones (R1 to R10). Based on the pre-matching method, four zones (R1, R3, R5, R7) are identified as having established matching relationships with their respective heat sources. Therefore, the percentage of matched zones = 4 / 10 = 40%.

[0053] S22 Based on the load regulation data in the heating network, determine the heating areas that will undergo load regulation during the load scheduling period of the heating network, and designate the load scheduling periods in which the heating areas undergoing load regulation do not belong to the matching heating areas as other scheduling periods; Other scheduling periods: These refer to the periods in the historical or planned load scheduling of the entire heating network that undertake load regulation tasks but do not belong to any heat source's 'matched heating zone'. They record traces of "inefficient or temporary scheduling" outside the "efficient matching" framework.

[0054] This is a crucial step in discovering the discrepancy between the actual and ideal matching. It directly reveals the system's "compromise" behavior under pressure (when adjustments are needed). The frequent occurrence of "other scheduling periods" indicates that the existing matching relationship cannot meet the actual and dynamic scheduling needs, forcing the system to simultaneously utilize multiple heat sources, making adjustments more difficult. This is direct evidence that optimization is needed.

[0055] Specific examples (continuous S21): Analyzing the load dispatch records from the past week revealed that on a certain workday, between 2:00 PM and 3:00 PM, the system issued a load reduction command. The regions responsible for this adjustment were R2 and R4. However, neither R2 nor R4 was on the list of matched heating regions (R2 was not selected due to irregular dispatching, while R4 was a matched region and performed its task normally). Therefore, this period (2:00 PM - 3:00 PM) during which R2 participated in the adjustment was defined as an "other dispatching period." Statistics showed that there were 15 such "other dispatching periods" in the past week.

[0056] S23 determines whether the load adjustment strategy for the heating area needs to be optimized based on the heating areas that are not part of the matching heating area in the other scheduling periods and in combination with the proportion of the matching area.

[0057] It is understandable that if the heating area belongs to a matching heating area, the load adjustment process only needs to utilize the heat source of the matching heating area.

[0058] Specifically, based on the proportion of the matched area, it is determined whether the load regulation strategy for the heating area needs to be optimized, including the following situations: S231 Determine whether the proportion of the matching area is less than the preset area proportion threshold. If yes, then determine that the load adjustment strategy of the heating area needs to be optimized. If no, proceed to step S232. Determine if the matching coverage is too low This is a basic defect check. If the matching area percentage is extremely low (e.g., <50%), it means there is a fundamental problem with the system design, and most areas are not included in the optimization framework. At this point, there is no need to look at the details anymore; global optimization must be initiated immediately to redesign the matching relationships and expand the foundation for efficient operation.

[0059] A consistent example: The matching area accounts for 40%, and the preset threshold is 50%. Since 40% < 50%, the conclusion is: "Optimization of the load regulation strategy for the heating area is immediately required."

[0060] S232 Obtain the other scheduling time periods, determine whether the average daily number of the other scheduling time periods is greater than the preset time period number threshold, if yes, determine that the load adjustment strategy of the heating area needs to be optimized, if no, proceed to step S233. Determine if "Other Scheduling" is frequent: Even if the matching coverage is acceptable, if the system frequently needs to call non-matching regions for adjustment on a daily basis (e.g., the average number of "other scheduling periods" per day is greater than 3), it indicates that the current matching relationship is seriously out of sync with the actual daily scheduling needs. The energy waste and scheduling complexity accumulated from these frequent inefficient operations make optimization necessary.

[0061] A consistent example (assuming a matching rate of 60%, >50%): Statistical analysis shows that the average daily number of "other scheduling periods" is 4 (28 per week / 7 days). The preset threshold is assumed to be 3. Since 4 > 3, the conclusion is: optimization is needed.

[0062] S233 determines whether there are complex scheduling periods in other scheduling periods by the number of heating areas that do not belong to the matching heating area and are subject to load adjustment processing. If yes, proceed to step S234; otherwise, determine that there is no need to optimize the load adjustment strategy for the heating area. It should be noted that the complex scheduling period refers to other scheduling periods in which the number of heating areas that do not belong to the matching heating area and are subject to load regulation processing is greater than a preset threshold.

[0063] S234 determines the adjustment processing complexity factor by combining the average number of complex scheduling periods in different dates with the average number of heating areas that do not belong to the matching heating area and are subject to load adjustment processing in other scheduling periods. When the adjustment processing complexity factor is greater than the preset complexity factor threshold, it is determined that the load adjustment strategy of the heating area needs to be optimized.

[0064] It is understood that the adjustment processing complexity factor is determined based on the average number of complex scheduling periods on different dates and the average number of heating areas that do not belong to the matching heating area and are subject to load adjustment processing in other scheduling periods. The value ranges from 0 to 1. The higher the average number of complex scheduling periods on different dates and the higher the average number of heating areas that do not belong to the matching heating area and are subject to load adjustment processing in other scheduling periods, the higher the adjustment processing complexity factor will be.

[0065] Complex scheduling period: Within an "other scheduling period," multiple (number > preset threshold, e.g., >2) non-matching regions are required to participate in the adjustment process simultaneously. This represents a "peak" in scheduling pressure and a surge in coordination difficulty.

[0066] Complexity Factor Adjustment: A comprehensive quantitative indicator between 0 and 1, reflecting the overall severity of the "Other Scheduling" problem. It is determined by two dimensions: 1) the frequency (average number) of complex scheduling periods; 2) the average number of "problem areas" involved in each "Other Scheduling". The higher both are, the closer the factor value is to 1.

[0067] When "other scheduling" is infrequent but each instance is challenging, a more refined assessment is needed. The complexity factor in load balancing encompasses both the intensity and scope of the problem. A high value for this factor indicates that while problems don't occur daily, when they do, they are complex events with broad implications and significant systemic impact, making load balancing difficult. Therefore, optimization is necessary to fundamentally reduce the occurrence of such complex situations.

[0068] Consistent examples (assuming a matching rate of 60%, and an average daily number of other scheduling tasks of 2 < 3): S233: Analysis of "Other Scheduling Periods" within a week revealed that two periods simultaneously required adjustments to three non-matching regions, R2, R8, and R9 (number 3 > preset threshold 2). These two periods were marked as complex scheduling periods.

[0069] S234: The average number of complex scheduling periods within a week is calculated to be 2 / 7 ≈ 0.29 per day; among all “other scheduling periods”, the average number of non-matching areas involved each time is (2*3 + number of areas in other periods) / total number of periods, which is calculated to be 2.2.

[0070] Using the formula (e.g., complexity factor = MIN(1, 0.4 * average number of complex periods per day + 0.6 * average number of non-matching areas involved each time / total number of areas)), the complexity factor for adjustment is calculated to be 0.248, with a preset threshold of 0.2. Since 0.248 > 0.2, the conclusion is that the load adjustment strategy for the heating area needs to be optimized.

[0071] This method embodiment constructs a closed-loop management intelligent agent of "monitoring-diagnosis-triggering", the core value of which is: It has achieved a leap from passive operation to proactive optimization: the system no longer simply executes preset strategies, but continuously monitors the effect of strategy execution (the actual scheduling situation of matching and non-matching areas), and can automatically diagnose the state of strategy failure or inefficiency, and proactively initiate optimization requests.

[0072] A multi-dimensional, hierarchical optimization trigger standard was established: through progressive analysis from "coverage" to "frequency" and then to "complexity," a "one-size-fits-all" approach to decision-making was avoided. This approach can grasp both macro-level structural problems and keenly identify micro-level but serious operational pain points, ensuring the accuracy and timeliness of optimization triggers.

[0073] The "health" of the system's operation has been quantified: the introduction of indicators such as "adjusting and handling complex factors" allows for a quantitative assessment of the system's status. Managers and the system itself can clearly understand whether the current strategy can significantly reduce the difficulty of handling load regulation from multiple heat sources, providing data support for preventative maintenance and forward-looking optimization, and greatly enhancing the adaptability and long-term economic efficiency of the heating system as part of a smart energy network.

[0074] Furthermore, the overlapping scheduling period refers to the load scheduling period in the heating area that overlaps with the load scheduling period of the matching heating area.

[0075] Specifically, the method for identifying and determining the heating area within the heating area is as follows: Based on the established efficient "heat source-matching area" system, secondary diagnosis and potential assessment are conducted on the unmatched heating areas in the system. The aim is to identify those areas that were missed during the initial screening but whose actual scheduling behavior indicates that they have high collaborative value or unique functions. This provides precise targets for subsequent system optimization and ultimately reduces the complexity of multi-heat source collaborative scheduling. Its core logic is "behavioral diagnosis" and "value rediscovery". By analyzing the overlap of scheduling time between unmatched areas and efficient matching areas, it is determined whether they: (1) are highly collaborative in scheduling mode and possess the characteristics of becoming efficient areas; (2) play an indispensable and unique role in specific scheduling scenarios. Thus, optimization resources are precisely directed to the most valuable unmatched areas.

[0076] Suppose a heating network has completed intelligent matching and has 4 matched heating zones (R1, R2, R3, R4) and 4 unmatched heating zones (R5, R6, R7, R8). We will analyze whether the unmatched zones R5 and R6 should be identified as "identified heating zones" with optimization potential.

[0077] Overlapping scheduling periods specifically refer to the time periods during which the load scheduling periods of a non-matched heating area overlap with the load scheduling periods of any matched heating area.

[0078] Keyword Explanation: "Overlap" signifies temporal synchronization. It directly quantifies the degree of temporal alignment between the scheduling actions of a non-matching region and the system's "efficient scheduling core circle" (matching region).

[0079] This is a core indicator for assessing whether a mismatched area is "unintentionally" following an efficient scheduling pattern. The scheduling periods of a matched area are considered optimized and efficient. If the scheduling periods of a mismatched area highly overlap with those of an efficient area, it indicates that its load characteristics or the timing of its response commands are similar to those of an efficient area. It may be a potential problem area that is being "mistakenly harmed" due to slightly inferior performance in a single condition such as pipeline heat loss. Analyzing overlapping periods means starting from the actual "pain points" of system operation (i.e., complex moments requiring multi-heat-source coordination) to pinpoint the source of the problem.

[0080] S31 uses the distribution data of the load scheduling periods in the heating area to determine the proportion of overlapping scheduling periods in the heating area, and uses it as the proportion of overlapping periods. Assessing behavioral synergy (First screening: Diagnosis of high-frequency problem sources): Calculate the overlap period ratio of non-matching areas (such as R5), which is the proportion of the total duration of all overlapping scheduling periods to the total duration of its own total load scheduling periods. The "overlap period ratio" is a value between 0 and 1, which directly reflects what proportion of scheduling actions in this area will cause the system to enter the complex "multi-heat source coordination" operation mode.

[0081] This is the most direct quantification of the "burden." A high overlap rate (e.g., >70%) means that almost every time the region is scheduled, it forces the system to perform complex multi-heat source coordination. It is itself a systemic, high-frequency "complexity amplifier." Prioritizing its identification and optimization (such as pipeline network modification to reduce heat loss) is the most effective way to reduce the coordination pressure in the daily operation of the system and is the most direct way to achieve the goal of "reducing difficulty."

[0082] Specific example (continuous): Analyzing data from R5 over the past week, it has 10 scheduling periods (totaling 15 hours). Of these, 8 periods (totaling 12 hours) overlap with the scheduling periods of matching areas R1 or R3. Therefore, the overlap rate for R5 = 12 hours / 15 hours = 80%. Assuming a preset threshold of 70%, since 80% > 70%, the system directly identifies R5 as an "identified heating area." R5 is identified as a key target that frequently causes coordination complexity.

[0083] S32 takes the overlapping scheduling period of the load scheduling area including the heating area as the screening period, and determines the screening period in the screening period that only the load scheduling area exists except for the matching heating area, based on the load scheduling area data in the screening period, and takes it as the secondary screening period. S33 determines the identified heating area within the heating area based on the overlapping time period ratio and the secondary screening time period.

[0084] For regions with a low proportion of overlapping time periods (such as R6), conduct in-depth scenario analysis within the overlapping time periods.

[0085] Mark the overlapping scheduling periods where all R6 is one of the participants as the filtering periods. From these filtering periods, find those periods where only R6, the only non-matching region besides the matching region, is scheduled, and define them as the secondary filtering periods.

[0086] "Secondary filtering period" is a special scheduling scenario, whose scheduling instruction mode can be simplified to "1 (or more) matching regions + 1 specific non-matching region (i.e., R6)". It eliminates interference from other non-matching regions.

[0087] This step aims to identify "deterministically inefficient pairings." The secondary screening period reveals that, under specific scheduling requirements, the system algorithm or scheduler "intentionally" paired R6 with an efficient region. However, since R6 is not a matching region, this pairing is inherently inefficient (requiring the use of multiple heat sources). This reveals that while R6 possesses irreplaceable or superior adjustment characteristics in specific functional scenarios, its identity also makes it a "deterministic bottleneck." Optimizing such fixed pairings (e.g., targeted optimization of R6 with a heat source in one of the matching regions) can precisely dismantle a known complex scheduling combination.

[0088] Specific example (continuous): The overlap rate of R6 is 50%, which does not exceed the 70% threshold, so proceed to this step. Among all its overlapping scheduling periods, two periods (such as a certain afternoon and a certain evening) were found to have scheduling instructions that only included "matching region R2 + R6", without involving R5, R7, and R8. These two periods are marked as secondary screening periods, indicating that there is a specific, recurring, inefficient collaborative relationship between R6 and R2.

[0089] It should be noted that if the proportion of overlapping time periods is greater than a preset proportion threshold, then the heating area is determined to belong to the identified heating area.

[0090] Additionally, it is understood that if the overlap period ratio is not greater than a preset ratio threshold, the following content is also included: S331 Based on the overlapping scheduling period, determine the secondary screening period in the screening period, and determine whether the proportion of the secondary screening period in the screening period is greater than the preset screening proportion threshold. If yes, proceed to step S332; otherwise, determine that the heating area does not belong to the identified heating area. The proportion of secondary screening periods to the total number of screening periods is calculated. The "proportion of secondary screening periods" measures whether the "critical bottleneck" attribute of the region is the main pattern of its scheduling behavior or an occasional phenomenon.

[0091] If this ratio is high, it indicates that the "fixed inefficient pairing" of region (R6) with the matching region is its main way of participating in system scheduling, and optimizing it will bring significant and stable benefits. If the ratio is low, it indicates that the complex scenarios it causes are sporadic, and the optimization priority can be appropriately reduced. This ensures the expected rate of return on subsequent resource investment.

[0092] Specific example (continuous): R6 has 10 screening periods, 2 of which are secondary screening periods. Therefore, the proportion of secondary screening periods = 2 / 10 = 20%. Assuming the preset screening proportion threshold is 15%, since 20% > 15%, R6 passes this test, indicating that its "critical bottleneck" pattern is significant.

[0093] S332 obtains the proportion of the identified heating area in the heating areas excluding the matched heating area, and determines whether the proportion of the identified heating area in the heating areas excluding the matched heating area is less than a preset identification area proportion threshold. If so, the heating area is determined to belong to the identified heating area; otherwise, the heating area is determined not to belong to the identified heating area.

[0094] Global decision based on the current state of the system: Calculate the proportion of currently identified "identified heating areas" to the total number of all non-matching areas. The "identified heating area proportion" is a resource control valve set from the perspective of the entire system optimization project.

[0095] System optimization resources (such as renovation budgets, computing resources, and management effort) are limited. If many areas to be optimized have been identified (the proportion is already high), resources should be concentrated on addressing them first to avoid slow progress due to target generalization. At this point, the acceptance of new identified targets can be postponed. Conversely, if few targets have been identified, areas like R6 that have proven to have unique value should be actively included to ensure that the optimization project has sufficient targets and potential to be explored. This reflects the holistic view of intelligent systems in balancing the "breadth of the problem" and the "depth of the solution" in decision-making.

[0096] Specific example (final ruling): Currently, only R5 is identified (result S32), therefore the identification area ratio = 1 / 4 = 25%. Assume the system's preset identification area ratio threshold is 30%. Since 25% < 30%, and R6 has already demonstrated its significant value in S331, the system ultimately determines that R6 also belongs to the "identified heating area". This indicates that the system believes the current optimization target pool is not yet saturated, and the unique bottleneck value of R6 is worth including.

[0097] This embodiment of the "Identifying Heating Areas" method constructs a refined decision-making chain "from phenomenon diagnosis to value assessment, and then to overall planning," and its core value lies in: This method enables precise localization and proactive management of system operation problems: It allows the system to go beyond static initial matching results and proactively diagnose the "problem areas" that lead to increased scheduling complexity from actual operation data, thus realizing the transformation from "passively enduring inefficiency" to "proactively identifying and preparing for optimization".

[0098] It distinguishes different types of optimization potential and guides differentiated strategies: it clearly distinguishes between "high-frequency synergistic" potential areas (such as R5) and "specific bottleneck" potential areas (such as R6). This provides clear guidance for subsequent optimization: for the former, the focus should be on evaluating the feasibility of integrating it into an efficient network (such as modifying the pipeline network); for the latter, the focus should be on analyzing and solidifying its optimal synergistic relationship with a specific heat source to achieve precise "bomb disposal".

[0099] A dynamic allocation mechanism for global optimization resources has been introduced: by using the control parameter of "identifying the regional proportion threshold", the micro-level regional assessment is combined with the macro-level system optimization engineering management, ensuring that optimization actions are always focused and avoiding the dispersion of resources due to too many objectives. This allows for a steady reduction in the difficulty of overall system coordination and scheduling with the highest cost-effectiveness ratio, and enhances the continuous self-optimization capability of the heating system as a smart energy system.

[0100] Specifically, the method for determining the load scheduling strategy for a heating area under different heat sources is as follows: For each identified "identified heating area," a dynamic and optimal load dispatching strategy is developed, specifying which heat source(s) should perform the dispatch when needed, and how. The core logic is "coordination first, efficiency as the foundation, and dynamic adaptation." That is, priority is given to heat sources with the highest coordination time (high matching coefficient) with the identified area; simultaneously, pipeline transmission efficiency (heat loss rate) must be considered; and the aggressiveness of the strategy (whether to call a single optimal heat source or prepare multiple backup heat sources) is dynamically adjusted based on the urgency of the area's coordination complexity (number of secondary screening periods). The ultimate goal is to transform the dispatching of non-matching areas from "temporary, chaotic multi-heat source calls" to "planned, prioritized heat source call sequences," thereby significantly reducing the processing difficulty and uncertainty of each adjustment event.

[0101] System prerequisites: Continuing from the previous example, the system has identified two "identified heating areas": R5 (high-frequency coordinated type) and R6 (specific bottleneck type). The system has two heat sources: heat source A (primarily matching areas R1 and R2) and heat source B (primarily matching areas R3 and R4). Load scheduling strategies need to be developed for R5 and R6 respectively.

[0102] S41 determines the number of overlapping load scheduling periods between the identified heating area and the matching heating area of ​​different heat sources based on the overlap of the identified heating area and the matching heating area of ​​the heat source. For a given heating area (e.g., R5), the total number of times its load scheduling period overlaps with the load scheduling periods of all matching areas under each heat source is counted. The "overlapping number" is an absolute value, referring to the frequency of collisions between the identified area and the "sphere of influence" (its matching areas) of a certain heat source in terms of scheduling time.

[0103] This is the starting point for assessing the "closeness" between the identified region and the scheduling of each heat source. The more times there is overlap, the closer the historical scheduling demand of that region is related to the operating rhythm of a certain heat source. For example, if R5 is always scheduled simultaneously with the matching regions (R1, R2) of heat source A, it indicates that it and the load cluster served by heat source A may have similar heat consumption patterns or be affected by the same grid commands. This provides the most basic data correlation for subsequent allocation of scheduling responsibilities.

[0104] Specific example: Analyze the scheduling records of R5. Statistics show that the scheduling periods of R5 overlap with the scheduling periods of the matching region (R1, R2) of heat source A 15 times, and overlap with the scheduling periods of the matching region (R3, R4) of heat source B 5 times. Therefore, the number of overlaps between R5 and heat source A is 15, and the number of overlaps with heat source B is 5.

[0105] S42 determines the matching coefficient between the identified heating area and the heat source based on the number of overlapping load scheduling periods between the identified heating area and the matching heating area of ​​the heat source. Calculate the basic matching coefficient between the identified region (R5) and each matching region (such as R1) using the formula: (Number of overlapping time periods between R5 and R1) / (Total number of scheduling time periods for R1 itself). Then, add the basic matching coefficients of the identified region to all matching regions under the same heat source to obtain the matching coefficient between the identified region and the heat source.

[0106] The "matching coefficient" is a normalized relative value that represents the average temporal probability of coordination between the scheduling behavior of the identified region and the matching region cluster of a certain heat source. The higher the coefficient, the stronger the coordination.

[0107] Using only the "number of overlaps" biases towards heat sources that are already frequently scheduled. The basic matching coefficient, by dividing by the scheduling frequency of the matching region itself, eliminates this scale bias and better reflects the "conditional probability" relationship between the two heat sources. Adding the coefficients for the same heat source aggregates the overall synergy strength between the identified region and the entire heat source "team." The matching coefficient is the core quantitative indicator for measuring "scheduling synergy" and is the primary basis for selecting the preferred heat source for scheduling.

[0108] Specific example (continuous): Given that R1 has 20 scheduling periods, and R5 overlaps with R1 10 times, then the basic matching coefficient between R5 and R1 = 10 / 20 = 0.5. Assume the basic matching coefficient between R5 and R2 is 0.3. Then, the matching coefficient between R5 and heat source A = 0.5 + 0.3 = 0.8. Similarly, the matching coefficient between R5 and heat source B is calculated to be 0.2. For R6, its matching coefficient with heat source A is calculated to be 0.7, and its matching coefficient with heat source B is 0.1.

[0109] S43 determines the load scheduling strategy for the identified heating area under different heat sources based on the matching coefficient between the identified heating area and different heat sources and the secondary screening time period data of the identified heating area.

[0110] Furthermore, the method for determining the matching coefficient is as follows: Based on the number of overlapping load scheduling periods between the identified heating area and the matching heating area of ​​the heat source, and the proportion of these overlapping periods in the load scheduling period of the matching heating area, the basic matching coefficient between the identified heating area and the matching heating area is determined. The matching coefficient is determined by summing the basic matching coefficients of the identified heating area and the matching heating area.

[0111] It should be noted that if the number of secondary screening periods in the most recent preset time period of the identified heating area is greater than the preset screening period number threshold, then when the identified heating area needs to be subjected to load scheduling, load scheduling will be performed under different heat sources. For example, with the constraint that only one heat source is used in each load adjustment process, according to the overall matching logic entry: first check the frequency of the identified area as a "critical bottleneck" (i.e. appearing in the secondary screening period) in the recent period (such as the past week).

[0112] Scenario 1: High-frequency bottleneck (emergency situation): If the number of secondary screening periods exceeds the preset threshold (e.g., 4), it indicates that the region has recently been frequently generating "deterministically inefficient pairings," which is a prominent pain point in the system's operation and requires high vigilance.

[0113] When the region requires scheduling, all heat sources must prepare scheduling plans. The constraint is that only one heat source can be activated for each actual adjustment. In practice, different heat sources are tried in descending order of their matching coefficients to simulate or actually adjust the load, until a heat source is found that can meet the adjustment duration requirement for that specific load adjustment demand.

[0114] This is a safeguard strategy. For high-frequency problem areas, a single "optimal" heat source may not be able to meet the regulation speed or capacity requirements under certain special operating conditions. By keeping all heat sources on standby and trying them in descending order of synergy, the system ensures that under any complex operating condition, it can always find a feasible heat source to "take over" the task in this difficult area, greatly improving the scheduling success rate and system resilience. This is equivalent to having multiple experts consult for "difficult and complicated problems."

[0115] Specific example (for R5): Suppose R5 generated 5 secondary screening periods in the past week, exceeding the threshold of 4. Therefore, the system's strategy for R5 is: when scheduling is required, both heat source A (coefficient 0.8) and heat source B (coefficient 0.2) enter a standby state. During the current scheduling process, heat source A is attempted, and in the next utilization, the system immediately switches to heat source B. The coefficients are ordered from high to low, and all heat sources are utilized sequentially for load scheduling in the above order.

[0116] It should also be noted that if the number of secondary screening periods within the most recent preset time period for the identified heating area is not greater than the preset screening period number threshold, this specifically includes: S431 Based on the matching coefficient with different heat sources, determine whether there is a region where the matching coefficient is greater than the preset matching coefficient threshold. If yes, proceed to step S432. If no, when the identified heating area needs to be load-scheduled, load scheduling is performed under different heat sources. For example, with the constraint that only one heat source is used in each load adjustment process, load scheduling is performed using all heat sources in order of matching coefficient from high to low. S432 identifies heat sources with matching coefficients greater than a preset matching coefficient threshold as associated heat sources and determines whether multiple associated heat sources exist. If so, when the identified heating area needs load scheduling, load scheduling is performed under different associated heat sources. For example, with the constraint that only one heat source is used in each load adjustment process, load scheduling is performed using all associated heat sources in order of matching coefficient from high to low. If not, proceed to step S333. S433 determines the comprehensive matching value of the heat source based on the matching coefficient of the heat source and the heat loss rate between the heat source and the identified heating area, and selects a preset number of heat sources with the largest comprehensive matching value as regulating heat sources. That is, when the identified heating area needs to be subjected to load scheduling, load scheduling is performed under different regulating heat sources. For example, with the constraint that only one heat source is used in each load scheduling process, load scheduling is performed using all regulating heat sources in order of matching coefficient from high to low.

[0117] Scenario 2: Low-frequency bottleneck (general case): If the number of secondary screening periods is less than or equal to the preset threshold, then a more refined decision-making process (S431-S433) will be initiated.

[0118] S431: Check for the presence of a strong co-current heat source. Determine if there is a heat source with a matching coefficient greater than a preset matching coefficient threshold (e.g., 0.6). This threshold is used to define whether there is an "obvious" best cooperating object. If it exists, prioritize developing a strategy around that heat source; if it does not exist (all coefficients are average), it means that the identified area is not strongly associated with any heat source, and a multi-heat source alternative strategy still needs to be activated (same as case one) to avoid selecting the wrong primary heat source due to "choosing the best among the worst".

[0119] Specific example (for R6): R6 has two recent secondary screening periods (≤ threshold 3). Its matching coefficient with heat source A is 0.7, and with heat source B it is 0.1. The preset coefficient threshold is 0.6. Because 0.7 > 0.6, proceed to S432.

[0120] S432: Handling multiple strongly correlated heat sources: Check the number of heat sources with a matching coefficient greater than the threshold. If multiple heat sources exist, treat them all as "associated heat sources" and adopt a multi-heat source sequential invocation strategy similar to Case 1, but narrow the scope to these associated heat sources. This balances synergy and complexity. If there is only one heat source, proceed to S433 for final comprehensive evaluation.

[0121] Specific example (continuous): For R6, only heat source A (0.7>0.6) meets the condition and is the only "associated heat source". Therefore, proceed to S433.

[0122] S433: Comprehensive evaluation determines the final dispatch heat source: For a single strongly correlated heat source (heat source A), its comprehensive matching value is calculated. This value comprehensively considers the matching coefficient (synergy) and the pipeline heat loss rate (efficiency) between the heat source and the identified area. A preset number (e.g., 2) of heat sources with the highest comprehensive matching values ​​are selected as "regulating heat sources".

[0123] This is the final decision-making point, achieving a balance between "scheduling coordination" and "physical efficiency." A heat source might be well-matched in terms of scheduling time (high coefficient), but if the heat loss in the pipeline network leading to the area is significant, the actual scheduling efficiency will be low. The comprehensive matching value integrates both factors, ensuring that the selected heat source is not only "willing" to schedule the area in terms of time, but also "efficiently" able to complete the scheduling in terms of physical properties. The final determined "regulatory heat source" is the most ideal and economical primary heat source for the identified area.

[0124] Strategy: When the region needs to be dispatched, load dispatching is carried out under different heat sources, and the same order of matching coefficients is tried.

[0125] Specific example (final outcome): The matching coefficient between heat source A and R6 is known to be 0.7, but the heat loss rate of the pipeline from heat source A to R6 is 14%. Although the matching coefficient between heat source B and R6 is only 0.1, there is a dedicated short pipeline from heat source B to R6, with a heat loss rate of only 10%. Using a comprehensive matching value model (e.g., comprehensive value = 0.6 * matching coefficient + 0.4 * (1 - heat loss rate)), it may be found that the comprehensive value of heat source A is actually higher. Selecting two heat sources, heat source A and heat source B are determined as the 'regulating heat sources' for R6. The strategy is: when R6 needs to be scheduled, heat source A and heat source B are used in the same way.

[0126] This embodiment of the "scheduling and processing strategy determination" method constructs a closed-loop decision engine "from data analysis to strategy generation," and its core value lies in: It transforms the abstract concept of "identifying heating areas" into a concrete set of scheduling instructions on "who adjusts and how," thus completing the leap from problem diagnosis to treatment plan and enabling the optimization chain of the entire intelligent heating system to be closed.

[0127] The method no longer relies on a single indicator for decision-making, but dynamically balances three key factors: scheduling coordination (matching coefficient), transmission efficiency (heat loss rate), and the urgency of the problem (number of secondary screening periods). The strategy can flexibly switch between "guaranteed multi-source backup" and "economical single-source selection" based on the urgency of the regional "illness," demonstrating a high degree of intelligence and practicality.

[0128] By pre-determining the heat source call sequence and strategy for each difficult-to-adjust area, the system does not require temporary calculations and coordination when actual scheduling commands are issued; it simply executes according to the predetermined strategy sequence. In particular, the mechanism of "trying in sequence until the adjustment duration is met" ensures the inevitable completion of the adjustment task while adhering to the simplification principle of "using only one heat source per adjustment," fundamentally reducing the decision-making difficulty and operational complexity of real-time load adjustment processing. This allows multi-heat-source systems to respond as quickly and control as single-heat-source systems.

[0129] Furthermore, the overall matching value of the heat source is related to the matching coefficient of the heat source and the heat loss rate between the heat source and the identified heating area, wherein the higher the matching coefficient and the lower the heat loss rate, the higher the overall matching value.

[0130] It is understandable that by performing load scheduling in sequence, heat sources that meet the requirements for adjustment duration under different load adjustment needs can be obtained. Then, if there is only a matching adjustment area that needs to be adjusted during the load adjustment period, the heat source that is the same as the matching adjustment area and meets the requirements for adjustment duration under different load adjustment needs can be used for load scheduling, thereby reducing the difficulty of load adjustment.

[0131] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for optimizing the allocation of heating loads from multiple heat sources when running the computer program.

[0132] Example 3 Thirdly, the present invention provides a computer storage medium storing a computer program, which, when executed in a computer, causes the computer to execute the aforementioned method for optimizing the allocation of heating loads from multiple heat sources.

[0133] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0134] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0135] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for optimizing the allocation of heating load from multiple heat sources, characterized in that, Specifically, it includes: The heat source's heating data is acquired, and the heat loss data of the heating network between the heat source and different heating areas is determined using the heating data. Combined with the load adjustment data of the heating areas, the matching heating areas of the heat source are determined. Once the matching heating area data is determined, and combined with the distribution data of the load scheduling period of the matching heating area, if it is determined that the load adjustment strategy of the heating area needs to be optimized, proceed to the next step; Determine the overlap between the load scheduling periods of the heating area and the load scheduling periods of different matching heating areas, determine the overlapping scheduling periods based on the overlap, and identify the heating areas in the heating area based on the distribution data of the overlapping scheduling periods in the load scheduling periods of the heating area and the load scheduling areas in the overlapping scheduling periods. Based on the overlap of load scheduling periods between the identified heating area and the matching heating areas of different heat sources, a load scheduling processing strategy for the identified heating area under different heat sources is determined.

2. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The heating data of the heat source includes the heating time periods between the heat source and different heating areas.

3. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The heat loss data of the heating pipeline network between the heat source and different heating areas is determined based on the heat loss rate of the heating pipeline network between the heat source and different heating areas.

4. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The load regulation data for the heating area is determined based on the load scheduling period of the heating area and the distribution data of the load scheduling period on different dates.

5. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The method for determining the matching heating area of ​​the heat source is as follows: Using the heat loss data of the heating network between the heat source and the heating area, the heat loss rate of the heating network between the heat source and the heating area is determined, and the heating areas with a heat loss rate less than a preset heat loss rate threshold are identified as potential matching areas. Based on the load regulation data of the potential matching pipeline network, determine the load scheduling period of the potential matching area; The matching heating area of ​​the heat source is determined based on the load scheduling period of the potential matching area and the potential matching area data of the heat source.

6. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 5, characterized in that, If the number of potential matching areas of the heat source is less than a preset threshold for the number of matching areas, then all potential matching areas are determined to belong to the matching heating area of ​​the heat source.

7. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The optimization of load regulation strategies for the heating area needs to be determined, specifically including: The proportion of the matching heating area in the heating network is determined based on the matching heating area data, and this proportion is used as the matching area proportion. Based on the load regulation data in the heating network, the heating areas that undergo load regulation during the load scheduling period of the heating network are determined, and the load scheduling periods in which the heating areas undergoing load regulation do not belong to the matching heating areas are designated as other scheduling periods. Based on the heating areas that do not belong to the matching heating areas in the other scheduling periods and are subject to load adjustment, and in combination with the proportion of the matching areas, it is determined whether the load adjustment strategy of the heating area needs to be optimized.

8. The method for optimizing the allocation of heating load from multiple heat sources as described in claim 1, characterized in that, The method for determining the load scheduling strategy for a heating area under different heat sources is as follows: Based on the overlap of the load scheduling periods between the identified heating area and the matching heating areas of different heat sources, the number of overlaps in the load scheduling periods between the identified heating area and the matching heating areas of the heat source is determined. The matching coefficient between the identified heating area and the heat source is determined based on the number of overlapping load scheduling periods between the identified heating area and the matching heating area of ​​the heat source. Based on the matching coefficients between the identified heating area and different heat sources, and the secondary screening time period data of the identified heating area, the load scheduling and processing strategies for the identified heating area under different heat sources are determined.

9. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a method for optimizing the allocation of heating loads from multiple heat sources as described in any one of claims 1-8.

10. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed in a computer, it is characterized in that, The computer is instructed to execute the multi-heat source heating load optimization allocation method as described in any one of claims 1-8.

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