Heating resource allocation method and system based on global data analysis of gas heating

Abstract

The present application provides a heating resource allocation method and system based on gas heating global data analysis, which relates to the field of smart heating technology. The method determines the primary heating network and the secondary heating network under the entire heating domain, introduces the first gating unit and the second gating unit, constructs a demand forecasting model, performs data validation and heating demand decision-making on the input data set, determines the demand forecast data, and allocates heating resources under the primary heating network and the secondary heating network with the constraint of minimizing heat loss in the pipeline. The resource allocation strategy is determined, which solves the technical problems of the variability of scenarios in the prior art, resulting in insufficient accuracy in supply and demand prediction, and the error superposition under the complex interweaving of the heating network, resulting in insufficient accuracy in heating resource allocation and insufficient scenario fit. By optimizing the demand forecasting and resource allocation process, accurate supply and demand forecasting and reasonable resource allocation in variable scenarios are achieved.

Description

Technical Field

[0001] The present application relates to the field of smart heating technology, and specifically to a heating resource allocation method and system based on global data analysis of gas heating. Background Art

[0002] With the acceleration of urbanization, the demand for heating is increasing. With the introduction of artificial intelligence, the multiple challenges of traditional heating systems such as low energy efficiency, resource waste and environmental pollution have been solved to a certain extent. They often rely on fixed resource allocation strategies and lack flexibility and real-time performance, resulting in the inability of heat to effectively meet the changing needs of different regions. At the same time, new challenges have been introduced.

[0003] This includes effectively integrating multi-dimensional data, processing information flows in real time, and rapidly responding to and adjusting to dynamically changing demand. Furthermore, climate change and external environmental factors are increasingly impacting heating demand, making it difficult to accurately predict and allocate resources, resulting in heat losses and reduced user satisfaction.

[0004] In summary, the existing technology still has certain technical limitations. The variability of scenarios makes the prediction accuracy of supply and demand insufficient. In addition, the errors accumulated under the complex interweaving of the heating network lead to the lack of accuracy and scenario fit in the allocation of heating resources. Summary of the Invention

[0005] The present application provides a heating resource allocation method and system based on global data analysis of gas heating, which is used to solve the technical problems that the variability of scenarios existing in the existing technology makes the prediction accuracy of supply and demand insufficient, and the errors are superimposed under the complex interweaving of the heating network, resulting in insufficient accuracy of heating resource allocation and insufficient fit with the scenario.

[0006] In view of the above problems, the present application provides a heating resource allocation method and system based on global data analysis of gas heating.

[0007] In a first aspect, the present application provides a heating resource allocation method based on global gas heating data analysis, the method comprising:

[0008] Determine the primary heating network and secondary heating network for the entire heating area, where the primary heating network is from the gas facility side to the heating station side, and the secondary heating network is from the heating station side to the demand side;

[0009] A first gating unit and a second gating unit are introduced to construct a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment;

[0010] Obtaining an input data set, performing data validation and heat demand decision-making in combination with the demand forecasting model, and determining demand forecast data, wherein the input data set includes real-time heat supply data, preset time zone heat supply data, and external factors;

[0011] Based on the demand forecast data and with the minimization of heat loss in pipelines as a constraint, heating resources are allocated between the primary heating network and the secondary heating network to determine a resource allocation strategy.

[0012] In a second aspect, the present application provides a heating resource allocation system based on global gas heating data analysis, the system comprising:

[0013] A heat network determination module, which is used to determine the primary heat network and the secondary heat network in the entire heat supply area, wherein the primary heat network is from the gas facility side to the heat power station side, and the secondary heat network is from the heat power station side to the demand side;

[0014] a model construction module, the model construction module being used to introduce a first gating unit and a second gating unit to construct a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment;

[0015] a demand forecasting module, the demand forecasting module being configured to obtain an input data set, perform data validation and heating demand decision-making in conjunction with the demand forecasting model, and determine demand forecast data, wherein the input data set includes real-time heating data, preset time zone heating data, and external factors;

[0016] A resource allocation module is used to allocate heating resources between the primary heating network and the secondary heating network based on the demand forecast data and with the minimization of heat loss in the pipeline as a constraint, and to determine a resource allocation strategy.

[0017] In a third aspect, an electronic device comprises: a processor, wherein the processor is coupled to a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the system executes the steps of the method in the first aspect.

[0018] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the method steps described in the first aspect are implemented.

[0019] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0020] The embodiment of the present application provides a heating resource allocation method based on global data analysis of gas heating, determines the primary heating network and the secondary heating network under the global heating area, introduces the first gating unit and the second gating unit, constructs a demand forecasting model, performs data validation and heating demand decision-making on the input data set, determines the demand forecast data, and allocates heating resources under the primary heating network and the secondary heating network with the constraint of minimizing heat loss in the pipeline, and determines the resource allocation strategy, which solves the technical problems of the variability of scenarios existing in the prior art, resulting in insufficient accuracy in supply and demand prediction, and the error superposition under the complex interweaving of the heating network, resulting in insufficient accuracy of heating resource allocation and insufficient fit for the scenario. By optimizing the demand forecasting and resource allocation process, accurate supply and demand forecasting and reasonable resource allocation in variable scenarios are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a heating resource allocation method based on global data analysis of gas heating is provided for this application;

[0022] Figure 2 A schematic diagram of the heating resource allocation system structure based on global data analysis of gas heating is provided for this application;

[0023] Figure 3 A structural diagram of an electronic device is provided for this application. DETAILED DESCRIPTION

[0024] The present application provides a heating resource allocation method and system based on global data analysis of gas heating, determines the primary heating network and the secondary heating network under the global heating area, introduces a first gating unit and a second gating unit, constructs a demand forecasting model, performs data validation and heating demand decision-making on the input data set, determines demand forecast data, and allocates heating resources under the primary heating network and the secondary heating network with the constraint of minimizing heat loss in the pipeline, and determines a resource allocation strategy to solve the technical problems of the variability of scenarios existing in the prior art, resulting in insufficient accuracy in supply and demand predictions, and the superposition of errors under the complex interweaving of the heating network, resulting in insufficient accuracy in heating resource allocation and insufficient fit with the scenarios.

[0025] Example 1

[0026] like Figure 1 As shown, the present application provides a heating resource allocation method based on gas heating global data analysis, the method comprising:

[0027] S1: Determine the primary heating network and secondary heating network in the entire heating area, wherein the primary heating network is from the gas facility side to the heating station side, and the secondary heating network is from the heating station side to the demand side;

[0028] In modern heating systems, the rational division of primary and secondary heat networks is key to ensuring efficient energy management. The primary heat network refers to the heat energy transmission network from gas-fired facilities (such as natural gas power plants or boiler rooms) to thermal power stations (such as centralized heating stations). This network is primarily responsible for transporting generated heat to the thermal power stations at high temperature and pressure, minimizing heat loss and ensuring heat stability during long-distance transmission.

[0029] Once the heat reaches the heating station, it enters the secondary heating network, a system that distributes heat from the heating station to the final demand side (such as residential and commercial buildings). The secondary heating network is designed to distribute heat to each user at the appropriate temperature and pressure, ensuring timely response to user needs and flexible heating supply. At this point, the heating station converts the high-temperature hot water transmitted from the primary heating network into heating parameters suitable for each user, thereby achieving efficient heat distribution.

[0030] Due to the differences in the specific structures of heating networks, namely the relatively gradual distribution of piping in the primary heating network, which is mostly direct, and the more complex and branching distribution of piping in the secondary heating network, segmentation facilitates adaptive resource allocation based on the specific distribution characteristics of the heating network, thereby improving adaptability and ensuring optimal heat distribution and minimized losses under different load conditions. Through scientific resource allocation, the heating system can achieve optimized operation in a dynamic environment.

[0031] S2: Introduce a first gating unit and a second gating unit to build a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment;

[0032] Specifically, the primary function of the first gating unit is to control the flow of reset information from the previous moment. That is, during the demand forecasting process, it is responsible for filtering and resetting data inputs that are no longer relevant or important. When the heating demand environment changes, such as sudden fluctuations in user demand, the first gating unit can quickly identify and adjust the model's internal state to suppress the impact of outdated information. This dynamic reset mechanism ensures that the model always makes predictions based on the latest, most relevant data, thereby improving the real-time and accuracy of the forecasts.

[0033] The second gating unit focuses on controlling the flow of information from the previous moment, selectively retaining important information for use in subsequent calculations. Specifically, when receiving new input data, the second gating unit evaluates the importance of the information from the previous moment and determines which information should be passed on to the next stage of the forecast. In this way, the model maintains a valid information flow, thereby constructing a more accurate heat demand forecast.

[0034] Combined with these two gating units, the demand forecasting model can achieve efficient information management and efficient and accurate demand forecasting. In the embodiment of the present application, the gated loop algorithm principle, such as the long short-term memory network, is combined to realize the functions of these two units, which can enhance the model's ability to process time series data. Not only does it improve the prediction accuracy of heating demand, but it also reduces the computational burden caused by data redundancy, making the system more flexible and efficient when dealing with complex environmental changes, so that the heating demand forecasting model can be quickly adjusted in a dynamically changing environment, improving the accuracy of demand forecasting to optimize the configuration of thermal energy resources, and thus improving the efficiency of the entire heating system and user satisfaction.

[0035] Wherein, the construction of the demand forecasting model, step S2 of this application further includes:

[0036] Call historical heating data and perform data cleaning and normalization to determine the first sample and the second sample that have a mapping relationship; based on the first sample, construct the first gating unit by training a first reset function, wherein the first reset function is generated based on the supervised learning of the first sample; based on the second sample, construct the second gating unit by training a second transfer function, wherein the second transfer function is generated based on the supervised learning of the second sample; and control the first gating unit and the second gating unit in parallel to generate the demand forecasting model.

[0037] In an embodiment of the present application, the historical heating data is a record of heating distribution for the entire heating area. Due to accuracy and data format issues of the data, in order to ensure the quality and consistency of the input data, the historical heating data is cleaned and normalized in advance.

[0038] Specifically, the primary purpose of data cleaning is to remove incomplete, duplicate, or noisy data to ensure that model training is based on high-quality information. Examples include addressing missing values, removing outliers, and standardizing data formats. Normalization converts values ​​from different ranges to the same scale to prevent certain features from unduly affecting model training.

[0039] Next, the cleaned and normalized data is divided into a first sample and a second sample. Specifically, the first heating supply record is divided and mapped based on whether the data is reset or continued in the current scenario, and the first sample and the second sample are added respectively.

[0040] In an embodiment of the present application, the first reset function is used to determine the reset probability of new input data. As the sample training progresses, the system is generated based on sample supervised learning, that is, based on the relationship captured by the training samples.

[0041] The first sample is usually used to train the first reset function, focusing on capturing the instantaneous changes and dynamic characteristics of heating demand. The second sample is used to train the second transfer function, aiming to capture the persistent characteristics and long-term dependencies in the time series.

[0042] Using supervised learning, a first reset function trained on the first sample is used to construct the first gating unit, which is responsible for dynamically adjusting the model's state and promptly resetting irrelevant information. This reset mechanism helps enhance the model's sensitivity to sudden changes in demand and improves the timeliness of forecasts.

[0043] Simultaneously, a second transfer function trained on the second sample is used to construct a second gating unit. Its primary function is to selectively retain important information, ensuring that valid data is passed on for the next prediction. The parallel operation of these two gating units enables the model to flexibly process time series data, resulting in excellent performance in both short-term and long-term demand forecasting.

[0044] Ultimately, by integrating the first gating unit and the second gating unit, the generated demand forecasting model has powerful data processing capabilities, which can achieve accurate data processing and forecasting when faced with complex and dynamic heating demands.

[0045] S3: Acquire an input data set, perform data validation and heating demand decision-making in combination with the demand forecasting model, and determine demand forecast data, wherein the input data set includes real-time heating data, preset time zone heating data, and external factors;

[0046] The input dataset mainly consists of three parts: real-time heating data, preset time zone heating data and external factors, which provides a comprehensive perspective for demand forecasting.

[0047] Real-time heating data refers to the current state of the heating system, including information such as users' real-time heat demand, pipe network flow rates, and temperatures. This data is collected in real time through monitoring sensors and data acquisition systems, directly reflecting immediate load changes in the heating system and providing up-to-date input for the model.

[0048] The preset time zone heating data is the heating data of the preset time zone forward from the current moment, which has a time pattern. It usually includes the heating demand characteristics of different time periods, which helps the model understand the cyclical characteristics of heating demand and facilitates trend prediction based on previous heating demand.

[0049] External factors encompass various environmental factors that influence heat demand, such as weather conditions, holidays, and economic activity. For example, cold weather can lead to a surge in heat demand, while holiday periods can cause a significant drop in demand in certain areas. Incorporating these external factors into the model can help improve the accuracy of demand forecasts.

[0050] In this embodiment of the present application, the first and second gate control units perform reset and transfer analysis on the real-time heating data and the preset time zone heating data, and combine these with external factors to make predictions and determine the demand-side allocation criteria. This effectively improves the accuracy and flexibility of demand forecasts, enabling the heating system to implement intelligent scheduling decisions in a dynamically changing environment, thereby enhancing overall energy efficiency and user satisfaction.

[0051] Wherein, the step S3 of determining demand forecast data further includes:

[0052] The input data set is transmitted to the demand forecasting model; based on the first gating unit resetting the data and processing it, the second gating unit locates the transmitted data and retains it, the units interact and perform demand forecasting to determine the demand forecast data.

[0053] First, the input data set is fed into the demand forecasting model, where the first and second gating units within the model run in parallel. The first gating unit is responsible for locating and resetting the data. Specifically, it assesses the relevance of the current input data and dynamically resetting unnecessary information based on the model's state. For example, when new real-time heating data indicates a change in demand, the first gating unit can promptly suppress outdated data input, ensuring that the model makes predictions based only on the latest and most relevant data.

[0054] Meanwhile, the second gating unit locates and retains important historical information. It analyzes data from previous moments and selectively passes on information that is helpful for forecasting. This process ensures that the model effectively utilizes long-term data and enhances its understanding of heating demand.

[0055] In an embodiment of the present application, under the model training mechanism, the reset probability is determined by the first reset function. Preferably, a first critical probability value can be preset, such as 70%, and the first gating unit will reset the data portion that is higher than the first critical probability value; the transfer probability is determined by the second transfer function. Similarly, a second critical probability value can be preset, and the second gating unit will transfer data that is higher than the second critical probability value.

[0056] Furthermore, based on the interaction between the first and second gating units, demand forecasting is performed based on valid data to generate the required demand forecast data. By operating in parallel, the dynamic resetting of the first gating unit and the information transfer of the second gating unit can be combined to form a comprehensive and accurate demand forecast.

[0057] Among them, step S3 of this application also includes: the preset time zone heating data is determined by pushing forward the preset time zone at a real-time moment node; wherein, when the output of the first reset function is closer to 0, the feature neglect degree of the preset time zone heating data is higher; when the output of the second transfer function is closer to 1, the feature transfer degree of the preset time zone heating data is higher.

[0058] Specifically, the preset time zone heating data is used to analyze the real-time and effectiveness of the model output. During the real-time operation of the model, these data are based on the real-time moment node to push forward the preset time zone and perform auxiliary analysis to ensure that the data is referenceable. Specifically, when the output value of the first reset function approaches 0, it means that the feature neglect degree of the preset time zone heating data is higher, that is, the model tends to rely more on the current real-time data; on the contrary, when the output value of the second transfer function approaches 1, it means that the feature transfer degree of the preset time zone heating data is higher, that is, the model relies more on this part to form a prediction.

[0059] For example, when making long-term predictions, the trend characteristics based on the preset time zone heating data can effectively assist in subsequent trend predictions, and the dependence on real-time data is low; when making short-term predictions, it may be more dependent on real-time data, and through flexible processing, the accuracy of the prediction can be effectively improved.

[0060] The dynamic adjustment mechanism provided in the embodiment of the present application enables the model to flexibly adapt to the changing heating environment, thereby improving the accuracy and reliability of the prediction, allowing the system to still make accurate demand predictions under complex conditions, and providing a scientific basis for the effective allocation of heating resources.

[0061] S4: Based on the demand forecast data and with the minimization of heat loss in pipelines as a constraint, allocate heating resources between the primary heating network and the secondary heating network, and determine a resource allocation strategy.

[0062] Specifically, during the allocation and transmission of heating resources, the longer the distance, the greater the heat loss. Minimizing heat loss in pipelines is a constraint to ensure efficient operation of the heating system. Pipeline heat loss refers to the energy lost due to heat dissipation as it passes through the pipelines. To reduce this loss, the heating system needs to optimize the allocation and scheduling process.

[0063] In this embodiment of the present application, the demand forecast data serves as the resource allocation standard on the demand side. Considering the complex pipelines and numerous intersections in the actual allocation and scheduling process, a recursive decision is made by combining the primary and secondary heating networks to determine the optimal allocation path and supply quantity at each supply end based on the demand forecast data. Specifically, based on the actual pipeline distribution, a recursive decision is made from the demand side to calculate the allocation quantity for each pipeline node, while also taking pipeline transmission losses into account to ensure the resource allocation strategy's adaptability to the scenario.

[0064] In the process of allocating heating resources between the primary heating network and the secondary heating network, step S4 of the present application further includes:

[0065] Identify the demand forecast data, execute resource allocation on the heating station side under the secondary heating network, and determine the secondary heating network strategy, wherein the pipeline bifurcation node is used as a decision-making basis; identify the heating station margin, take the demand of the heating station as the target, and based on the proximity principle, perform resource allocation on the gas facility side under the primary heating network to determine the primary heating network strategy; integrate the primary heating network strategy with the secondary heating network strategy to generate the resource allocation strategy.

[0066] In the process of allocating heating resources, the demand forecast data is identified and based on this, a targeted analysis of the distribution status of the secondary heating network and the primary heating network is carried out.

[0067] Specifically, the secondary heating network, that is, from the heating station side to the demand side, has a complex pipeline distribution and many branches, so the allocation process decision is made in a multi-step recursive manner based on the demand side. At this time, the pipeline bifurcation node is used as the decision-making basis. The pipeline bifurcation node is usually an important intersection point for heat transmission, and the heat supply flow direction can be flexibly adjusted according to demand. For example, when the heat demand in a certain area suddenly increases, the system can respond quickly by adjusting the flow direction and distribution ratio of the pipeline to ensure that heat can be supplied to users with higher demand in a timely manner. In this way, the system can ensure the efficient distribution of heat in the secondary heating network, minimize heat loss, and improve user satisfaction.

[0068] Furthermore, by identifying the primary heating network strategy, the required heat supply of each heating station is determined. Simultaneously, the remaining energy reserves at the heating station are determined. This energy reserve refers to the energy available for real-time supply, in addition to backup energy. Based on the required heat supply of each heating station and its corresponding remaining energy reserves, the amount of energy to be dispatched to that station is determined, ensuring that its supply capacity matches user demand.

[0069] During this process, the demand from the heating station will be the target of resource allocation. Since the pipeline layout of the first heating network, from the gas facility to the heating station, is relatively simple, with few branches and mostly direct connections, a transmission path is determined based on the principle of proximity to minimize potential heat loss during transmission. This allocation strategy from the gas facility to the heating station will serve as the second heating network strategy.

[0070] Furthermore, the first heating network strategy and the second heating network strategy are integrated as the resource allocation strategy, so that the allocation of heating resources is optimized from demand to dynamic scheduling, maximizing the rationality of resource allocation and improving resource utilization.

[0071] In the implementation of the resource allocation at the heating station side under the secondary heating network, step S4 of the present application further includes:

[0072] Determine a first proximal node, wherein the first proximal node is a distributed pipeline bifurcation node close to the demand side; traverse the demand forecast data to determine the node resource requirements of the first proximal node; determine multiple levels of proximal nodes under the primary heating network, perform iterative recursion based on the node resource requirements, and determine a pipeline allocation plan as the primary heating network strategy.

[0073] Specifically, in the resource allocation process for the secondary heating network, the first proximal node refers to a distributed pipeline bifurcation node close to the demand side, typically serving as the direct source of user heating demand. The node resource requirements for each first proximal node are determined by summing the demands of users at each bifurcation end. Preferably, the first proximal node corresponding to each user is allocated based on proximity.

[0074] Furthermore, the distributed pipeline intersection node near the first proximal node is used as the second proximal node, and the node resource requirements are determined by superposition. The above steps are repeated recursively to determine the node resource requirements of multiple levels of proximal nodes, until the resource requirements of each heating station are determined. In summary, the heating system can ensure efficient heat distribution, minimize heat loss, and respond to user needs in a timely manner.

[0075] After determining the resource allocation strategy, step S4 of the present application further includes:

[0076] The resource allocation strategy is identified, and a pipeline transmission distance between each distribution end is determined; based on the pipeline transmission distance, a heat loss is determined; and based on the heat loss, the resource allocation strategy is compensated.

[0077] First, identify the resource allocation strategy, involving the resource flow of multi-level nodes between different heating stations and users, as well as the resource flow from the gas facility side to the heating station side, and determine a heating network view covering the entire area.

[0078] The pipe transfer distance between each distribution point is further determined. This distance refers to the length of pipe required to transport heat from one distribution point (such as a heating station or branch node) to another. The longer the transfer distance, the greater the potential for heat loss during transportation. Based on this pipe transfer distance, heat loss is further calculated.

[0079] Among them, heat loss is mainly affected by many factors, including: pipe material: pipes of different materials have differences in heat conduction; pipe diameter: the larger the diameter, the more heat may flow, but it may also increase heat dissipation; temperature difference: the temperature difference inside and outside the pipe directly affects heat loss, etc.

[0080] For example, combined with the heat conduction formula, based on parameters such as the transfer distance, pipeline characteristics and temperature difference, the heat loss of each section of the pipeline is obtained, and the resource allocation strategy is compensated to ensure that the end user can obtain the expected heat supply without being affected by the heat loss during the transportation process.

[0081] Exemplary compensation strategies might include: increasing heat supply: During resource allocation, appropriately increasing the heat supply to certain nodes to compensate for heat lost during transportation; optimizing pipeline layout: Targeting pipeline sections with high heat losses by reducing their length or adding insulation measures to fundamentally reduce heat losses. In summary, the heating system can ensure optimal configuration during heat distribution, minimize heat losses, and improve overall energy efficiency.

[0082] The heating resource allocation method based on global gas heating data analysis provided in this application has the following technical effects:

[0083] 1. By dividing the heat network based on the gas facility side to the heat station side and the heat station side to the demand side, targeted allocation process decisions can be made according to the distribution characteristics of the heat network pipelines to improve the effectiveness and adaptability of the decision.

[0084] 2. Introducing the gated loop principle, for dynamic scenarios, timely reset no longer relevant information and transmit relevant information to increase sensitivity to sudden changes in demand, improve the flexibility of analysis and the timeliness and accuracy of predictions, thereby improving the targetedness of predictions in short-term and long-term demand forecasts.

[0085] 3. Based on the pipeline distribution characteristics of the first and second heating networks, targeted dynamic allocation decisions are made. At the same time, combined with the actual transmission heat loss, strategic compensation correction is carried out to improve the accuracy of the resource allocation strategy.

[0086] Example 2

[0087] Based on the same inventive concept as the heating resource allocation method based on gas heating global data analysis in the aforementioned embodiment, Figure 2 As shown, the present application provides a heating resource allocation system based on global data analysis of gas heating, the system comprising:

[0088] A heat network determination module 11 is used to determine a primary heat network and a secondary heat network in the entire heat supply area, wherein the primary heat network is from the gas facility side to the heat power station side, and the secondary heat network is from the heat power station side to the demand side;

[0089] A model building module 12, wherein the model building module 12 is used to introduce a first gating unit and a second gating unit to build a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment;

[0090] The demand forecasting module 13 is used to obtain an input data set, perform data validation and heating demand decision-making in combination with the demand forecasting model, and determine demand forecast data, wherein the input data set includes real-time heating data, preset time zone heating data, and external factors;

[0091] The resource allocation module 14 is used to allocate heating resources between the primary heating network and the secondary heating network based on the demand forecast data and with the minimization of heat loss in the pipeline as a constraint, and to determine a resource allocation strategy.

[0092] Among them, the model construction module 12 is also used to perform the following steps: calling historical heating data and performing data cleaning and normalization, dividing and determining the first sample and the second sample with a mapping relationship; based on the first sample, by training the first reset function, constructing the first gating unit, wherein the first reset function is generated based on the supervised learning of the first sample; based on the second sample, by training the second transfer function, constructing the second gating unit, wherein the second transfer function is generated based on the supervised learning of the second sample; and parallelizing the first gating unit and the second gating unit to generate the demand forecasting model.

[0093] Among them, the demand forecasting module 13 is also used to perform the following steps: transmitting the input data set to the demand forecasting model; resetting the data and processing it based on the first gating unit, the second gating unit locates the transmission data and retains it, the units interact and perform demand forecasting, and determine the demand forecasting data.

[0094] Among them, the preset time zone heating data is determined by pushing forward the preset time zone based on the real-time moment node; when the output of the first reset function is closer to 0, the feature neglect degree of the preset time zone heating data is higher; when the output of the second transfer function is closer to 1, the feature transfer degree of the preset time zone heating data is higher.

[0095] Among them, the resource allocation module 14 is also used to perform the following steps: identifying the demand forecast data, executing the resource allocation on the thermal power station side under the secondary thermal network, and determining the secondary thermal network strategy, wherein the pipeline bifurcation node is used as the decision-making basis; identifying the thermal power station surplus, taking the demand of the thermal power station as the target, and based on the proximity principle, performing the resource allocation on the gas facility side under the primary thermal network, and determining the primary thermal network strategy; integrating the primary thermal network strategy with the secondary thermal network strategy to generate the resource allocation strategy.

[0096] Among them, the resource allocation module 14 is also used to perform the following steps: determine the first proximal node, wherein the first proximal node is a distributed pipeline bifurcation node close to the demand side; traverse the demand forecast data to determine the node resource requirements of the first proximal node; determine the multi-level proximal nodes under the primary heating network, perform iterative recursion based on the node resource requirements, and determine the pipeline allocation plan as the primary heating network strategy.

[0097] The resource allocation module 14 is further configured to perform the following steps: identifying the resource allocation strategy and determining the pipeline transmission distance between each distribution end; determining the heat loss based on the pipeline transmission distance; and compensating the resource allocation strategy based on the heat loss.

[0098] Example 3

[0099] Based on the same inventive concept as the heating resource allocation method based on global data analysis of gas heating in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method in embodiment one.

[0100] Through the detailed description of the heating resource allocation method based on global gas heating data analysis in this specification, those skilled in the art will clearly understand the heating resource allocation method and system based on global gas heating data analysis in this embodiment. Therefore, for the sake of brevity, a detailed description is not given here. As the device disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the description of the method.

[0101] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0102] refer to Figure 3 Describe an electronic device according to an embodiment of the present application. Based on the same inventive concept as the method for allocating heating resources based on global gas heating data analysis in the aforementioned embodiment, the present application further provides an electronic device comprising: a processor coupled to a memory, the memory being configured to store a program, which, when executed by the processor, causes the system to perform the steps of the method described in Example 1.

[0103] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may further include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0104] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0105] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0106] The memory 301 can be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor via the bus architecture 304. The memory can also be integrated with the processor.

[0107] Memory 301 is used to store computer-executable instructions for executing the solution of the present application, and is controlled by processor 302. Processor 302 is used to execute the computer-executable instructions stored in memory 301, thereby implementing the heating resource allocation method based on gas heating global data analysis provided in the above embodiment of the present application.

[0108] While the present application has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations thereof can be made without departing from the spirit and scope of the present application.

[0109] Accordingly, this specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, to the extent such modifications and variations fall within the scope of the present application and its equivalents, the present application is intended to include such modifications and variations.

Claims

1. A heating resource allocation method based on global gas heating data analysis is characterized by: The method comprises: Determine the primary heating network and secondary heating network for the entire heating area, where the primary heating network is from the gas facility side to the heating station side, and the secondary heating network is from the heating station side to the demand side; A first gating unit and a second gating unit are introduced to construct a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment; Obtaining an input data set, performing data validation and heating demand decision-making in combination with the demand forecasting model, and determining demand forecast data, wherein the input data set includes real-time heating data, heating data in a preset time zone, and external factors, and the real-time heating data includes real-time heat demand of users, flow information of the pipe network, and temperature information of the pipe network; Based on the demand forecast data, and subject to the constraint of minimizing heat loss in pipelines, heat supply resources are allocated between the primary heat network and the secondary heat network, and a resource allocation strategy is determined; The constructing of the demand forecasting model includes: Call historical heating data and perform data cleaning and normalization to divide and determine the first sample and the second sample with a mapping relationship; Based on the first sample, constructing the first gating unit by training a first reset function, wherein the first reset function is generated based on supervised learning of the first sample; Based on the second sample, constructing the second gating unit by training a second transfer function, wherein the second transfer function is generated based on supervised learning of the second sample; The first gating unit and the second gating unit are operated in parallel to generate the demand forecasting model, wherein the closer the output of the first reset function is to 0, the higher the degree of neglect of the characteristic of the heating data in the preset time zone is; and the closer the output of the second transfer function is to 1, the higher the degree of transfer of the characteristic of the heating data in the preset time zone is. Allocating heating resources between the primary heating network and the secondary heating network includes: Identifying the demand forecast data, executing the heat station-side resource allocation under the secondary heat network, and determining the secondary heat network strategy, wherein the pipeline bifurcation node is used as a decision basis; Identify the surplus capacity of the heating station, allocate resources to the gas facilities under the primary heating network based on the principle of proximity, and determine the primary heating network strategy based on the demand of the heating station; fusing the primary heating network strategy and the secondary heating network strategy to generate the resource allocation strategy; Executing the resource allocation at the heating station side under the secondary heating network includes: Determining a first proximal node, wherein the first proximal node is a distributed pipeline bifurcation node close to the demand side; Traversing the demand forecast data to determine the node resource demand of the first proximal node; Determine multiple levels of proximal nodes under the primary heating network, perform iterative recursion based on the node resource requirements, and determine a pipeline allocation plan as the primary heating network strategy.

2. The method for allocating heating resources based on global gas heating data analysis according to claim 1, characterized in that: The determining of demand forecast data includes: transmitting the input data set to the demand forecasting model; Based on the first gating unit resetting data and processing, the second gating unit positioning transfer data and retaining it, the units interact and perform demand forecasting to determine the demand forecast data.

3. The method for allocating heating resources based on global gas heating data analysis according to claim 2, characterized in that: The preset time zone heating data is determined by pushing forward the preset time zone at the real-time moment node.

4. The method for allocating heating resources based on global gas heating data analysis according to claim 1, characterized in that: After determining the resource allocation strategy, including: Identifying the resource allocation strategy and determining the pipeline transmission distance between each allocation end; determining heat loss based on the pipeline transfer distance; The resource allocation strategy is compensated based on the heat loss.

5. The heating resource allocation system based on gas heating global data analysis is characterized by: The system is used to execute the heating resource allocation method based on gas heating global data analysis according to any one of claims 1 to 4, and the system includes: A heat network determination module, which is used to determine the primary heat network and the secondary heat network in the entire heat supply area, wherein the primary heat network is from the gas facility side to the heat power station side, and the secondary heat network is from the heat power station side to the demand side; a model construction module, the model construction module being used to introduce a first gating unit and a second gating unit to construct a demand forecasting model, wherein the first gating unit is used to control the flow of reset information at the previous moment, and the second gating unit is used to control the flow of transmission information at the previous moment; a demand forecasting module, the demand forecasting module being configured to obtain an input data set, perform data validation and heating demand decision-making in conjunction with the demand forecasting model, and determine demand forecast data, wherein the input data set includes real-time heating data, heating data in a preset time zone, and external factors, the real-time heating data including real-time heat demand of users, flow information of the pipe network, and temperature information of the pipe network; a resource allocation module configured to allocate heating resources between the primary heating network and the secondary heating network based on the demand forecast data and subject to a constraint of minimizing heat loss in pipelines, and to determine a resource allocation strategy; The constructing of the demand forecasting model includes: Call historical heating data and perform data cleaning and normalization to divide and determine the first sample and the second sample with a mapping relationship; Based on the first sample, constructing the first gating unit by training a first reset function, wherein the first reset function is generated based on supervised learning of the first sample; Based on the second sample, constructing the second gating unit by training a second transfer function, wherein the second transfer function is generated based on supervised learning of the second sample; The first gating unit and the second gating unit are operated in parallel to generate the demand forecasting model, wherein the closer the output of the first reset function is to 0, the higher the degree of neglect of the characteristic of the heating data in the preset time zone is; and the closer the output of the second transfer function is to 1, the higher the degree of transfer of the characteristic of the heating data in the preset time zone is. Allocating heating resources between the primary heating network and the secondary heating network includes: Identifying the demand forecast data, executing the heat station-side resource allocation under the secondary heat network, and determining the secondary heat network strategy, wherein the pipeline bifurcation node is used as a decision basis; Identify the surplus capacity of the heating station, allocate resources to the gas facilities under the primary heating network based on the principle of proximity, and determine the primary heating network strategy based on the demand of the heating station; fusing the primary heating network strategy and the secondary heating network strategy to generate the resource allocation strategy; Executing the resource allocation at the heating station side under the secondary heating network includes: Determining a first proximal node, wherein the first proximal node is a distributed pipeline bifurcation node close to the demand side; Traversing the demand forecast data to determine the node resource demand of the first proximal node; Determine multiple levels of proximal nodes under the primary heating network, perform iterative recursion based on the node resource requirements, and determine a pipeline allocation plan as the primary heating network strategy.

6. An electronic device, characterized in that: include: A processor is coupled to a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the system is enabled to perform the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which implements the method steps according to any one of claims 1 to 4 when executed by a processor.

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