Heating Data Processing Method, Device and Storage Medium for Smart City

Abstract

The present invention relates to the technical field of heating data processing, and in particular to a heating data processing method, device and storage medium for a smart city, including: collecting the spatial building topographic map of the target area of the city, and obtaining the soil data and underground pipe network data of the target area of the city in real time; using the collection points of the soil data in the target area of the city as regional sample points, and setting the heating temperature gradient of each regional sample point, thereby constructing a planar heating demand map, collecting the surface meteorological data of each regional sample point in real time, and updating the planar heating demand map, analyzing the vertical heat gradient coefficient of each spatial building, and generating a vertical heating demand set for each spatial building, and integrating the planar heating demand map and the vertical heating demand sets of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map. The present invention effectively improves the processing efficiency of heating data.

Description

Technical Field

[0001] The present invention relates to the technical field of heating data processing, and in particular, to a heating data processing method, device, and storage medium for a smart city. Background Art

[0002] Currently, the heating systems in smart cities mostly rely on static and planar heat distribution models, making it difficult to adapt to the dynamic demands in complex urban environments. Existing technologies usually use fixed temperature thresholds or single meteorological parameters for heating regulation, lacking comprehensive consideration of key factors such as soil thermal properties, heat attenuation in underground pipe networks, and vertical building heat gradients.

[0003] Chinese Patent Publication No. CN118296558A discloses a heating data fusion method, device, and system based on big data processing. The method includes the following steps: collecting basic heating data; constructing a heating map from the basic heating data and the corresponding heating location data of the basic heating data to obtain heating map data; extracting graph features from the heating map data to obtain heating graph feature data, and extracting time-series relationship features from the basic heating data to obtain heating time-series relationship data; performing first data fusion on the basic heating data according to the heating graph feature data to obtain first heating fusion data, and performing second data fusion on the basic heating data according to the heating time-series relationship data to obtain second heating fusion data; deeply fusing the first heating fusion data and the second heating fusion data to obtain heating deep fusion data. The present invention excavates the value of heating data by fusing basic heating data in different dimensions; thus, it can be seen that the invention does not set specific heating weights in both the horizontal and vertical directions, resulting in low efficiency in processing heating data. Summary of the Invention

[0004] The purpose of the present invention is to provide a heating data processing method, device, and storage medium for a smart city to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, according to one aspect of the present application, the present invention provides a heating data processing method for a smart city, including:

[0006] Taking the collection points of soil data in the target urban area as regional sample points, setting the heating temperature gradient of each regional sample point based on the soil data and underground pipe network data, and then constructing a planar heating demand map based on the temperature gradient index settings of each regional sample point;

[0007] Real-time collecting the surface meteorological data of each regional sample point, analyzing the meteorological risk status of each regional sample point, and then updating the planar heating demand map according to the meteorological risk status;

[0008] Based on the spatial building topographic map of the urban target area, analyze the vertical heat gradient coefficients of each spatial building, and generate the vertical heating demand set of each spatial building according to the construction result of the plane heating demand map.

[0009] Optionally, it further includes: collecting the spatial building topographic map of the urban target area, and obtaining the soil data and underground pipeline network data of the urban target area in real time;

[0010] Calculate the heat loss index α(i) of each regional sample point respectively, calibrate the heating temperature of each pipe section of the underground pipeline network according to the heat loss index of each regional sample point, set the calibrated heating temperature of each pipe section as Tc(i), and when the difference between the calibrated heating temperature of each pipe section and the set heating temperature is greater than the warning temperature, give a pipeline leakage alarm to the user.

[0011] Optionally, set the heating temperature gradient of each regional sample point according to the calibrated heating temperature;

[0012] Calculate the loss ratio of each pipe section, and set the loss ratio as the ratio of the calibrated heating temperature of each pipe section lower than the set heating temperature;

[0013] Calculate the heating temperature gradient of each regional sample point with the calibrated heating temperature and loss ratio of each pipe section: take the temperature drop of the underground pipeline network in this pipe section as the first gradient component with the heating temperature gradient, and take the loss ratio of the pipe section as the second gradient component.

[0014] Optionally, calculate the actual radiation area radius S(i) of each regional sample point;

[0015] Calculate the heating demand index α(i) of each regional sample point;

[0016] Divide the spatial building topographic map of the urban target area with the actual radiation area radius S(i) of each regional sample point to obtain each divided area, and set the heating demand index of the regional sample point as the heating demand index of this divided area;

[0017] Combine the heating demand indexes of each divided area into a plane heating demand map.

[0018] Optionally, collect the surface meteorological data of each regional sample point in real time;

[0019] Set the meteorological risk index β(i) of each regional sample point according to the local meteorological wind speed, meteorological precipitation and local temperature difference;

[0020] When the meteorological risk index of the sample points in the i-th region is less than the risk threshold, the meteorological risk status is determined to be normal, and the current planar heating demand map is not updated; when the meteorological risk index of the sample points in the i-th region is greater than or equal to the risk threshold, the meteorological risk status is determined to be a cooling risk, and the calculation process of the heating demand index of the sample points in the i-th region is updated, and the updated heating demand index of the sample points in the i-th region is set to αb(i).

[0021] Optionally, collect the groundwater level data of the sample points in each region;

[0022] Construct the groundwater-temperature coupling index of the sample points in each region based on the groundwater level data;

[0023] When the meteorological risk status is a cooling risk, if the groundwater-temperature coupling index of the sample points in the i-th region is less than the preset coupling value, the meteorological risk status is adjusted to a multi-source cooling risk, and at the same time, the meteorological risk index is adjusted to βt(i); if the groundwater-temperature coupling index of the sample points in the i-th region is greater than or equal to the preset coupling value, the update result of the planar heating demand map remains unchanged.

[0024] Optionally, divide each spatial building according to the spatial building topographic map of the urban target area;

[0025] Obtain the heat distribution data of each spatial building in real time, and construct the vertical heat gradient coefficient of each spatial building based on the heat distribution data of each spatial building.

[0026] Optionally, use the product of the heating demand index of the division area where each spatial building is located and the vertical heat gradient coefficient of each floor in the j-th spatial building as the vertical heating demand set of the j-th spatial building;

[0027] Based on the spatial building topographic map of the urban target area, integrate the planar heating demand map and the vertical heating demand sets of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map, and output the heating demand distribution map to the user.

[0028] According to another aspect of the present application, there is provided a heating data processing method device for a smart city, including:

[0029] A data acquisition module for: collecting the spatial building topographic map of the urban target area, and obtaining the soil data and underground pipe network data of the urban target area in real time;

[0030] A planar demand construction module for: using the collection points of the soil data in the urban target area as regional sample points, setting the heating temperature gradient of each regional sample point based on the soil data and the underground pipe network data, and then constructing a planar heating demand map based on the temperature gradient index of each regional sample point;

[0031] A demand update module, configured to: collect in real time the surface meteorological data of each regional sample point, analyze the meteorological risk status of each regional sample point, and then update the planar heating demand map according to the meteorological risk status;

[0032] A vertical demand construction module, configured to: based on the spatial building topographic map of the urban target area, analyze the vertical heat gradient coefficient of each spatial building, and generate the vertical heating demand set of each spatial building according to the construction result of the planar heating demand map;

[0033] A data output module, configured to: based on the spatial building topographic map of the urban target area, integrate the planar heating demand map and the vertical heating demand sets of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map, and output the heating demand distribution map to the user.

[0034] According to another aspect of the present application, there is provided a computer-readable storage medium storing a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the heating data processing method for a smart city when running.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: This solution breaks through the limitations of the traditional heating system being static and planar. Through dynamic weight calibration, vertical heat gradient modeling, and three-dimensional visualization technology, a smart heating system integrating "ground-air-building" is constructed, providing an efficient, low-carbon, and sustainable innovative solution for high-density urban energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is a schematic flowchart of the heating data processing method for a smart city in this embodiment.

[0038] Figure 2 It is a schematic flowchart of the method for constructing the planar heating demand map in this embodiment.

[0039] Figure 3 It is a schematic flowchart of the method for updating the planar heating demand map in this embodiment.

[0040] Figure 4 It is a schematic flowchart of the method for generating the vertical heating demand set in this embodiment.

[0041] Figure 5 It is a schematic structural diagram of the electronic device provided in this embodiment.

[0042] Figure 6 It is a schematic structural diagram of the heating data processing device for a smart city provided in this embodiment. Detailed implementation manners

[0043] To describe the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0044] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0045] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0046] Specifically, a heating data processing method, device, and storage medium for a smart city described in the present application are applied to: heating optimization based on the thermal dynamic characteristics of underground pipe networks in the city; the heating data processing for a smart city in the present application involves the distribution processing of planar heat source data, the distribution processing of vertical heat source data for high-density building groups, and data correction based on soil data and pipe network data.

[0047] Based on the above application scenarios, please refer to Figure 1 As shown, it is a flowchart of a heating data processing method for a smart city provided by the present application, including:

[0048] Step S101, collect the spatial building topographic map of the urban target area, and obtain the soil data and underground pipe network data of the urban target area in real time;

[0049] The spatial building topographic map is a scene map in a three-dimensional space. Its acquisition process can be carried out by the urban planning and management department, or can be modeled and collected through photography and three-dimensional modeling. Its acquisition process is an existing public technology, and the present application will not elaborate on it;

[0050] The soil data of the urban target area are the soil parameters around the underground pipe networks in the urban target area, including: soil temperature and soil thermal lag coefficient. The soil thermal lag coefficient is the soil thermal conductivity in this embodiment;

[0051] The underground pipe network data is the relevant data of the underground heating pipe network, including: the distribution plan of the underground pipe network and the parameters of the underground pipe network; the parameters of the underground pipe network include the set heating temperature, the cross-sectional area of the pipe section, the flow velocity of the pipe section, and the heating area

[0052] It should be noted that the solution provided in this application acts on the urban target area, and the setting of the urban target area is determined by the user himself, which can be a certain area of the city or the whole city, and is determined according to the user's needs

[0053] Exemplarily, for the convenience of soil data collection and improving the pertinence of soil data, the best process for collecting soil data is to set sensors on the heating pipe network for collection, and the collection points are set at intervals of 50 meters

[0054] Please continue to refer to Figure 1 As shown, the heating data processing method for a smart city described in this application further includes:

[0055] Step S102: Use the collection points of the soil data in the urban target area as regional sample points, set the heating temperature gradient of each regional sample point according to the soil data and the underground pipe network data, and then construct a planar heating demand map based on the temperature gradient index setting of each regional sample point

[0056] To accurately construct the temperature gradient of each regional sample point, before constructing the temperature gradient of each regional sample point, it is necessary to execute the planar heating demand map construction method as shown in this application Figure 2 including:

[0057] Step S201: Judge the heat loss index in real time according to the soil data and the underground pipe network data, and calibrate the heating temperature with the heat loss index

[0058] Specifically, the process of calibrating the heating temperature with the heat loss index is as follows:

[0059] Calculate the heat loss index Rs(i) of each regional sample point respectively, and set Rs(i)=1 + Ssoil(i) / Smax×ln[STLC(i)];

[0060] Calibrate the heating temperature of each pipe section of the underground pipe network according to the heat loss index of each regional sample point, set the calibrated heating temperature of each pipe section as Tc(i), and when the difference between the calibrated heating temperature of each pipe section and the set heating temperature is greater than the warning temperature, alarm the user about pipeline leakage;

[0061] Set Tc(i)=T(i)-Traw(i)×C(i);

[0062] Wherein, Tc(i) is the calibrated heating temperature at the i-th sample point, T(i) is the set heating temperature before calibration at the i-th sample point, Traw(i) is the soil temperature of the i-th sample, C(i) is the heat and humidity compensation coefficient at the i-th sample point, Ssoil(i) is the absolute soil humidity at the i-th sample point, Smax is the set humidity threshold, STLC(i) is the soil thermal hysteresis coefficient at the i-th sample point, and i is a numerical subscript representing the serial number of the sample point, where i ∈ N + 。

[0063] Exemplarily, in the present application, the division of each pipe section of the underground pipe network is carried out based on the soil data collection points; meanwhile, those skilled in the art can freely set the value of the warning temperature, as long as it meets the value requirements. In this embodiment, the optimal value of the warning temperature can be 70% of the set heating temperature of the sample points in this area.

[0064] After completing the calibration of the heating data, please continue to refer to Figure 2 As shown, the method for constructing the planar heating demand map further includes:

[0065] Step S202: Set the heating temperature gradient of each regional sample point according to the calibrated heating temperature.

[0066] Specifically, the process of setting the heating temperature gradient in the present application is as follows:

[0067] Calculate the loss ratio of each pipe section, and set the loss ratio as the ratio of the heating temperature of each pipe section after calibration being lower than the set heating temperature;

[0068] Calculate the heating temperature gradient of each regional sample point based on the heating temperature of each pipe section after calibration and the loss ratio: take the temperature drop of the underground pipe network in this pipe section as the first gradient component of the heating temperature gradient, and take the loss ratio of the pipe section as the second gradient component.

[0069] In step S202, the gradient components are constructed with the temperature drop value and the loss ratio, which can accurately reflect the heat attenuation law of the pipe network and provide a quantitative basis for dynamic temperature adjustment.

[0070] Please continue to refer to Figure 2 As shown, the method for constructing the planar heating demand map further includes:

[0071] Step S203: Construct a planar heating demand map of the spatial building topographic map of the urban target area with the heating temperature gradient.

[0072] Specifically, the process of constructing the planar heating demand map is as follows:

[0073] Calculate the actual radiation area radius S(i) of each regional sample point, and set S(i) = v(i) × mc(i) / SM(i);

[0074] Wherein, mc(i) represents the cross-sectional area of the pipe section corresponding to the sample point in the i-th area, v(i) represents the flow velocity of the pipe section corresponding to the sample point in the i-th area, and SM(i) represents the heating area of the pipe section corresponding to the sample point in the i-th area;

[0075] Calculate the heating demand index α(i) of each area sample point, and set α(i) = |temperature gradient of the i-th area sample point|;

[0076] Divide the spatial building topographic map of the urban target area by the actual radiation area radius S(i) of each area sample point to obtain each divided area, and set the heating demand index of the area sample point as the heating demand index of the divided area;

[0077] Combine the heating demand indexes of each divided area into a planar heating demand map.

[0078] In the step S203, by constructing a planar heating demand map, and dividing the city into different areas through the actual radiation area and the heating demand index, it helps to allocate heat targeted, avoid overall overheating or insufficiency, and improve the heating efficiency.

[0079] It should be noted that in the calculation formula of α(i) in this application, "||" is the modulus of the gradient vector; at the same time, in this application, if there is an overlapping part between the actual radiation area of a certain area sample point and the actual radiation area of another area sample point, then retain the actual radiation area of the area sample point with a higher heating demand index.

[0080] Please continue to refer to Figure 1 As shown, the heating data processing method for a smart city further includes:

[0081] Step S103, collect the surface meteorological data of each area sample point in real time, analyze the meteorological risk status of each area sample point, and then update the planar heating demand map according to the meteorological risk status.

[0082] Please continue to refer to Figure 3 As shown, it is a schematic flow chart of the method for updating the planar heating demand map described in this embodiment, including:

[0083] Step S301, collect the surface meteorological data of each area sample point in real time; the meteorological data includes local meteorological wind speed, meteorological precipitation, and local temperature difference; in this application, the meteorological data is obtained in real time through a meteorological monitoring website; by introducing a multi-parameter meteorological model, the response ability of the system to sudden weather is enhanced, and the heating imbalance caused by environmental mutations is avoided;

[0084] Step S302: Analyze the meteorological risk status of each regional sample point based on the surface meteorological data of each regional sample point, and then update the planar heating demand map according to the meteorological risk status.

[0085] Specifically, the process of updating the planar demand map is as follows:

[0086] Set the meteorological risk index β(i) of each regional sample point according to the local meteorological wind speed, meteorological precipitation, and local temperature difference. Set β(i) = ln{1 + (f(i) - F) / F × (△T(i) - t) / t × (js - JS) / JS}; where f(i) is the local meteorological wind speed of the i-th regional sample point, △T(i) is the local temperature difference of the i-th regional sample point, js is the meteorological precipitation, F is the wind speed threshold, which is 5m / s in this application, t is the preset local temperature difference, with a value of 3°C, JS is the preset precipitation, with a value of 3ml, and the local temperature difference is the temperature difference of the surface area where the i-th regional sample point is located within a fixed period of time, and the length of the fixed time is 30min in this application; the local temperature difference is the temperature before the fixed time minus the temperature after the fixed time.

[0087] When the meteorological risk index of the i-th regional sample point is less than the risk threshold, determine that the meteorological risk status is normal and do not update the current planar heating demand map; when the meteorological risk index of the i-th regional sample point is greater than or equal to the risk threshold, determine that the meteorological risk status is a cooling risk, and update the calculation process of the heating demand index of the i-th regional sample point. Set the updated heating demand index of the i-th regional sample point as αb(i), and set αb(i) = α(i) / β(i).

[0088] It can be understood that in this application, no specific limitation is made on the value of the risk threshold, and those skilled in the art can freely set it as long as it meets the value requirements of the risk threshold. In this application, taking a temperature-sensitive city as an example, the risk threshold can be set to 40%.

[0089] Update the heating demand map according to the meteorological data, monitor the wind speed, precipitation, and temperature difference in real time, adjust the heating demand in a timely manner, respond to sudden weather changes, ensure the stable operation of the heating system, and avoid heating insufficiency or energy waste caused by sudden weather changes.

[0090] Please continue to refer to Figure 3 As shown, the method for updating the planar heating demand map further includes:

[0091] Step S303: Collect the groundwater level data of each regional sample point, and adjust the update result of the planar heating demand map according to the collection result.

[0092] Specifically, the process of adjusting the update result of the planar heating demand map is as follows:

[0093] Construct the groundwater-temperature coupling index γ(i) of each regional sample point based on the groundwater level data, and set γ(i)=Tc(i)×exp{-[sw(i)-YS] / YS}; where YS is the preset groundwater level, and sw(i) is the groundwater level of the i-th regional sample point.

[0094] When the meteorological risk status is a cooling risk, if the groundwater-temperature coupling index of the i-th regional sample point is less than the preset coupling value, adjust the meteorological risk status to a multi-source cooling risk. At the same time, adjust the meteorological risk index to βt(i), and set βt(i)=β(i)×exp{1 / [Tc(i)-γ(i)]}; if the groundwater-temperature coupling index of the i-th regional sample point is greater than or equal to the preset coupling value, keep the update result of the plane heating demand map unchanged.

[0095] Specifically, the specific value of the preset coupling value in this application is Tc(i)×60%; introduce the groundwater level data to adjust the update result. The combination of the groundwater and temperature coupling index can more comprehensively evaluate the cooling risk, especially in the case of multi-source cooling risk, provide a more accurate regulation basis, and enhance the system's ability to cope with complex environments.

[0096] Please continue to refer to Figure 1 As shown, the heating data processing method for a smart city further includes:

[0097] Step S104, based on the spatial building topographic map of the urban target area, analyze the vertical heat gradient coefficient of each spatial building, and generate the vertical heating demand set of each spatial building according to the construction result of the plane heating demand map.

[0098] Please refer to Figure 4 As shown, it is a schematic flow chart of the method for generating the vertical heating demand set described in this embodiment, including:

[0099] Step S401, divide each spatial building according to the spatial building topographic map of the urban target area; calculate the heat gradient by combining the floor height and the population density to solve the problem of thermal stratification in high-rise buildings and improve the refinement level of the heating system.

[0100] Exemplarily, the spatial building in this application specifically refers to a high-rise building, and the high-rise building in this application refers to a building with more than 6 floors; it can be understood that the vertical heating demand set of the high-rise building in this application is the plane demand index at the location of the non-high-rise building in the plane heating demand map.

[0101] Please continue to refer to Figure 4 As shown, the method for generating the vertical heating demand set further includes:

[0102] Step S402: Obtain the thermal distribution data of each space building in real time, and construct the vertical thermal gradient coefficient of each space building based on the thermal distribution data of each space building; the thermal distribution data of each space building includes building floor height, building floor population density, and temperature difference between adjacent floors.

[0103] Specifically, the process of constructing the vertical thermal gradient coefficient of each space building is as follows:

[0104] Set the vertical heat loss coefficient VIF(j,k) for each floor in each space building, and set VIF(j,k) = 0.6×H(j,k) + 0.4×ρ(j,k); in the formula, j and k are both digital subscripts.

[0105] In the formula, H(j,k) represents the height of the k-th floor in the j-th space building, and ρ(j,k) represents the population density of the k-th floor in the j-th space building.

[0106] Set the vertical thermal gradient coefficient FHP(j,k) for each floor in each space building, and set .

[0107] Construct the vertical thermal gradient coefficient. Utilize the thermal distribution data of the building (such as floor height, population density) to dynamically adjust the heating demand of each floor, ensure reasonable heat distribution for different floors of high-rise buildings, and improve the refined management level of the heating system.

[0108] Please continue to refer to Figure 4 As shown, the method for generating the vertical heating demand set further includes:

[0109] Step S403: Generate the vertical heating demand set of each space building based on the construction result of the plane heating demand map and the vertical thermal gradient coefficient of each space building; through the multi-dimensional superposition of the space topographic map and heating data, realize the global visualization of heating demand, and support the digital upgrade of smart city energy management.

[0110] Specifically, use the product of the heating demand index of the division area where each space building is located and the vertical thermal gradient coefficient of each floor in the j-th space building as the vertical heating demand set of the j-th space building.

[0111] Please continue to refer to Figure 1 As shown, the heating data processing method for smart cities further includes:

[0112] Step S105: Based on the spatial building topographic map of the urban target area, integrate the planar heating demand map and the vertical heating demand sets of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map, and output the heating demand distribution map to the user; integrating the planar heating demand map and the vertical demand sets into a three-dimensional distribution map can intuitively display the spatial differences in urban heating demand. This step supports precise heating regulation, provides a decision-making basis for operation and maintenance personnel, optimizes heat source allocation, reduces redundant pipeline loads, and extends the equipment life.

[0113] Exemplarily, in the present application, the process of "integrating the planar heating demand map and the vertical heating demand sets of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map" is to set heating demand indexes for all buildings in the spatial topographic map, set vertical heating demand sets for spatial buildings, and assign heating demand indexes to non-spatial buildings according to the planar heating demand map.

[0114] The heating data processing device for a smart city provided by the embodiments of the present application can execute the heating data processing method for a smart city provided by any embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method.

[0115] From the hardware level, for the functional implementation of the heating data processing method for a smart city in a computer, the present application also provides a computer-readable storage medium. Please refer to Figure 5 as shown, which is disposed in an electronic device. The electronic device includes:

[0116] A processing unit 1, a storage unit 2, a data port 3, and a bus 4; wherein, the processing unit 1 and the storage unit 2 perform data transmission through the bus 4, the data port 3 performs data transmission with the processing unit 1 through the bus 4, and the data port is used for receiving and sending data; the storage unit is jointly composed of one or more computer-readable media.

[0117] The physical implementation manner of the electronic device and the storage method of the storage medium described in the present application are all well-known prior arts to those skilled in the art (for example, through PCI circuit design, program burning, etc.), and the present application will not elaborate herein.

[0118] Exemplarily, the internal combustion engine generator set control method based on machine learning can be implemented as a computer program, and the computer program can be physically stored in the above computer-readable storage medium; when the computer program is loaded into the storage unit 2 and executed by the processing unit 1, one or more steps of the above internal combustion engine generator set control method based on machine learning can be executed.

[0119] In this application, the computer-readable storage medium is a tangible physical storage medium, which can store the above-mentioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations of media such as random storage units, read-only storage units, optical discs, hard disks, etc.

[0120] Please continue to refer to Figure 6 As shown, it is a schematic structural diagram of a heating data processing device for a smart city according to this embodiment, including:

[0121] A data acquisition module, configured to: collect the topographic map of spatial buildings in the target urban area, and obtain the soil data and underground pipeline network data of the target urban area in real time;

[0122] A planar demand construction module, configured to: use the collection points of the soil data in the target urban area as regional sample points, and set the heating temperature gradient of each regional sample point according to the soil data and the underground pipeline network data, and then construct a planar heating demand map based on the temperature gradient index settings of each regional sample point;

[0123] A demand update module, configured to: collect the surface meteorological data of each regional sample point in real time, analyze the meteorological risk status of each regional sample point, and then update the planar heating demand map according to the meteorological risk status;

[0124] A vertical demand construction module, configured to: analyze the vertical heat gradient coefficient of each spatial building based on the topographic map of spatial buildings in the target urban area, and generate a vertical heating demand set for each spatial building according to the construction result of the planar heating demand map;

[0125] A data output module, configured to: based on the topographic map of spatial buildings in the target urban area, integrate the planar heating demand map and the vertical heating demand set of each spatial building into the topographic map of spatial buildings to form a three-dimensional heating demand distribution map, and output the heating demand distribution map to the user.

[0126] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A heating data processing method for a smart city, characterized in that: include: The soil data collection points in the urban target area are used as regional sample points, and the heating temperature gradient of each regional sample point is set according to the soil data and underground pipe network data, and then the plane heating demand map is constructed based on the temperature gradient index of each regional sample point; Collect surface meteorological data of sample points in each region in real time, analyze the meteorological risk status of sample points in each region, and then update the planar heating demand map based on the meteorological risk status; Based on the spatial building topographic map of the urban target area, the vertical thermal gradient coefficient of each spatial building is analyzed, and the vertical heating demand set of each spatial building is generated according to the construction result of the plane heating demand map; Collect surface meteorological data of sample points in each region in real time; The meteorological risk index β(i) of each regional sample point is set according to the local meteorological wind speed, meteorological precipitation and local temperature difference; When the meteorological risk index of the sample point in the i-th area is less than the risk threshold, the meteorological risk state is determined to be normal, and the current planar heating demand map is not updated; When the meteorological risk index of the sample point in the i-th region is greater than or equal to the risk threshold, the meteorological risk state is determined to be a cooling risk, and the calculation process of the heating demand index of the sample point in the i-th region is updated, and the updated heating demand index of the sample point in the i-th region is set to αb(i); Collect groundwater level data at sample points in each area; The groundwater-temperature coupling index of each regional sample point was constructed based on the groundwater level data; When the meteorological risk state is cooling risk, if the groundwater-temperature coupling index of the sample point in the i-th region is less than the preset coupling value, the meteorological risk state is adjusted to multi-source cooling risk, and at the same time, the meteorological risk index is adjusted to βt(i); If the groundwater-temperature coupling index of the sample point in the i-th region is greater than or equal to the preset coupling value, the update result of the planar heating demand map remains unchanged.

2. The heating data processing method for smart city according to claim 1, characterized in that: Also includes: Collect spatial building topographic maps of urban target areas, and obtain soil data and underground pipe network data of urban target areas in real time; The heat loss index α(i) of each regional sample point is calculated respectively, and the heating temperature of each pipe section of the underground pipeline network is calibrated according to the heat loss index of each regional sample point. The calibrated heating temperature of each pipe section is set as Tc(i), and when the difference between the calibrated heating temperature of each pipe section and the set heating temperature is greater than the warning temperature, a pipeline leakage alarm is issued to the user.

3. The heating data processing method for smart city according to claim 2 is characterized in that: The heating temperature gradient of each area sample point is set according to the calibrated heating temperature; Calculate the loss ratio of each pipe section, and set the loss ratio as the ratio of the heating temperature of each pipe section after calibration to be lower than the set heating temperature; The heating temperature gradient of each regional sample point is calculated based on the heating temperature and loss ratio of each pipe section after calibration: the heating temperature gradient is the temperature drop of the underground pipeline network in this pipe section as the first gradient component, and the loss ratio of the pipe section is the second gradient component.

4. The heating data processing method for smart city according to claim 3 is characterized in that: Calculate the actual radiation area radius S(i) of each regional sample point; Calculate the heating demand index α(i) of each regional sample point; The spatial building topographic map of the urban target area is divided according to the actual radiation area radius S(i) of the sample points in each area to obtain each divided area, and the heating demand index of the sample points in the area is set as the heating demand index of the divided area; The heating demand index of each divided area is combined into a planar heating demand map.

5. The heating data processing method for smart city according to claim 4 is characterized in that: According to the spatial building topographic map of the urban target area, the various spatial buildings are divided; The thermal distribution data of each space building is obtained in real time, and the vertical thermal gradient coefficient of each space building is constructed based on the thermal distribution data of each space building.

6. The heating data processing method for smart city according to claim 5, characterized in that: The vertical heating demand set of the j-th space building is taken as the product of the heating demand index of the divided area where each space building is located and the vertical thermal gradient coefficient of each floor in the j-th space building; Based on the spatial building topographic map of the target urban area, the planar heating demand map and the vertical heating demand set of each spatial building are integrated into the spatial building topographic map to form a three-dimensional heating demand distribution map, and the heating demand distribution map is output to the user.

7. A heating data processing device for a smart city, characterized in that: include: The data acquisition module is used to collect the spatial building topographic map of the urban target area and obtain the soil data and underground pipe network data of the urban target area in real time; The plane demand construction module is used to: use the soil data collection points in the urban target area as regional sample points, and set the heating temperature gradient of each regional sample point based on the soil data and underground pipe network data, and then set and construct a plane heating demand map based on the temperature gradient index of each regional sample point; The demand update module is used to collect the surface meteorological data of the sample points in each area in real time, analyze the meteorological risk status of the sample points in each area, and then update the plane heating demand map according to the meteorological risk status; The vertical demand construction module is used to: analyze the vertical thermal gradient coefficient of each spatial building based on the spatial building topographic map of the urban target area, and generate the vertical heating demand set of each spatial building according to the construction result of the plane heating demand map; The data output module is used to: based on the spatial building topographic map of the target area of ​​the city, integrate the planar heating demand map and the vertical heating demand set of each spatial building into the spatial building topographic map to form a three-dimensional heating demand distribution map, and output the heating demand distribution map to the user.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the heating data processing method for a smart city according to any one of claims 1 to 6 during operation.

Images