Heat supply data processing method and device for smart city and storage medium

Abstract

The invention relates to the technical field of heat supply data processing, in particular to a heat supply data processing method and device for a smart city and a storage medium, and the method comprises the steps: collecting a spatial building topographic map of a city target area, and obtaining the soil data and underground pipe network data of the city target area in real time; collection points of soil data in an urban target area serve as area sample points, the heat supply temperature gradient of each area sample point is set, then a plane heat supply demand graph is constructed, surface meteorological data of each area sample point are collected in real time, the plane heat supply demand graph is updated, and the vertical heat gradient coefficient of each space building is analyzed. And generating a vertical heat supply demand set of each space building, and fusing the plane heat supply demand map and the vertical heat supply demand set of each space building into a space building topographic map to form a three-dimensional heat supply demand distribution map. The heat supply data processing efficiency is effectively improved.

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] The current smart city heating system mostly relies on static and flat heat distribution modes, which are difficult to adapt to the dynamic needs 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 characteristics, underground pipe network thermal attenuation, and vertical building thermal gradients.

[0003] Chinese patent publication number CN118296558A discloses a method, device and system for heating data fusion based on big data processing. The method includes the following steps: collecting basic heating data; constructing a heating map for the basic heating data and the heating location data corresponding to the basic heating data to obtain heating map data; extracting graph features from the heating map data to obtain heating map feature data, and extracting time series relationship features from the basic heating data to obtain heating time series relationship data; performing a first data fusion on the basic heating data according to the heating map feature data to obtain the first heating fusion data, and performing a second data fusion on the basic heating data according to the heating time series relationship data to obtain the second heating fusion data; performing a deep fusion of the first heating fusion data and the second heating fusion data to obtain heating deep fusion data. The present invention mines the value of heating data by fusing the basic heating data in different dimensions; it can be seen that the invention does not set specific heating weights in both the plane and vertical directions, and there is a problem of low efficiency in the processing of heating data. Summary of the invention

[0004] The object 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, comprising: 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.

[0006] Optionally, it also includes: collecting a spatial building topographic map of the urban target area, and acquiring soil data and underground pipe network data of the urban target area 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.

[0007] Optionally, the heating temperature gradient of the sample points in each area 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.

[0008] Optionally, the actual radiation area radius S(i) of each regional sample point is calculated; 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.

[0009] Optionally, collecting 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 judged 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 area is greater than or equal to the risk threshold, the meteorological risk state is judged to be a cooling risk, and the calculation process of the heating demand index of the sample point in the i-th area is updated, and the updated heating demand index of the sample point in the i-th area is set to αb(i).

[0010] Optionally, collecting 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 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 plane heating demand map remains unchanged.

[0011] Optionally, the various spatial buildings are divided according to the spatial building topographic map of the urban target area; 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.

[0012] Optionally, 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 is used as the vertical heating demand set of the j-th space building; Based on the spatial building topographic map of the target area of ​​the city, 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.

[0013] According to another aspect of the present application, a heating data processing method and device for a smart city is provided, comprising: 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.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein 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 during operation.

[0015] Compared with the prior art, the beneficial effects of the present invention are: this solution breaks through the static and planar limitations of traditional heating systems, and through dynamic weight calibration, vertical thermal gradient modeling and three-dimensional visualization technology, it constructs an integrated "ground-air-building" intelligent heating system, providing an efficient, low-carbon and sustainable innovative solution for high-density urban energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the flow of the heating data processing method for smart cities according to this embodiment.

[0018] Figure 2 It is a flow chart of the method for constructing a planar heating demand graph in this embodiment.

[0019] Figure 3 It is a schematic flow chart of the method for updating the planar heating demand graph of this embodiment.

[0020] Figure 4 It is a flowchart of the vertical heating demand set generation method of this embodiment.

[0021] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this embodiment.

[0022] Figure 6 A schematic diagram of the structure of a heating data processing device for a smart city provided in this embodiment. DETAILED DESCRIPTION

[0023] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0024] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, 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.

[0025] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0026] Specifically, the heating data processing method, device and storage medium for smart cities described in this application are applied to: heating optimization based on the thermal dynamic characteristics of underground pipeline networks within cities; the heating data processing for smart cities in this application involves the distribution processing of planar heat source data, the distribution processing of vertical heat source data of high-density building complexes, and data correction based on soil data and pipeline network data.

[0027] Based on the above application scenarios, please refer to Figure 1 As shown, it is a flow chart of a heating data processing method for a smart city provided by the present application, including: Step S101, collecting a spatial building topographic map of a target urban area, and acquiring soil data and underground pipe network data of the target urban area in real time; The spatial architectural topographic map is a scene map in a three-dimensional space, and its collection process can be collected by the urban planning management department, or it can be collected by modeling through photography and three-dimensional modeling. Its collection process is an existing public technology and this application will not elaborate on it; The soil data of the urban target area are soil parameters around the underground pipe network in the urban target area, including: soil temperature and soil thermal hysteresis coefficient, wherein the soil thermal hysteresis coefficient is soil thermal conductivity in this embodiment; The underground pipe network data is the relevant data of the underground heating pipe network, including: underground pipe network distribution plan and underground pipe network parameters; the underground pipe network parameters include the set heating temperature, the cross-sectional area of ​​the pipe section, the flow rate of the pipe section and the heating area It is worth noting that the solution provided in this application acts on the urban target area, and the setting of the urban target area is set by the user. It can be a certain area of ​​the city or the entire city, which is determined according to the needs of the user.

[0028] Exemplarily, in order to facilitate the collection of soil data and improve the pertinence of soil data, the optimal collection process of the soil data is to set sensors on the heating network for collection, and a collection point is set every 50 meters.

[0029] Please continue reading Figure 1As shown, the heating data processing method for smart cities described in this application also includes: Step S102, taking the soil data collection points in the urban target area as regional sample points, and setting the heating temperature gradient of each regional sample point according to 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.

[0030] In order to accurately construct the temperature gradient of each sample point in each area, before constructing the temperature gradient of each sample point in each area, it is necessary to perform the following steps in this application. Figure 2 The method for constructing a planar heating demand graph shown includes: Step S201, determining the heat loss index in real time based on soil data and underground pipe network data, and calibrating the heating temperature with the heat loss index.

[0031] Specifically, the process of calibrating the heating temperature with the heat loss index is as follows: Calculate the heat loss index Rs(i) of each regional sample point respectively, and set Rs(i)=1+Ssoil(i) / Smax×ln[STLC(i)]; The heating temperature of each pipe section of the underground pipe network is calibrated according to the heat loss index of each regional sample point, and the calibrated heating temperature of each pipe section is set as Tc(i). 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; Set Tc(i)=T(i)-Traw(i)×C(i); Where Tc(i) is the heating temperature after calibration 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 thermal humidity compensation coefficient of 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, i is a digital subscript indicating the number of the sample point, i∈N + .

[0032] Exemplarily, the division of each pipe section of the underground pipe network described in the present application is based on the collection points of soil data; at the same time, the value of the warning temperature can be freely set by those skilled in the art, 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 point in the area.

[0033] After completing the calibration of the heating data, please continue to Figure 2 As shown, the planar heating demand graph construction method also includes: Step S202, setting the heating temperature gradient of each area sample point according to the calibrated heating temperature.

[0034] Specifically, the process of setting the heating temperature gradient in this application is as follows: 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.

[0035] In step S202, a gradient component is constructed using the temperature drop value and the loss ratio to accurately reflect the thermal attenuation law of the pipeline network and provide a quantitative basis for dynamic temperature control.

[0036] Please continue reading Figure 2 As shown, the planar heating demand graph construction method also includes: Step S203, constructing a planar heating demand map of the spatial building topography map of the urban target area with the heating temperature gradient.

[0037] Specifically, the process of constructing a planar heating demand map is as follows: Calculate the actual radiation area radius S(i) of each regional sample point and set S(i)=v(i)×mc(i) / SM(i); In the formula, mc(i) represents the cross-sectional area of ​​the pipe section corresponding to the sample point in the i-th region, v(i) represents the flow velocity of the pipe section corresponding to the sample point in the i-th region, and SM(i) represents the heating area of ​​the pipe section corresponding to the sample point in the i-th region; Calculate the heating demand index α(i) of each regional sample point, and set α(i) = |temperature gradient of the i-th 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.

[0038] In step S203, by constructing a planar heat demand map, the city is divided into different areas according to the actual radiation area and the heat demand index, which helps to distribute heat in a targeted manner, avoid overall overheating or underheating, and improve heating efficiency.

[0039] It is worth noting 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 the actual radiation area of ​​a sample point in a certain region overlaps with the actual radiation area of ​​a sample point in another region, the actual radiation area of ​​the sample point in the region with a high heating demand index will be retained.

[0040] Please continue reading Figure 1 As shown, the heating data processing method for smart city also includes: Step S103, collecting surface meteorological data of sample points in each area in real time, analyzing the meteorological risk status of the sample points in each area, and then updating the planar heating demand map according to the meteorological risk status.

[0041] Please continue reading Figure 3 As shown, it is a schematic diagram of the process of the method for updating the planar heating demand map described in this embodiment, including: Step S301, collecting surface meteorological data of sample points in each region in real time; the meteorological data includes local meteorological wind speed, meteorological precipitation and local temperature difference; the meteorological data in this application is obtained in real time through a meteorological monitoring website; by introducing a multi-parameter meteorological model, the system's ability to respond to sudden weather events is enhanced to avoid heat supply imbalance caused by sudden environmental changes; Step S302: analyzing the meteorological risk status of each regional sample point based on the surface meteorological data of each regional sample point, and then updating the planar heating demand map according to the meteorological risk status.

[0042] Specifically, the process of updating the plane demand graph is as follows: 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, and β(i)=ln{1+(f(i)-F) / F×(△T(i)-t) / t×(js-JS) / JS}; wherein, f(i) is the local meteorological wind speed of the sample point in the ith region, △T(i) is the local temperature difference of the sample point in the ith region, 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, which is 3°C, JS is the preset precipitation, which is 3ml, and the local temperature difference is the temperature difference of the surface area where the sample point in the ith region is located within a fixed time, and the fixed time length is 30min in this application; the local temperature difference is the temperature before the fixed time minus the temperature after the fixed time; 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 judged 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 area is greater than or equal to the risk threshold, the meteorological risk state is judged to be a cooling risk, and the calculation process of the heating demand index of the sample point in the i-th area is updated, and the updated heating demand index of the sample point in the i-th area is set to αb(i), and αb(i)=α(i) / β(i).

[0043] It is understandable that the present application does not impose any specific limitation on the value of the risk threshold, and those skilled in the art can freely set it as long as the value requirement of the risk threshold is met. In the present application, taking temperature-sensitive cities as an example, the risk threshold can be set to 40%.

[0044] Update the heating demand map according to meteorological data, monitor wind speed, precipitation and temperature difference in real time, adjust the heating demand in time, respond to sudden weather changes, ensure the stable operation of the heating system, and avoid insufficient heating or energy waste due to sudden weather changes.

[0045] Please continue reading Figure 3 As shown, the planar heating demand map updating method further includes: Step S303, collecting groundwater level data of sample points in each area, and adjusting the update result of the planar heating demand map according to the collection result.

[0046] Specifically, the process of adjusting the update result of the planar heating demand map is as follows: The groundwater-temperature coupling index γ(i) of each regional sample point is constructed based on the groundwater level data, and γ(i)=Tc(i)×exp{-[sw(i)-YS] / YS} is set; where YS is the preset groundwater level, and sw(i) is the groundwater level of the sample point in the i-th region; 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. At the same time, the meteorological risk index is adjusted to βt(i), and βt(i)=β(i)×exp{1 / [Tc(i)-γ(i)]} is set; 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.

[0047] Specifically, the specific value of the preset coupling value described in this application is Tc(i)×60%; the groundwater level data is introduced to adjust the update result. The combination of groundwater and temperature coupling index can more comprehensively assess the cooling risk, especially in the case of multi-source cooling risk, provide a more accurate basis for regulation and control, and enhance the system's ability to cope with complex environments.

[0048] Please continue reading Figure 1 As shown, the heating data processing method for smart city also includes: Step S104, based on the spatial building topographic map of the urban target area, analyze the vertical thermal gradient coefficient of each spatial building, and generate a vertical heating demand set of each spatial building according to the construction result of the planar heating demand map.

[0049] See also Figure 4As shown, it is a flow chart of the method for generating a vertical heating demand set according to this embodiment, including: Step S401, divide the various spatial buildings according to the spatial building topographic map of the urban target area; calculate the thermal gradient by combining the floor height and the population density to solve the thermal stratification problem of high-rise buildings and improve the refinement level of the heating system.

[0050] For example, the spatial building described in this application specifically refers to a high-rise building, and the high-rise building described in this application refers to a building with more than 6 floors; it can be understood that the vertical heating demand set of high-rise buildings described in this application is the plane demand index of the non-high-rise building location in the plane heating demand diagram.

[0051] Please continue reading Figure 4 As shown, the vertical heating demand set generation method also includes: Step S402, real-time acquisition of thermal distribution data of each spatial building, and construction of a vertical thermal gradient coefficient of each spatial building based on the thermal distribution data of each spatial building; the thermal distribution data of each spatial building includes building floor height, building floor population density, and temperature difference between adjacent floors.

[0052] Specifically, the process of constructing the vertical thermal gradient coefficient of each space building is as follows: Set the vertical heat loss coefficient VIF(j,k) of each floor in each space building, and set VIF(j,k)=0.6×H(j,k)+0.4×ρ(j,k); where j and k are both digital subscripts; In the formula, H(j,k) represents the height of the kth floor in the jth space building, and ρ(j,k) represents the population density of the kth floor in the jth space building; Set the vertical thermal gradient coefficient FHP(j,k) of each floor in each space building, and set .

[0053] Construct vertical thermal gradient coefficient. Use the building's thermal distribution data (such as floor height and population density) to dynamically adjust the heating demand of each floor, ensure reasonable heat distribution on different floors of high-rise buildings, and improve the refined management level of the heating system.

[0054] Please continue reading Figure 4 As shown, the vertical heating demand set generation method also includes: Step S403, generating a vertical heating demand set for each spatial building based on the construction result of the planar heating demand map and the vertical thermal gradient coefficient of each spatial building; through the multi-dimensional superposition of the spatial topographic map and the heating data, the global visualization of the heating demand is realized, supporting the digital upgrade of smart city energy management.

[0055] Specifically, 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 is taken as the vertical heating demand set of the j-th space building.

[0056] Please continue reading Figure 1 As shown, the heating data processing method for smart city also includes: Step S105: Based on the spatial building topography 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 topography map to form a three-dimensional heating demand distribution map, and the heating demand distribution map is output to the user; the planar heating demand map and the vertical demand set are integrated into a three-dimensional distribution map to intuitively display the spatial differences in urban heat demand. This step supports precise heating control, provides decision-making basis for operation and maintenance personnel, optimizes heat source distribution, reduces pipeline redundant load, and extends equipment life.

[0057] Exemplarily, in the present application, the process of "integrating 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" is to set the heating demand index for all buildings in the spatial topographic map, set the vertical heating demand set for the spatial buildings, and assign the heating demand index to the non-spatial buildings using the planar heating demand map.

[0058] The heating data processing device for smart cities provided in the embodiments of the present application can execute the heating data processing method for smart cities provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.

[0059] From the hardware level, this application is to realize the function of the heating data processing method for smart city in a computer. This application also provides a computer readable storage medium, please refer to Figure 5 As shown, it is arranged in an electronic device, and the electronic device includes: 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 via the bus 4, and the data port 3 performs data transmission with the processing unit 1 via the bus 4, and the data port is used to receive and send data; and the storage unit is composed of one or more computer-readable media.

[0060] The physical implementation of the electronic device and the storage method of the storage medium described in this application are both existing technologies well known to those skilled in the art (for example, through PCI circuit design, program burning, etc.), and this application will not elaborate on them here.

[0061] 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-mentioned 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-mentioned internal combustion generator set control method based on machine learning can be executed.

[0062] In the present application, computer-readable storage media are tangible physical storage media that can store the above-mentioned computer programs and various types of data used in the programs; the physical storage media include but are not limited to random access memory units, read-only memory units, optical disks, hard disks and other existing physical storage media or combinations of media.

[0063] Please continue reading Figure 6 As shown, it is a structural schematic diagram of a heating data processing device for a smart city according to this embodiment, including: 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.

[0064] So far, the technical solutions of the present invention have 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 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.

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 1, characterized in that: 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 area 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 area is updated, and the updated heating demand index of the sample point in the i-th area is set to αb(i).

6. The heating data processing method for smart city according to claim 5, characterized in that: 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.

7. The heating data processing method for smart city according to claim 6, 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.

8. The heating data processing method for smart city according to claim 7, 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 area of ​​the city, 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.

9. 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.

10. 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 8 during operation.

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