GIS-based heat supply cost dynamic adjustment method and system
By establishing a topology diagram in the heating pipeline network, calculating the cost coefficient and pipeline network complexity, and dynamically adjusting the cost system in combination with weather data, the problem of low heating economy is solved and more reasonable and effective cost management is achieved.
Patent Information
- Application Number
- CN202510129666.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-05
- Publication Date
- 2025-05-16
AI Technical Summary
In the prior art, the heating user's heating efficiency of heating users is low, mainly because a fixed single cost system cannot effectively reflect the impact of different geographical locations and pipeline complexity on heating costs.
By obtaining the geographical location of each heat user in the heating pipeline network, establishing a topology map of the heating pipeline network, calculating the geographical cost coefficient and the pipeline network complexity, clustering based on these data, and dynamically adjusting the cost system based on weather data.
Dynamic adjustment of the heat user cost system has been achieved, heating economy has been improved, and cost rationality and effectiveness have been ensured.
Smart Images
Figure CN120013184A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of heating pipe networks, and more specifically, to a method and system for dynamically adjusting heating costs based on GIS. Background Art
[0002] Geographic Information System (GIS) is an emerging frontier discipline that integrates new theories of earth science, space science, environmental science, information science and management science, and comprehensively applies the latest achievements of computer technology, surveying and mapping remote sensing technology, modern geography and automatic mapping technology. Introducing geographic information into pipe network management can completely and accurately manage massive pipe network and terrain data, improve the degree of information sharing and data update cycle. Due to the advantages of geographic information system in information processing, it has been applied to the operation and management of urban infrastructure such as heating pipe networks at home and abroad in recent years.
[0003] Due to the complexity of urban heating pipeline networks and the large number of pipe sections and accessories, the heating costs incurred by heat users in different geographical locations are also different. The existing technology only determines the cost of each heat user through a fixed single cost system, and the heating economy is low. Summary of the invention
[0004] The present invention provides a method for dynamically adjusting heating costs based on GIS, which is used to solve the problem of low heating economy of heat users in the heating network in the prior art, and comprises: Obtaining the geographical location of each heat user in the heat supply network, establishing a heat supply network topology map according to the geographical location of each heat user, and determining the geographical cost coefficient according to the heat supply network topology map; Calculate the network complexity of each heat user node in the heat supply network topology diagram, and determine the basic cost system coefficient based on the network complexity and geographical cost coefficient of each heat user node; Cluster the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient, and determine the basic cost system of each heat user in the cluster partition according to the clustering result; The weather data of the cluster partition is obtained, the cost adjustment coefficient of the heat user is determined according to the weather data of the cluster partition, and the basic cost system is adjusted according to the cost adjustment coefficient to obtain the cost system of the heat user.
[0005] Furthermore, the heat supply network topology map is established according to the geographical location of each heat user, and the geographical cost coefficient is determined according to the heat supply network topology map, including: Each heat user in the heat supply network is regarded as a node, and a heat supply network topology diagram is established according to each heat user node and the connecting pipeline; Calculate the distance between the heat user node and the root node in the heat supply network topology diagram, and use the distance between the heat user node and the root node as the first cost coefficient; Calculate the heating area of the parent node of the heat user node in the heat network topology diagram, use the heating area of the parent node of the heat user node as the second cost coefficient, and determine the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient.
[0006] Further, determining the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient includes: The heating cost coefficient of the heat user node is determined according to the cost calculation formula, and the cost calculation formula is specifically: , in, is the heating cost coefficient of the heat user node, is the first preset weight, is the second preset weight, is the first cost coefficient, is the second cost coefficient, is the first preset standard coefficient, It is the second preset standard coefficient.
[0007] Furthermore, the calculation of the pipe network complexity of each heat user node in the heat supply pipe network topology diagram includes: Obtain the number of pipe elbows at the heat user node in the heat supply pipe network topology diagram, and determine the first pipe network complexity according to the number of pipe elbows at the heat user node; Obtain the elbow angle of the heat user node in the heat supply network topology diagram, and determine the complexity of the second pipe network according to the elbow angle of the heat user node; Obtain the pipe length of the heat user node in the heat supply pipe network topology diagram, and determine the complexity of the third pipe network according to the pipe length of the heat user node; Convert the first pipe network complexity, the second pipe network complexity and the third pipe network complexity into the first line segment, the second line segment and the third line segment according to a preset ratio; The midpoints of the first line segment and the second line segment are intersected, and a triangular pyramid is established with the intersection point as the starting point and the third line segment as the height. The area of the triangular pyramid is calculated to obtain the network complexity of the heat user node.
[0008] Furthermore, the basic cost system coefficient is determined according to the pipe network complexity and geographical cost coefficient of each heat user node, including: Multiply the network complexity of the heat user node by the heating cost coefficient to obtain the basic cost system coefficient.
[0009] Furthermore, clustering the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient includes: Determine the k value according to the number of heat user nodes in the heat supply network topology diagram, establish a sample data set according to the basic cost system coefficient of each heat user node, and randomly select k initial clustering centers of the sample data set; Calculate the Euclidean distance from each basic cost system coefficient to the initial cluster center in the sample data set, and divide the hot user nodes into corresponding cluster partitions according to the Euclidean distance from each basic cost system coefficient to the initial cluster center; Calculate the average value of all basic cost system coefficients in each cluster partition, and update the cluster center according to the average value of all basic cost system coefficients in each cluster partition; The above steps are iterated repeatedly until the cluster center no longer changes, and k cluster partitions of the hot user nodes are obtained.
[0010] Furthermore, the basic cost system of each heat user in the cluster partition is determined according to the clustering result, including: Obtain historical heating data of the heating network, and determine the historical cost system coefficient of the heat user and the corresponding basic cost system based on the historical heating data of the heating network; According to the historical cost system coefficients of heat users and the corresponding basic cost system, a training sample set is established according to the historical cost system coefficients and the corresponding basic cost system; Establishing a cost system evaluation model according to the training sample set and training the cost system evaluation model to obtain a trained cost system evaluation model; The cluster partitions corresponding to the hot users are obtained, and the cluster center values of the cluster partitions corresponding to the hot users are input into the trained cost system evaluation model to obtain the basic cost system of the hot users.
[0011] Furthermore, the cost adjustment coefficient of the heat user is determined according to the clustered partitioned weather data, including: Obtain historical weather data of the heating network, and determine the amount of weather type data and the heating load of each heating user based on the historical weather data of the heating network; Calculate the correlation coefficient between the amount of weather type data and the heating load of each heat user, normalize the correlation coefficient between the amount of weather type data and the heating load of each heat user, and obtain the impact weight of each weather type on the heat user; Obtain the weather forecast data of the current heating network, and determine the forecast weather type and the corresponding weather type data volume according to the weather forecast data; Multiply the weather type data volume by the corresponding impact weight to obtain the cost adjustment coefficient for heat users.
[0012] Furthermore, the adjusting the basic cost system according to the cost adjustment coefficient includes: Obtaining a preset standard cost adjustment coefficient, calculating a ratio of the cost adjustment coefficient to the preset standard cost adjustment coefficient, and obtaining an adjustment ratio; Obtain the impact factor of each cost in the basic cost system, multiply the impact factor of each cost by the adjustment ratio, and obtain the adjustment factor corresponding to each cost; Multiply the adjustment factor by the corresponding cost to obtain the adjusted cost system.
[0013] In order to achieve the above object, the present invention also provides a GIS-based heating cost dynamic adjustment system, comprising: The first module is used to obtain the geographical location of each heat user in the heat supply network, establish a heat supply network topology map according to the geographical location of each heat user, and determine the geographical cost coefficient according to the heat supply network topology map; The second module is used to calculate the pipe network complexity of each heat user node in the heat supply pipe network topology diagram, and determine the basic cost system coefficient according to the pipe network complexity of each heat user node and the geographical cost coefficient; The third module is used to cluster the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient, and determine the basic cost system of each heat user in the cluster partition according to the clustering result; The fourth module is used to obtain the weather data of the cluster partition, determine the cost adjustment coefficient of the heat user according to the weather data of the cluster partition, adjust the basic cost system according to the cost adjustment coefficient, and obtain the cost system of the heat user.
[0014] The beneficial effects of the present invention are: By applying the above technical scheme, the present invention utilizes GIS technology to reflect the geographical location of the heating network, and sets the cost system of each heat user by the geographical location of each heat user corresponding to the heating network and combining weather data, thereby realizing dynamic adjustment of the cost system of heat users and effectively improving the economy of heating. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0016] Figure 1 The overall flow chart of a method for dynamically adjusting heating costs based on GIS proposed in an embodiment of the present invention is shown; Figure 2 A structural schematic diagram of a heating cost dynamic adjustment system based on GIS proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0018] The present application embodiment provides a method for dynamically adjusting heating costs based on GIS, such as Figure 1 As shown, including: S101, obtaining the geographical location of each heat user in the heat supply network, establishing a heat supply network topology map according to the geographical location of each heat user, and determining a geographical cost coefficient according to the heat supply network topology map; In some embodiments of the present application, the method of establishing a heating network topology map according to the geographical location of each heat user and determining the geographical cost coefficient according to the heating network topology map includes: taking each heat user in the heating network as a node, and establishing a heating network topology map according to each heat user node and the connecting pipeline; calculating the distance value between the heat user node and the root node in the heating network topology map, and taking the distance value between the heat user node and the root node as the first cost coefficient; calculating the heating area of the parent node of the heat user node in the heating network topology map, and taking the heating area of the parent node of the heat user node as the second cost coefficient, and determining the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient.
[0019] In this embodiment, each heat user is taken as a heat user node, the heat source is taken as the root node, and the connecting pipe is taken as the connecting line to establish a heat supply network topology map. The first cost coefficient is determined by the distance value of the connecting line between the heat user node and the root node, which can accurately reflect the heat loss from the heat source to the heat user. The second cost coefficient is calculated by the heating area of the parent node of the heat user node, and the heat loss of the heat source at the parent node is calculated, thereby calculating the heating cost coefficient of the heat user node.
[0020] In some embodiments of the present application, determining the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient includes: determining the heating cost coefficient of the heat user node according to a cost calculation formula, and the cost calculation formula is specifically, ， in, is the heating cost coefficient of the heat user node, is the first preset weight, is the second preset weight, is the first cost coefficient, is the second cost coefficient, is the first preset standard coefficient, It is the second preset standard coefficient.
[0021] S102, calculating the pipe network complexity of each heat user node in the heat supply pipe network topology diagram, and determining the basic cost system coefficient according to the pipe network complexity of each heat user node and the geographical cost coefficient; In some embodiments of the present application, the calculation of the pipe network complexity of each heat user node in the heating pipe network topology diagram includes: obtaining the number of pipe elbows of the heat user node in the heating pipe network topology diagram, and determining the first pipe network complexity according to the number of pipe elbows of the heat user node; obtaining the elbow angle of the heat user node in the heating pipe network topology diagram, and determining the second pipe network complexity according to the elbow angle of the heat user node; obtaining the pipe length of the heat user node in the heating pipe network topology diagram, and determining the third pipe network complexity according to the pipe length of the heat user node; converting the first pipe network complexity, the second pipe network complexity and the third pipe network complexity into a first line segment, a second line segment and a third line segment according to a preset ratio; intersecting the midpoints of the first line segment and the second line segment, establishing a triangular pyramid with the intersection point as the starting point and the third line segment as the height, calculating the area of the triangular pyramid, and obtaining the pipe network complexity of the heat user node.
[0022] In this embodiment, the number of pipe elbows at the heat user node is taken as the first pipe network complexity. The average elbow angle of the pipe elbow corresponding to the heat user node is obtained, and the absolute value of the difference between the average elbow angle and the preset allowable elbow angle is calculated to obtain the second pipe network complexity. The pipe length of the heat user node is taken as the third pipe network complexity. The preset ratios of the first pipe network complexity, the second pipe network complexity and the third pipe network complexity are set respectively, and the first pipe network complexity, the second pipe network complexity and the third pipe network complexity are standardized to obtain the first line segment, the second line segment and the third line segment and establish a triangular pyramid to obtain the pipe network complexity of the heat user node and the complexity of the heating pipe network corresponding to the heat user.
[0023] In some embodiments of the present application, determining the basic cost system coefficient based on the pipeline network complexity and geographical cost coefficient of each heat user node includes: multiplying the pipeline network complexity of the heat user node by the heating cost coefficient to obtain the basic cost system coefficient.
[0024] In this embodiment, the heating cost of each heat user corresponding to the heating network is accurately reflected by the basic cost system coefficient, thereby obtaining the basic cost system coefficient.
[0025] S103, clustering each heat user node in the heat supply network topology diagram according to the basic cost system coefficient, and determining the basic cost system of each heat user in the cluster partition according to the clustering result; In some embodiments of the present application, the clustering of each heat user node in the heating network topology diagram according to the basic cost system coefficient includes: determining the k value according to the number of heat user nodes in the heating network topology diagram, establishing a sample data set according to the basic cost system coefficient of each heat user node, and randomly selecting k initial clustering centers of the sample data set; calculating the Euclidean distance from each basic cost system coefficient in the sample data set to the initial clustering center, and dividing the heat user nodes into corresponding cluster partitions according to the Euclidean distance from each basic cost system coefficient to the initial clustering center; calculating the average value of all basic cost system coefficients in each cluster partition, and updating the cluster center according to the average value of all basic cost system coefficients in each cluster partition; repeating the above steps until the cluster center no longer changes, and obtaining k cluster partitions of the heat user nodes.
[0026] In this embodiment, the k value is determined by the number of heat user nodes in the heat supply network topology diagram. The higher the number, the larger the corresponding k value. Each heat user is clustered into the corresponding cluster partition through the basic cost system coefficient.
[0027] In some embodiments of the present application, determining the basic cost system of each heat user in the cluster partition based on the clustering results includes: obtaining historical heating data of the heating network, and determining the historical cost system coefficient and the corresponding basic cost system of the heat user based on the historical heating data of the heating network; establishing a training sample set based on the historical cost system coefficient and the corresponding basic cost system of the heat user; establishing a cost system evaluation model based on the training sample set and training the cost system evaluation model to obtain a trained cost system evaluation model; obtaining the cluster partition corresponding to the heat user, and inputting the cluster center value of the cluster partition corresponding to the heat user into the trained cost system evaluation model to obtain the basic cost system of the heat user.
[0028] In this embodiment, the basic cost system is specifically the specific values of various heating costs in the heating database, including fuel costs, maintenance costs, etc. A cost system evaluation model is established based on a deep learning neural network, and the cost system evaluation model is trained through historical heating data of the heating network, so that the cluster center values of the cluster partitions corresponding to the heat users are input into the trained cost system evaluation model to obtain the basic cost system of the heat users.
[0029] S104, obtaining weather data of the cluster partitions, determining cost adjustment coefficients of heat users according to the weather data of the cluster partitions, and adjusting the basic cost system according to the cost adjustment coefficients to obtain a cost system for heat users.
[0030] In some embodiments of the present application, the cost adjustment coefficient for heat users is determined based on the clustered and partitioned weather data, including: obtaining historical weather data of the heating network, and determining the amount of weather type data and the heating load of each heat user based on the historical weather data of the heating network; calculating the correlation coefficient between the amount of weather type data and the heating load of each heat user, and normalizing the correlation coefficient between the amount of weather type data and the heating load of each heat user to obtain the impact weight of each weather type on the heat user; obtaining the weather forecast data of the current heating network, and determining the predicted weather type and the corresponding amount of weather type data based on the weather forecast data; and multiplying the amount of weather type data by the corresponding impact weight to obtain the cost adjustment coefficient for the heat user.
[0031] In this embodiment, the weather type data volume is specifically the characteristic data volume corresponding to the weather type, for example, rainfall in rainy weather and light intensity in sunny weather. The impact weight of each weather type on the heat user is obtained by calculating the correlation coefficient between the weather type data volume and the heating load of each heat user. The weather forecast data corresponding to the current heat user is multiplied by the corresponding impact weight to obtain the cost adjustment coefficient of the heat user.
[0032] In some embodiments of the present application, the adjustment of the basic cost system according to the cost adjustment coefficient includes: obtaining a preset standard cost adjustment coefficient, calculating the ratio of the cost adjustment coefficient to the preset standard cost adjustment coefficient, and obtaining the adjustment ratio; obtaining the influencing factor of each cost in the basic cost system, multiplying the influencing factor of each cost by the adjustment ratio, and obtaining the adjustment factor corresponding to each cost; multiplying the adjustment factor by the corresponding cost to obtain the adjusted cost system.
[0033] In this embodiment, the influencing factor of each cost is obtained through the correlation coefficient between the weather type data volume and each cost, and the basic cost system is adjusted through the cost adjustment coefficient to achieve dynamic adjustment of the heat user cost system.
[0034] Based on the same technical concept, such as Figure 2As shown, the present invention also provides a GIS-based dynamic adjustment system for heating costs, including: a first module, used to obtain the geographical location of each heat user in the heating network, establish a heating network topology map according to the geographical location of each heat user, and determine the geographical cost coefficient according to the heating network topology map; a second module, used to calculate the network complexity of each heat user node in the heating network topology map, and determine the basic cost system coefficient according to the network complexity and geographical cost coefficient of each heat user node; a third module, used to cluster each heat user node in the heating network topology map according to the basic cost system coefficient, and determine the basic cost system of each heat user in the cluster partition according to the clustering result; a fourth module, used to obtain the weather data of the cluster partition, determine the cost adjustment coefficient of the heat user according to the weather data of the cluster partition, adjust the basic cost system according to the cost adjustment coefficient, and obtain the cost system of the heat user.
[0035] By applying the above technical scheme, the present invention obtains the geographical location of each heat user in the heating network, establishes a heating network topology map according to the geographical location of each heat user, and determines the geographical cost coefficient according to the heating network topology map; calculates the network complexity of each heat user node in the heating network topology map, and determines the basic cost system coefficient according to the network complexity and geographical cost coefficient of each heat user node; clusters each heat user node in the heating network topology map according to the basic cost system coefficient, and determines the basic cost system of each heat user in the cluster partition according to the clustering result; obtains the weather data of the cluster partition, determines the cost adjustment coefficient of the heat user according to the weather data of the cluster partition, and adjusts the basic cost system according to the cost adjustment coefficient to obtain the cost system of the heat user. The cost system of the heat user is flexibly adjusted using GIS technology, which effectively improves the economic efficiency of heating.
[0036] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.
[0037] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.
[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for dynamic adjustment of heating cost based on GIS, characterized in that: The method comprises: Obtaining the geographical location of each heat user in the heat supply network, establishing a heat supply network topology map according to the geographical location of each heat user, and determining the geographical cost coefficient according to the heat supply network topology map; Calculate the network complexity of each heat user node in the heat supply network topology diagram, and determine the basic cost system coefficient based on the network complexity and geographical cost coefficient of each heat user node; Cluster the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient, and determine the basic cost system of each heat user in the cluster partition according to the clustering result; The weather data of the cluster partition is obtained, the cost adjustment coefficient of the heat user is determined according to the weather data of the cluster partition, and the basic cost system is adjusted according to the cost adjustment coefficient to obtain the cost system of the heat user.
2. The method for dynamic adjustment of heating cost based on GIS according to claim 1, characterized in that: The method of establishing a heating network topology map according to the geographical location of each heat user and determining a geographical cost coefficient according to the heating network topology map includes: Each heat user in the heat supply network is regarded as a node, and a heat supply network topology diagram is established according to each heat user node and the connecting pipeline; Calculate the distance between the heat user node and the root node in the heat supply network topology diagram, and use the distance between the heat user node and the root node as the first cost coefficient; Calculate the heating area of the parent node of the heat user node in the heat network topology diagram, use the heating area of the parent node of the heat user node as the second cost coefficient, and determine the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient.
3. The method for dynamic adjustment of heating cost based on GIS according to claim 2, characterized in that: The step of determining the heating cost coefficient of the heat user node according to the first cost coefficient and the second cost coefficient includes: The heating cost coefficient of the heat user node is determined according to the cost calculation formula, and the cost calculation formula is specifically: ， in, is the heating cost coefficient of the heat user node, is the first preset weight, is the second preset weight, is the first cost coefficient, is the second cost coefficient, is the first preset standard coefficient, It is the second preset standard coefficient.
4. The method for dynamic adjustment of heating cost based on GIS according to claim 1, characterized in that: The calculation of the pipe network complexity of each heat user node in the heating pipe network topology diagram includes: Obtain the number of pipe elbows at the heat user node in the heat supply pipe network topology diagram, and determine the first pipe network complexity according to the number of pipe elbows at the heat user node; Obtain the elbow angle of the heat user node in the heat supply network topology diagram, and determine the complexity of the second pipe network according to the elbow angle of the heat user node; Obtain the pipe length of the heat user node in the heat supply pipe network topology diagram, and determine the complexity of the third pipe network according to the pipe length of the heat user node; Convert the first pipe network complexity, the second pipe network complexity and the third pipe network complexity into the first line segment, the second line segment and the third line segment according to a preset ratio; The midpoints of the first line segment and the second line segment are intersected, and a triangular pyramid is established with the intersection point as the starting point and the third line segment as the height. The area of the triangular pyramid is calculated to obtain the network complexity of the heat user node.
5. The method for dynamic adjustment of heating cost based on GIS according to claim 4, characterized in that: The basic cost system coefficient is determined according to the pipe network complexity and geographical cost coefficient of each heat user node, including: Multiply the network complexity of the heat user node by the heating cost coefficient to obtain the basic cost system coefficient.
6. The method for dynamic adjustment of heating cost based on GIS according to claim 1, characterized in that: The clustering of the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient includes: Determine the k value according to the number of heat user nodes in the heat supply network topology diagram, establish a sample data set according to the basic cost system coefficient of each heat user node, and randomly select k initial clustering centers of the sample data set; Calculate the Euclidean distance from each basic cost system coefficient to the initial cluster center in the sample data set, and divide the hot user nodes into corresponding cluster partitions according to the Euclidean distance from each basic cost system coefficient to the initial cluster center; Calculate the average value of all basic cost system coefficients in each cluster partition, and update the cluster center according to the average value of all basic cost system coefficients in each cluster partition; The above steps are iterated repeatedly until the cluster center no longer changes, and k cluster partitions of the hot user nodes are obtained.
7. The method for dynamic adjustment of heating cost based on GIS according to claim 6, characterized in that: Determining the basic cost system of each heat user in the cluster partition according to the clustering result includes: Obtain historical heating data of the heating network, determine the historical cost system coefficients and the corresponding basic cost system of the heat users according to the historical heating data of the heating network, and establish a training sample set according to the historical cost system coefficients and the corresponding basic cost system; Establishing a cost system evaluation model according to the training sample set and training the cost system evaluation model to obtain a trained cost system evaluation model; The cluster partitions corresponding to the hot users are obtained, and the cluster center values of the cluster partitions corresponding to the hot users are input into the trained cost system evaluation model to obtain the basic cost system of the hot users.
8. The method for dynamic adjustment of heating cost based on GIS according to claim 1, characterized in that: The step of determining the cost adjustment coefficient of the heat user according to the clustered partitioned weather data includes: Obtain historical weather data of the heating network, and determine the amount of weather type data and the heating load of each heating user based on the historical weather data of the heating network; Calculate the correlation coefficient between the amount of weather type data and the heating load of each heat user, normalize the correlation coefficient between the amount of weather type data and the heating load of each heat user, and obtain the impact weight of each weather type on the heat user; Obtain the weather forecast data of the current heating network, and determine the forecast weather type and the corresponding weather type data volume according to the weather forecast data; Multiply the weather type data volume by the corresponding impact weight to obtain the cost adjustment coefficient for heat users.
9. The method for dynamic adjustment of heating cost based on GIS according to claim 8, characterized in that: The adjustment of the basic cost system according to the cost adjustment coefficient includes: Obtaining a preset standard cost adjustment coefficient, calculating a ratio of the cost adjustment coefficient to the preset standard cost adjustment coefficient, and obtaining an adjustment ratio; Obtain the impact factor of each cost in the basic cost system, multiply the impact factor of each cost by the adjustment ratio, and obtain the adjustment factor corresponding to each cost; Multiply the adjustment factor by the corresponding cost to obtain the adjusted cost system.
10. A GIS-based dynamic adjustment system for heating costs, characterized in that: include: The first module is used to obtain the geographical location of each heat user in the heat supply network, establish a heat supply network topology map according to the geographical location of each heat user, and determine the geographical cost coefficient according to the heat supply network topology map; The second module is used to calculate the pipe network complexity of each heat user node in the heat supply pipe network topology diagram, and determine the basic cost system coefficient according to the pipe network complexity of each heat user node and the geographical cost coefficient; The third module is used to cluster the heat user nodes in the heat supply network topology diagram according to the basic cost system coefficient, and determine the basic cost system of each heat user in the cluster partition according to the clustering result; The fourth module is used to obtain the weather data of the cluster partition, determine the cost adjustment coefficient of the heat user according to the weather data of the cluster partition, adjust the basic cost system according to the cost adjustment coefficient, and obtain the cost system of the heat user.