A method and system for managing urban centralized heating data

Abstract

The present invention relates to the field of industrial data analysis technology, and more specifically to a method and system for managing urban centralized heating data. The method comprises: obtaining gas consumption of different types of users in various areas of a city during various collection time periods; mapping the average gas consumption of each type of user in each area during various collection time periods into a coordinate system to obtain a plurality of mapping points; segmenting the sequence of mapping points in the coordinate system to obtain a plurality of gas consumption sequences for the area, with different gas consumption sequences corresponding to different gradients; determining a heating priority number for the area based on the gas consumption of users corresponding to each of the gradient gas consumption sequences in the area; and sequentially supplying heat to each area based on the heating priority number and heating demand of each area. The present invention improves room temperature stability and heating flexibility for users.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial data analysis, and in particular to a method and system for managing urban centralized heating data. Background Art

[0002] Urban district heating is a heating system that generates heat from one or more central heat sources and distributes hot water or steam to buildings through a network of pipes. With the acceleration of urbanization, urban district heating has become the primary heating method in many regions, especially in colder areas. District heating systems involve extensive data collection, transmission, analysis, and optimization. Therefore, efficient management and optimization of urban district heating can help achieve intelligent, precise, and transparent heating processes, further improving energy efficiency, reducing operating costs, and enhancing customer satisfaction and system stability.

[0003] In some scenarios, after the transition to clean heating in urban centralized heating systems, waste heat and gas storage are both challenging. Waste heat is relatively stable during the heating season, providing consistent heating to users who require it. However, gas heating scheduling is difficult to tailor to changing heating needs across different regions, resulting in low room temperature stability and low heating flexibility. Summary of the Invention

[0004] In order to solve the technical problems of low room temperature stability and low heating flexibility for users, the purpose of the present invention is to provide a method and system for urban centralized heating data management. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for managing urban centralized heating data, comprising: obtaining the gas consumption of different types of users in various areas of the city during each collection time period; mapping the average gas consumption of users of each type in each of the areas during each collection time period to a coordinate system to obtain a plurality of mapping points; segmenting the mapping point sequence in the coordinate system according to the average gas consumption difference between adjacent mapping points in each of the areas and the average gas consumption corresponding to each mapping point to obtain a plurality of gas consumption sequences of the area, with different gas consumption sequences corresponding to different gradients; determining the heating priority number of the area according to the gas consumption of users corresponding to the gas consumption sequences of each gradient of the area; and supplying heat to each of the areas in turn according to the heating priority number and heating demand of each of the areas.

[0006] Optionally, dividing the mapping point sequence in the coordinate system according to the average gas consumption difference between adjacent mapping points in each area and the average gas consumption corresponding to each mapping point to obtain multiple gas consumption sequences in the area includes: using The function traverses the sequence of mapping points, calculates the difference between the average gas consumption of each adjacent mapping point, and obtains the average gas consumption difference between each adjacent mapping point; selects a maximum difference value from the average gas consumption differences between each adjacent mapping point in the area; determines the smaller of the average gas consumptions corresponding to the adjacent mapping points corresponding to the maximum difference value, and takes a predetermined number of mapping points before the smaller value and performs segmentation.

[0007] Optionally, determining the heating priority number of the area according to the gas consumption of users corresponding to the gas consumption sequences of each gradient of the area includes: determining a comprehensive gas consumption level corresponding to the gas consumption sequence in the area according to the gas consumption of users corresponding to the gas consumption sequence in the area; determining the fluctuation similarity of the gas consumption of two gradients in any two areas with the same gradient number according to the gas consumption in the gas consumption sequences in the areas; determining the confidence that the two gradients are of the same category according to the fluctuation similarity of the gas consumption of two gradients in the areas with the same gradient number and the comprehensive gas consumption level corresponding to the two gradients; Select two areas with the same gradient number and the areas corresponding to the two gradients with confidence greater than the first threshold belong to the same first category; divide the areas with the same gradient number into a block group, and determine the degree of merging between any two gradients in different block groups according to the first average gas consumption of users included in any two gradients in adjacent block groups; determine that the areas corresponding to any two gradients with the merging degree less than or equal to the second threshold belong to the same second category; determine the heating priority ordinal number of the area according to the first gas consumption of all users in the second category to which the area belongs in all gradients, the second gas consumption of all users in the first category to which the gradient belongs, and the gradient value corresponding to the area.

[0008] Optionally, determining the comprehensive gas consumption level corresponding to the gas consumption sequence in the area based on the gas consumption of users corresponding to the gas consumption sequence in the area includes: calculating a second average gas consumption of the gas consumption of all users corresponding to the gas consumption sequence; determining the maximum gas consumption and the minimum gas consumption of users in the area, and a first difference between the maximum gas consumption and the minimum gas consumption; and calculating a first ratio between the first average gas consumption and the first difference to obtain the comprehensive gas consumption level corresponding to the gas consumption sequence.

[0009] Optionally, determining the fluctuation similarity of gas consumption of two gradients in any two regions with the same gradient number based on the gas consumption in the gas consumption sequences in the regions includes: respectively calculating a second ratio between the maximum average gas consumptions in the gas consumption sequences corresponding to the two gradients, and a third ratio between the minimum average gas consumptions in the gas consumption sequences corresponding to the two gradients; and calculating a first product between the second ratio and the third ratio to obtain the fluctuation similarity of gas consumption of the two gradients in the region.

[0010] Optionally, determining the confidence that the two gradients are of the same category based on the fluctuation similarity of the gas consumption of the two gradients in the two areas with the same gradient number and the comprehensive gas consumption levels corresponding to the two gradients includes: calculating the absolute value of a second difference between the comprehensive gas consumption levels corresponding to the two gradients, and a second product between the absolute value of the second difference and the fluctuation similarity; and normalizing the inverse of the second product to obtain the confidence that the two gradients are of the same category.

[0011] Optionally, determining the degree of merging between any two gradients in different block groups based on the first average gas consumption of users included in any two gradients in adjacent block groups includes: calculating the absolute value of the third difference between the first average gas consumption of users included in any two gradients, and normalizing the absolute value of the third difference to obtain the degree of merging between any two gradients in the different block groups.

[0012] Optionally, determining the heating priority number of the area based on the first gas consumption of all users in the second category to which the area belongs in all gradients, the second gas consumption of all users in the first category to which the gradient belongs, and the gradient value corresponding to the area includes: calculating a fourth ratio between the second gas consumption and the first gas consumption, and the inverse of the superposition value of the gradient values ​​of all gradients corresponding to the area; calculating a third product between the fourth ratio and the inverse of the superposition value, and taking the average of the third products corresponding to each of the gradients to obtain the heating priority number of the area.

[0013] Optionally, supplying heat to each of the areas in sequence according to the heating priority number and heating demand of each area includes: first transporting the gas to the main pipeline of the area with a high heating priority number, and supplying heat to users in the area in sequence based on the heating demand of the area with a high heating priority number; after the heating demand of the area with a high heating priority number is met, allocating the remaining gas to the area with the next heating priority.

[0014] In the second aspect, an embodiment of the present invention provides an urban centralized heating data management system, comprising: a processor and a memory; wherein the memory is used to store computer programs that can be run on the processor; the processor is used to execute the programs stored in the memory to implement the steps of the urban centralized heating data management method mentioned in the first aspect.

[0015] The present invention has the following beneficial effects: first, the gas consumption of different types of users in various areas of the city in each collection time period is obtained; then, the average gas consumption of the gas consumption of each type of users in each of the areas in each collection time period is mapped to a coordinate system to obtain multiple mapping points; then, the mapping point sequence in the coordinate system is divided according to the average gas consumption difference between adjacent mapping points in each of the areas and the average gas consumption corresponding to each mapping point, to obtain multiple gas consumption sequences of the area, and different gas consumption sequences correspond to different gradients; secondly, the heating priority number of the area is determined according to the gas consumption of the users corresponding to the gas consumption sequences of each gradient of the area; finally, heat is supplied to each of the areas in turn according to the heating priority number and heating demand of each area.

[0016] In this way, the embodiment of the present invention can classify the gas consumption of users in different areas into multiple gas consumption sequences and determine the corresponding gradients. The heating priority number for each area is then determined based on the gas consumption of the users corresponding to each of the gradient gas consumption sequences. Finally, heating is provided to each area in a targeted manner based on the heating priority number and heating demand. This allows for timely adjustments to changes in heating demand from users in different areas, improving room temperature stability and heating flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 paying any creative work.

[0018] Figure 1 A flow chart of a method for managing urban centralized heating data provided by one embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a curve showing changes in gas consumption over time provided by one embodiment of the present invention;

[0020] Figure 3 A schematic structural diagram of a city centralized heating data management system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for managing urban centralized heating data according to the present invention, including its specific implementation, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable form.

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0023] The following describes in detail a method and system for managing urban centralized heating data provided by the present invention in conjunction with the accompanying drawings. Example

[0024] See also Figure 1 , which shows a flow chart of a method for managing urban centralized heating data provided by one embodiment of the present invention, including:

[0025] S101, obtaining gas consumption of different types of users in different areas of a city during different collection time periods.

[0026] Specifically, the city's centralized heating system is an energy system that uses a centralized approach to provide winter heating for cities or large areas, and is generally composed of heat sources, heating pipe networks, and users. In order to ensure the efficient operation of the centralized heating system, it is usually equipped with a central dispatch and control system, wherein the region in the embodiment of the present invention can be an area in the city. The length of the collection time period in the embodiment of the present invention can be determined according to actual conditions. In the embodiment of the present invention, the value is 1 week, and the collection cycle can generally be 4 months. Among them, the embodiment of the present invention obtains the weekly historical gas consumption of all users in different areas through the relevant gas supply company or energy management platform. After obtaining the data, due to the large scale of the data, it needs to be cleaned and the missing values ​​​​are filled to ensure its integrity and consistency. For example, if Figure 2 As shown, Figure 2 A graph showing the change of gas consumption over time is provided for one embodiment of the present invention. Generally speaking, users who enjoy heating in a certain area are divided into residential users, public institutions, commercial users, and industrial users. Different types of users have different corresponding weekly gas consumption. For example, the weekly gas consumption of commercial and industrial users is generally greater than that of residential users. Therefore, Figure 2The gas consumption of all users in the system presents a stepped distribution. Based on this, the embodiment of the present invention analyzes the stepped distribution characteristics of gas consumption to evaluate the heating demand of different areas and optimize the scheduling of heating resources.

[0027] S102: Map the average gas consumption of each type of users in each area during each collection time period into a coordinate system to obtain a plurality of mapping points.

[0028] Specifically, there may be multiple types of heating users in different areas, and different types of users have different weekly gas consumption. Therefore, in order to determine the heating gas demand in the corresponding area, it is necessary to analyze the corresponding gas consumption scale under different gradients for evaluation. Therefore, the embodiment of the present invention divides the gas consumption in different areas into gradients. Generally, the gas consumption of residential user terminals is relatively low and the number is large, so the corresponding data curves are generally densely distributed at a lower level, while other users such as public institutions have different gradients of gas consumption due to differences in volume, but the overall consumption is greater than that of residential user terminals, so the corresponding data curves are generally distributed above the data curves corresponding to residential user terminals.

[0029] Furthermore, the embodiment of the present invention first calculates the average consumption of a user in the collection period based on the historical gas consumption of a user in the area during the collection period, specifically using the following formula:

[0030] ;

[0031] In the above formula, Represents the average gas consumption of a user during the collection period. Representative Gas consumption during each collection period, Represents the number of collection time periods. Different users in this area will have an average gas consumption.

[0032] Furthermore, the average gas consumption of all users is mapped to a coordinate system, where the horizontal axis of the coordinate system is time and the vertical axis is the average gas consumption. Then the average gas consumption of different users corresponds to a data point on the y-axis, and thus multiple mapping points are obtained.

[0033] S103 : Segment the mapping point sequence in the coordinate system according to the average gas consumption difference between adjacent mapping points in each region and the average gas consumption corresponding to each mapping point to obtain multiple gas consumption sequences of the region.

[0034] Among them, different gas consumption sequences correspond to different gradients.

[0035] Specifically, the data points corresponding to the same gas consumption gradient are more concentrated, while the data points between different gas consumption gradients are more different. Therefore, the differences between any adjacent mapping points can be analyzed to perform gradient division.

[0036] Furthermore, when segmenting the mapping point sequence in the coordinate system, as an optional embodiment of the present invention, first use The function traverses the sequence of mapping points and calculates the difference between the average gas consumption of each adjacent mapping point to obtain the average gas consumption difference between each adjacent mapping point. It then selects the maximum difference value from the average gas consumption differences between each adjacent mapping point in the area. Finally, it determines the smaller of the average gas consumption corresponding to the adjacent mapping points corresponding to the maximum difference value, and takes a predetermined number of mapping points before the smaller one and performs segmentation.

[0037] Specifically, the embodiment of the present invention utilizes The function filters and segments all mapping point sequences in the coordinate system. The formula for the number of filtered mapping points is as follows:

[0038] ;

[0039] In the above formula, is the number of filtered mapping points, that is, the predetermined number. The parameter represents the maximum average gas consumption difference between any two adjacent mapping points. Parameter representation The function starts executing from the first mapping point in the sequence. Indicates the average gas consumption corresponding to the first mapping point. Indicates the average gas consumption corresponding to the i+1th mapping point. Indicates the average gas consumption corresponding to the i-th mapping point. In this embodiment, the mapping points are set to increase in sequence from bottom to top. That is, through Function to traverse the sequence, calculate the difference between any two adjacent mapping points, and then count the two corresponding points with the maximum difference. The smaller of the values ​​and the number of previous data points is Then, the sequence is segmented. This method is used to iteratively segment the two segments in the upper and lower order. Multiple gas consumption sequences of the region are obtained.

[0040] It is worth noting that during the cutting process, the iteration stop condition is defined at the same time. In the embodiment of the present invention, after the iteration stops, all the mapping points in each sequence are denser, that is, the average gas consumption of users in each gradient is more similar. According to experience, if the ratio of the maximum value of the average gas consumption difference in the sequence after a certain iteration to the average value of the average gas consumption difference is If sequence segmentation is not performed, each gas consumption sequence obtained at this time can be used as a gradient, and the corresponding area will obtain multiple gas consumption gradients for the user. Indicates the maximum value of the average gas consumption difference, Indicates the average value of the average gas consumption differences.

[0041] S104: Determine the heating priority number of the area according to the gas consumption of the users corresponding to the gas consumption sequences of each gradient in the area.

[0042] Specifically, for users in any region, different gas consumption gradients may have varying numbers of users. However, for this prefecture-level city, multiple regions may have similar gas consumption corresponding to a specific gradient. Subsequently, when assessing heating demand, heating resources can be centrally dispatched based on demand for these regions. The heating priority number refers to the heating priority of each region. A higher heating priority number indicates a higher priority for the corresponding region, and it should be prioritized for heating.

[0043] Furthermore, when determining the heating priority number of a region, as an optional embodiment of the present invention, first, based on the gas consumption of users corresponding to the gas consumption sequence of the region, a comprehensive gas consumption level corresponding to the gas consumption sequence of the region is determined; then, based on the gas consumption in the gas consumption sequence of any two regions with the same gradient number, a fluctuation similarity of the gas consumption of the two gradients in any two regions with the same gradient number is determined; then, based on the fluctuation similarity of the gas consumption of the two gradients in the two regions with the same gradient number and the comprehensive gas consumption level corresponding to the two gradients, a confidence level is determined that the two gradients are of the same category; and, in the two regions with the same gradient number, regions corresponding to two gradients with a confidence level greater than a first threshold are selected as belonging to the same first category; then, the regions with the same gradient number are divided into a block group, and a merging degree between any two gradients in different blocks is determined based on the first average gas consumption of users included in any two gradients in adjacent blocks; and regions corresponding to any two gradients with a merging degree less than or equal to a second threshold are determined as belonging to the same second category; finally, the heating priority number of the region is determined based on the first gas consumption of all users in the second category of the region in all gradients, the second gas consumption of all users in the first category of the region in the gradient, and the gradient value corresponding to the region.

[0044] Specifically, when calculating the comprehensive gas consumption level, an embodiment of the present invention, as an optional embodiment of the present invention, first calculates a second average gas consumption of the gas consumption of all users corresponding to the gas consumption sequence; then determines the maximum gas consumption and minimum gas consumption of users in the area, and a first difference between the maximum gas consumption and the minimum gas consumption; finally, calculates a first ratio between the first average gas consumption and the first difference to obtain the comprehensive gas consumption level corresponding to the gas consumption sequence.

[0045] The embodiment of the present invention specifically uses the following formula to calculate the comprehensive gas consumption level corresponding to the gas consumption sequence:

[0046] ;

[0047] In the above formula, It is the comprehensive gas consumption level corresponding to the gas consumption sequence corresponding to the gradient in a certain area. For this gradient Average gas consumption per user, The number of users included in this gradient. and are the maximum and minimum gas consumption of users in this area respectively. Indicates the second average gas consumption of all users corresponding to the gas consumption sequence of this gradient. The larger the value, the higher the comprehensive gas consumption level corresponding to this gradient. represents the extreme difference in gas consumption of all users in this area, then The larger the value, the higher the average gas consumption corresponding to this gradient accounts for the maximum gas consumption range of the entire area, indicating that the overall gas consumption of users within this gradient is greater and the comprehensive gas consumption level is greater.

[0048] Therefore, the corresponding comprehensive gas consumption level is calculated for all gradients in any region by the above embodiment of the present invention. Clustering can determine the category by the similarity of the comprehensive gas consumption levels of the corresponding gradients in different regions.

[0049] Furthermore, for any two regions with the same gradient number, as an optional embodiment of the present invention, determining the fluctuation similarity of gas consumption of two gradients in any two regions with the same gradient number based on the gas consumption in the gas consumption sequences in the any two regions with the same gradient number includes: respectively calculating a second ratio between the maximum average gas consumptions in the gas consumption sequences corresponding to the two gradients, and a third ratio between the minimum average gas consumptions in the gas consumption sequences corresponding to the two gradients; and calculating a first product between the second ratio and the third ratio to obtain the fluctuation similarity of gas consumption of the two gradients in the region.

[0050] Specifically, the embodiment of the present invention uses the following formula to calculate the fluctuation similarity of the gas consumption of two gradients in any two regions with the same gradient number:

[0051] ;

[0052] In the above formula, Represents the corresponding gradients in two regions with the same gradient number With gradient The similarity of gas consumption fluctuations. is the gradient The maximum average gas consumption. is the gradient Minimum average gas consumption. is the gradient The maximum average gas consumption. is the gradient The minimum average gas consumption in . Therefore It represents the difference in the fluctuation range of gas consumption of users included in the two gradients. The smaller the value, the more similar the fluctuation range of gas consumption of the two gradients is, and the greater the possibility of clustering into one category.

[0053] Furthermore, when determining the confidence that two gradients are of the same category, as an optional embodiment of the present invention, the absolute value of the second difference between the comprehensive gas consumption levels corresponding to the two gradients and the second product between the absolute value of the second difference and the fluctuation similarity are first calculated; then, the inverse of the second product is normalized to obtain the confidence that the two gradients are of the same category.

[0054] Specifically, the embodiment of the present invention uses the following formula to calculate two gradients as the confidence of the same category:

[0055] ;

[0056] In the above formula, is the gradient With gradient As the confidence of the same category. Represents the corresponding gradients in two regions with the same gradient number With gradient The similarity of gas consumption fluctuations. Represents the gradient With gradient The similarity of the comprehensive gas consumption levels. represents a normalization function, which is used to normalize the result to the range of [0, 1], which can be specifically, for example, maximum and minimum value normalization.

[0057] Furthermore, the first threshold value can be selected according to actual conditions. In the embodiment of the present invention, the first threshold value is set to 0.88. The corresponding two gradients can be considered to belong to the same category. Finally, for all regions with the same number of gradients, the corresponding confidence is calculated using the above method and classified.

[0058] It should be noted that the object of clustering here is the area where the gradient is located. Since there are multiple gradients in an area, clustering is performed on areas with the same gradient layer, that is, all areas corresponding to the same gradient layer are clustered based on similarity, using confidence as the basis for clustering judgment. So far, similarity clustering is performed on each gradient layer of all areas with the same gradient, and all areas with the same number of gradients are set as a block group. However, since it is impossible for all areas in a prefecture-level city to have the same gas consumption gradient, if there are multiple different types of user terminals in some areas, the corresponding gas consumption gradients will be more. The above method only obtains clustering results for all areas with the same gradient, and the scheduling of heating gas resources needs to be planned based on the needs of all areas in the entire prefecture-level city. Therefore, it is necessary to merge the clustering results of all different gradient areas to obtain the gas consumption scale gradient of the entire city.

[0059] Furthermore, this embodiment of the present invention uses a step-by-step merging method, starting with the first-level block group with the fewest gradients. The similarity between the gas consumption of a certain gradient layer and the gas consumption of each gradient layer in the next level of gradient is analyzed and merged. This process is then repeated, gradually merging blocks with smaller gradients into blocks with larger gradients. This results in a city-wide gradient clustering of gas consumption.

[0060] Furthermore, when determining the degree of merging between any two gradients in different block groups, as an optional embodiment of the present invention, the absolute value of the third difference between the first average gas consumption of users included in any two gradients is calculated to obtain the degree of merging between any two gradients in different block groups.

[0061] Specifically, the embodiment of the present invention can use the following formula to calculate the merging degree between any two gradients:

[0062] ;

[0063] In the above formula, For the Gradients in block groups With the Gradients in block groups degree of merger. For the Gradients in block groups The first average gas consumption of all users included in . For the Gradients in block groups The first average gas consumption of all users included in . Represents the normalization function, which is used to Perform normalization processing.

[0064] It should be noted that i in this scheme specifically represents the order and has no physical meaning. The parameter that truly represents the physical meaning is For example, if i is 1, it means the first block group. The formula represents the average gas consumption of the first mapping point.

[0065] Furthermore, the second threshold value may be set to 0.2. In the embodiment of the present invention, the second threshold value is recorded as . Select here The corresponding two gradients are merged, that is, the merged object is the area corresponding to this gradient, that is, the areas corresponding to any two gradients are divided into the same second category.

[0066] Furthermore, when allocating heating resources, priority is given to heating for residents' daily lives, followed by heating for production. The gas consumption at residential user terminals is generally at a lower gradient, while the gas consumption at industrial and commercial institutions is at a higher gradient. Therefore, for any region, the lower its gradient distribution in the city's gas consumption scale gradient and the larger the scale of the clustering result, the higher its priority for heating demand, and vice versa. Therefore, as an optional embodiment of the present invention, determining the heating priority ordinal number of a region based on the first gas consumption of all users in the second category to which the region belongs in all gradients, the second gas consumption of all users in the first category to which the region belongs in the gradient, and the gradient value corresponding to the region includes: calculating the fourth ratio between the second gas consumption and the first gas consumption, and the inverse of the superposition value of the gradient values ​​of all gradients corresponding to the region; calculating the third product between the fourth ratio and the inverse of the superposition value, and taking the average value of the third product corresponding to each gradient to obtain the heating priority ordinal number of the region.

[0067] Specifically, the embodiment of the present invention uses the following formula to calculate the heating priority number:

[0068] ;

[0069] In the above formula, Represents the heating priority number of a certain area. It is the first gas consumption of all users in the cluster category to which this region belongs in all gradients of this region, that is, the first gas consumption of all users in the second category to which the region belongs in all gradients. For this area in The gas consumption of all users in the cluster category to which the layer gradient belongs, that is, the second gas consumption of all users in the first category to which the gradient belongs. Represents the gradient value corresponding to a certain gradient in this area, Represents the superposition of the gradient values ​​corresponding to all gradients in the region. Represents this region The ratio of the scale of the cluster category in the layer gradient to the scale of all gradients. The larger the value, the larger the gas consumption scale in the lower gradient location in this area. The larger the value of the heating priority ordinal number, the higher the corresponding priority. It represents the distribution of this area in different gradients. The smaller the value, the larger the gradient value in this area. The higher the gradient distribution, the smaller the heating priority ordinal value, and the lower the corresponding priority.

[0070] So far, the corresponding heating priority is determined for any area through the above method, and subsequently the heating gas resources can be centrally allocated based on the size of the heating priority sequence number.

[0071] S105: Supply heat to each area in turn according to the heating priority number and heating demand of each area.

[0072] Specifically, in embodiments of the present invention, different heating strategies can be employed for zones with different priorities. As an optional embodiment of the present invention, sequentially supplying heat to zones based on their heating priority numbers and heating demand includes: first delivering gas to the main pipeline of zones with higher heating priority numbers, and sequentially supplying heat to users in those zones based on the heating demand of those zones; and after the heating demand of the zones with higher heating priority numbers is met, allocating the remaining gas to the zones with the next higher heating priority number.

[0073] For high-priority areas, where most users are residents with high gas demand, gas can be preferentially transported from the source to the corresponding main pipeline in this area, and then to the main pipelines in other areas. Once the gas demand in this area is met, the remaining gas can be redistributed to other areas with relatively lower priorities, thereby coordinating the gas demand in different areas and regulating resources. This reduces operating costs, enhances user satisfaction, and improves system stability.

[0074] This embodiment of the present invention divides the gas consumption of users in different areas into multiple gas consumption sequences and determines corresponding gradients. It then determines the heating priority number for each area based on the gas consumption of users corresponding to each gradient of the gas consumption sequence. Finally, heating is provided to each area in a targeted manner based on the heating priority number and heating demand. This allows for timely adjustments to changes in heating demand from users in different areas, improving room temperature stability and heating flexibility.

[0075] Example 2:

[0076] Corresponding to the urban centralized heating data management method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention also provides an urban centralized heating data management system, which is used to execute the above urban centralized heating data management method. Figure 3 A schematic diagram of the structure of a city centralized heating data management system provided by an embodiment of the present invention is shown as follows: Figure 3 The urban centralized heating data management system may have relatively large differences due to different configurations or performances, and may include one or more processors 301 and memory 302. The memory 302 is used to store computer programs that can be run on the processor 301. The processor 301 is used to execute the programs stored in the memory 302 to achieve the above Figure 1 The various steps in the method embodiment are described in detail. Memory 302 may be either transient or persistent storage. The application stored in memory 302 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for the urban centralized heating data management system.

[0077] Furthermore, the processor 301 can be configured to communicate with the memory 302 to execute a series of computer-executable instructions in the memory 302 on the urban centralized heating data management system. The urban centralized heating data management system can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0078] Specifically in this embodiment, the urban centralized heating data management system includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described again here.

[0079] It should be noted that the urban centralized heating data management system provided by the embodiment of the present invention and the urban centralized heating data management method provided by the embodiment of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned urban centralized heating data management method, and has the same or similar beneficial effects, and the repetitions will not be repeated.

[0080] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for managing urban centralized heating data, characterized in that: The urban centralized heating data management method includes: Obtain gas consumption of different types of users in different areas of the city during each collection time period; Mapping the average gas consumption of each type of user in each area during each collection time period into a coordinate system to obtain a plurality of mapping points; Segmenting the mapping point sequence in the coordinate system according to the average gas consumption difference between adjacent mapping points in each of the regions and the average gas consumption corresponding to each mapping point, to obtain multiple gas consumption sequences for the region, where different gas consumption sequences correspond to different gradients; Determine the heating priority number of the area according to the gas consumption of users corresponding to the gas consumption sequence of each gradient in the area; Supplying heat to each of the areas in sequence according to the heating priority number and heating demand of each area; The determining of the heating priority number of the area according to the gas consumption of the users corresponding to the gas consumption sequence of each gradient in the area includes: determining a comprehensive gas consumption level corresponding to the gas consumption sequence in the area according to the gas consumption of users corresponding to the gas consumption sequence in the area; Determining the fluctuation similarity of the gas consumption of two gradients in any two regions with the same gradient number according to the gas consumption in the gas consumption sequence in the regions with the same gradient number; Determining the confidence level that the two gradients are of the same category based on the similarity of fluctuations in gas consumption of the two gradients in the two regions with the same gradient number and the comprehensive gas consumption levels corresponding to the two gradients; Selecting two regions with the same gradient number and corresponding regions of the two gradients with confidence greater than the first threshold to belong to the same first category; Divide the area with the same number of gradients into a block group, and determine the degree of merging between any two gradients in different blocks according to the first average gas consumption of users included in any two gradients in adjacent blocks; Determining that regions corresponding to any two gradients whose merging degree is less than or equal to a second threshold belong to the same second category; Obtaining the first gas consumption of all users in the second category of all gradients in the area, the second gas consumption of all users in the first category of the gradient in the area, and the gradient value corresponding to the area, and calculating a fourth ratio between the second gas consumption and the first gas consumption, and the inverse of the superposition value of the gradient values ​​of all gradients corresponding to the area; The third product between the fourth ratio and the inverse of the superposition value is calculated, and the average value of the third products corresponding to each gradient is obtained to obtain the heating priority number of the area.

2. The urban centralized heating data management method according to claim 1 is characterized in that: The step of dividing the mapping point sequence in the coordinate system according to the average gas consumption difference between adjacent mapping points in each area and the average gas consumption corresponding to each mapping point to obtain multiple gas consumption sequences for the area includes: use The function traverses the mapping point sequence, calculates the difference between the average gas consumption of each adjacent mapping point, and obtains the average gas consumption difference between each adjacent mapping point; Selecting a maximum difference value from the average gas consumption differences between adjacent mapping points in the area; The smaller of the average gas consumptions corresponding to adjacent mapping points corresponding to the maximum difference value is determined, and a predetermined number of mapping points before the smaller one are selected and segmented.

3. The urban centralized heating data management method according to claim 1 is characterized in that: Determining the comprehensive gas consumption level corresponding to the gas consumption sequence in the area according to the gas consumption of the users corresponding to the gas consumption sequence in the area includes: Calculating a second average gas consumption of the gas consumption of all users corresponding to the gas consumption sequence; Determining a maximum gas consumption and a minimum gas consumption of users in the area, and a first difference between the maximum gas consumption and the minimum gas consumption; A first ratio between the first average gas consumption and the first difference is calculated to obtain a comprehensive gas consumption level corresponding to the gas consumption sequence.

4. The urban centralized heating data management method according to claim 1 is characterized in that: The step of determining the fluctuation similarity of the gas consumption of two gradients in any two regions with the same gradient number according to the gas consumption in the gas consumption sequence in the regions with the same gradient number includes: respectively calculating a second ratio between the maximum average gas consumptions in the gas consumption sequences corresponding to the two gradients, and a third ratio between the minimum average gas consumptions in the gas consumption sequences corresponding to the two gradients; A first product between the second ratio and the third ratio is calculated to obtain fluctuation similarity of the gas consumption of the two gradients in the region.

5. The urban centralized heating data management method according to claim 1 is characterized in that: The step of determining the confidence level of the two gradients as belonging to the same category based on the similarity of gas consumption fluctuations of the two gradients in the two regions with the same gradient number and the comprehensive gas consumption levels corresponding to the two gradients includes: calculating an absolute value of a second difference between the comprehensive gas consumption levels corresponding to the two gradients, and a second product of the absolute value of the second difference and the fluctuation similarity; The reciprocal of the second product is normalized to obtain the two gradients as confidences of the same category.

6. The urban centralized heating data management method according to claim 1 is characterized in that: The step of determining the merging degree between any two gradients in different block groups according to the first average gas consumption of users included in any two gradients in adjacent block groups includes: The absolute value of the third difference between the first average gas consumption of users included in any two gradients is calculated, and the absolute value of the third difference is normalized to obtain the merging degree between any two gradients in the different block groups.

7. The urban centralized heating data management method according to claim 1 is characterized in that: The step of sequentially supplying heat to each of the areas according to the heating priority number and heating demand of each area comprises: First, the gas is transported to the main pipeline of the area with a high heating priority sequence, and heat is supplied to users in the area in sequence based on the heating demand of the area with a high heating priority sequence; After the heating demand of the area with a higher heating priority number is met, the remaining gas is distributed to the area with the next heating priority.

8. A city centralized heating data management system, characterized by: The urban centralized heating data management system includes: a processor and a memory; wherein the memory is used to store computer programs that can be run on the processor; the processor is used to execute the program stored in the memory to implement the steps of the urban centralized heating data management method as described in any one of claims 1-7.

Images