Heat supply dispatching automation implementation system and method based on data visualization

Abstract

The invention relates to the technical field of data processing, and discloses a heat supply scheduling automation realization system and method based on data visualization, and the system comprises a user heat supply amount information processing module, a heat supply scheduling and monitoring processing module, and a heat supply scheduling monitoring feedback module. Real-time heat consumption parameters of a user side are scientifically collected on line through a heat meter, and reliable data are provided for accurately matching the standard heat supply amount of the user side. Different standard heat supply parameters are scientifically preset and stored, an intelligent identification algorithm and real-time heat consumption parameters of the user side are combined to carry out heat consumption numerical analysis, the standard heat supply parameters needed by the user side are efficiently and accurately analyzed, and the heat supply scheduling precision and efficiency of the heat supply scheduling system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a system and method for realizing automatic heating scheduling based on data visualization. Background Art

[0002] The urban heating dispatching system is an advanced heating management system with comprehensive capabilities. It includes multiple capabilities such as perception, memory, logic, identification, calculation, analysis, judgment, and decision. The dispatching control center can realize the full network control, adjustment, calculation, and analysis of the operating parameters of the entire centralized heating network, and can implement dynamic intelligent balance adjustment of the centralized heating system. The urban heating dispatching system is an important part of the smart city. Traditional heating is based on a way of transmitting heat to thousands of households by burning hot water through boilers. With the development of heating pipe networks, heating systems are becoming larger and larger. Such a large heating system needs a good management platform to deliver the heat needed by users to their homes and realize precise heating, that is, the ideal "heating on demand and heating on demand"; however, the existing urban heating dispatching system cannot dynamically and accurately dispatch the user's heating according to the real-time heat consumption of the user, resulting in a mismatch between heat supply and demand at the user end; at the same time, the existing urban heating dispatching system cannot realize the dynamic and monitoring display of the heating status of different locations in the heating system.

[0003] A Chinese invention patent with announcement number CN113757784B discloses a scheduling control method and related devices for a heating system. Based on first and second information of heating and in combination with objective functions, constraints and operating efficiency, the adjustment amount of heating power is calculated to achieve efficient operation of the heating system. However, the above technical solution cannot dynamically analyze the heating parameters of the heating scheduling system based on the real-time heat consumption at the user end, nor can it visually monitor the heating status of different locations of the heating system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the problem that the existing urban heating scheduling system cannot dynamically and accurately schedule the heating supply of the user end according to the real-time heat consumption of the user end, resulting in a mismatch between heat supply and demand at the user end; at the same time, the existing urban heating scheduling system cannot realize the dynamic monitoring and display of the heating status of different locations of the heating system, the above scientific collection of real-time heat consumption at the user end, efficient and accurate analysis of the standard heating supply at the user end, and intelligent analysis of the real-time heating supply scheduling information at the user end are achieved; the purpose of scientifically selecting monitoring points of the heating system, intelligently analyzing the heating status of the heating monitoring points and visually feedback is achieved.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical solution: a method for realizing automatic heating scheduling based on data visualization, the method comprising the following steps:

[0008] S1, collect real-time heat data of the user end;

[0009] S2, performing real-time heat supply matching processing on the user side of the heating scheduling system according to the real-time heat consumption data on the user side and different standard heat supply data, and generating real-time standard heat supply data on the user side;

[0010] S3, constructing user-side real-time heating supply scheduling data according to the user-side real-time standard heating supply data;

[0011] S4, performing heating monitoring point object screening processing based on the preset monitoring point data of the heating system, and generating heating system status analysis monitoring point data;

[0012] S5, collecting real-time heating data of the monitoring point and performing abnormal heating state recognition processing on the standard heating data of the monitoring point, and constructing real-time abnormal heating state recognition result data of the monitoring point;

[0013] S6, performing abnormal heating state recognition result data of the monitoring point and the heating system state analysis monitoring point data on the heating system to identify the abnormal heating state of the heating system and generate abnormal heating state recognition and monitoring position combination data of the heating system;

[0014] S7. Construct the heat supply dispatch system heat supply dispatch monitoring feedback data and push the feedback to the heat supply dispatch management center.

[0015] Preferably, the operation steps of collecting the real-time heat data of the user end are as follows:

[0016] S11. Collect the real-time heat consumption data of a specific user end in the heating system online through a heat meter and generate the real-time heat consumption data q of the user end, where the unit of q is kilowatt. The user end includes any one of a single residential area, a single commercial area, a single school, and a single government unit.

[0017] The present invention collects the real-time heat consumption parameters of the user end online and scientifically through the heat meter, so as to provide reliable data for accurately matching the standard heat supply of the user end.

[0018] Preferably, the operation steps of matching the real-time heat supply of the user end of the heating scheduling system with the real-time heat supply data of the user end and generating the real-time standard heat supply data of the user end are as follows:

[0019] S21, establish different standard heating data sets A = (a 1 ,…,a m ,…,aμ ), m=1,2,3,…,μ; where a m represents the mth different standard heating data, a m =[q m1 ,q m2 ], where [q m1 ,q m2 ] represents the user end real-time heat data interval, when the user end real-time heat data q∈[q m1 ,q m2 ], the user end real-time heat data q corresponds to the different standard heat supply data a m ;q m1 and q m2 It means [q m1 ,q m2 ] represents the left and right end values, μ represents the maximum value of the number of different standard heating data types, a m The unit is kilowatt;

[0020] S22, compare the user end real-time heat consumption data q with the different standard heat supply data a in the different standard heat supply data set A. m According to the user's real-time heat value matching, search for the different standard heat supply data a corresponding to the user's real-time heat data q m , execute the search to find the different standard heating data a m The specific steps are as follows:

[0021] S221, initialization, update algorithm maximum iteration number T;

[0022] S222, exploration stage, this stage is mainly to search for different standard heating data a in the space of different standard heating data set A m Perform a global search and search for different standard heat supply data a that matches the user's real-time heat data q m , the search mathematical formula is Where χ represents a random number in the interval (0,1), and χ determines the probability that the heat supply searching gorilla individual chooses the migration mechanism to an unknown location; F(t+1) is the number of different standard heat supply data a that matches the user-side real-time heat supply data q that the heat supply searching gorilla individual searches for in the search space of different standard heat supply data sets A in the t+1th iteration. m Candidate positions of σ and Denote the upper and lower boundaries of the heat supply search gorilla in the search space of different standard heat supply data sets A; F(t) represents the t-th iteration heat supply search gorilla individual searching for different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A. m Candidate position of F r (t) represents the t-th iteration of the heat supply search gorilla population randomly selected heat supply search gorilla individual search in the different standard heat supply data set A search space to search for different standard heat supply data a that matches the user end real-time heat supply data q m Candidate position of 1 、r 2 、r 3 , rand represent random numbers in the interval (0,1) of the algorithm iteration update value; τ, υ, ξ represent the algorithm iteration update adjustment factors; at the end of the exploration phase, calculate the fitness values ​​of all F(t+1) heat supply search gorilla individuals, if the fitness value satisfies F(t+1)>F(t), then use the F(t+1) heat supply search gorilla individual to replace the F(t) heat supply search gorilla individual, the optimal individual heat supply search gorilla generated in the exploration phase is regarded as the silverback heat supply search gorilla, that is, search for different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A m location;

[0023] S223, development stage, the development stage adopts two behaviors: following the silverback heat supply to search for gorillas and competing for the heat supply of adult females to search for gorillas;

[0024] S2231, follow the silverback heat supply to search for the gorilla, if The heat supply searching gorilla individual chooses to follow the mechanism of the silverback heat supply searching gorilla, and searches for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m , where τ represents the algorithm iteration update adjustment factor, The weight factor of the heat-seeking gorilla population choosing to follow the silverback heat-seeking gorilla and competing with the adult female heat-seeking gorilla; the behavior simulation calculation formula for choosing to follow the silverback heat-seeking gorilla is F(t+1)=υ×ω×(F(t)-F best )+F(t), where F best It indicates that the silverback heat supply search gorilla individual searches for different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A. mThe best candidate location; ω represents the control factor that simulates the behavior of the gorilla searching for heat by following the silverback;

[0025] S2232, competition for adult females, if The heat supply searching gorilla mechanism of following the competition of adult females is selected, and the heat supply searching gorilla individuals entering adolescence compete with other male heat supply searching gorillas in the problem of selecting adult females and search for different standard heat supply data a that matches the real-time heat supply data q of the user end in the search space of different standard heat supply data sets A. m , the competition behavior simulation calculation formula is: where φ is used to simulate the impact force of a male heat-seeking gorilla, is the coefficient vector of the degree of violence in the conflict; at the end of the development stage, the fitness values ​​of all F(t+1) heat supply search gorilla individuals are calculated. If the fitness value satisfies F(t+1)>F(t), the F(t+1) heat supply search gorilla individual is used to replace the F(t) heat supply search gorilla individual. The optimal male heat supply search gorilla generated in this stage is regarded as the silverback heat supply search gorilla, that is, different standard heat supply data a matching the real-time heat supply data q of the user end is searched in the search space of different standard heat supply data sets A. m ;

[0026] S224: satisfy the maximum number of iterations and output different standard heat supply data a that matches the user's real-time heat data q m ;

[0027] S23, the different standard heating data output in step S224 m Generate real-time standard heating data a at the user end through data identification shishi , a shishi The unit is kilowatt.

[0028] The present invention scientifically presets and stores different standard heating parameters, combines the elephant optimization algorithm with the real-time heating parameters of the user end to perform numerical analysis of heat consumption, and efficiently and accurately analyzes the required standard heating parameters of the user end, thereby achieving the effect of improving the heating scheduling accuracy and efficiency of the heating scheduling system.

[0029] Preferably, the operation steps of constructing the user-side real-time heating supply scheduling data according to the user-side real-time standard heating supply data are as follows:

[0030] S31, the user end real-time standard heating data a shishi The program editing software is used to convert the heating scheduling system to identify the heating scheduling instructions and build real-time heating scheduling data for the user end. diaodu, the program editing software includes any one of Visual Studio Code, PyCharm, and IntelliJ IDEA.

[0031] The present invention achieves the effect of autonomously translating the real-time standard heating supply at the user end into the real-time heating supply scheduling control instruction at the user end by accurately generating the real-time heating supply scheduling control instruction parameters at the user end.

[0032] Preferably, the steps of performing the screening process of the heating monitoring point objects based on the preset monitoring point data of the heating system to generate the monitoring point data for the state analysis of the heating system are as follows:

[0033] S41, when the user terminal real-time heating scheduling data a diaodu When the construction is completed, the preset monitoring point data set B of the heating system is established = (b 1 ,…,b n ,…,b ν ), n=1,2,3,…,ν; where b n represents the nth preset monitoring point data of the heating system, ν represents the maximum number of preset monitoring point data of the heating system; b n =(x n ,y n ,z n ), where x n Indicates the horizontal coordinate corresponding to the preset monitoring point data of the nth heating system, y n Indicates the vertical coordinate corresponding to the data of the preset monitoring point of the nth heating system, z n Indicates the vertical coordinate corresponding to the nth preset monitoring point data of the heating system, wherein the preset monitoring point data of the heating system represents the monitoring coordinate point data used to monitor the abnormal heating state of the heating pipe network in the heating system;

[0034] S42, using a bidirectional search algorithm to randomly select and filter the preset monitoring point data set B of the heating system to obtain the preset monitoring point data set B of the heating system. n And generate the heating system status analysis monitoring point data set B'=(b' n1 ,…,b' n2 ), 1≤n1≤n≤n2≤ν, where b' n1 represents the data of the n1th heating system status analysis monitoring point, b' n2 Represents the data of the n2th heating system status analysis monitoring point.

[0035] The present invention performs intelligent random selection of monitoring points of a heating pipe network of a heating scheduling system through a bidirectional search algorithm, thereby achieving the effect of scientific sampling of monitoring point objects of the heating pipe network of the heating scheduling system.

[0036] Preferably, the steps of collecting the real-time heating data of the monitoring point and performing abnormal heating state recognition processing on the monitoring point with the standard heating data of the monitoring point to construct the real-time abnormal heating state recognition result data of the monitoring point are as follows:

[0037] S51, collecting the state analysis monitoring point data of the heating system online through industrial sensors b' n1 to b' n2 The real-time heating data of the monitoring points of the heating pipe network in the corresponding heating system is generated, and the real-time heating data set of the monitoring points is generated C = (c n1 ,…,c n2 ), where c n1 and c n2 Respectively represent the heating system status analysis monitoring point data b' n1 and b' n2 The corresponding real-time heating data of the monitoring point, the real-time heating data of the monitoring point includes any one of the real-time supply pressure data, real-time back pressure data, real-time temperature data, real-time return temperature data, and real-time flow data of the heating pipe network monitoring point;

[0038] S52, establish the monitoring point standard heating data interval D = [d 1 ,…,d 2 ],d 1 and d 2 Respectively represent the end value of the left monitoring point standard heating data and the end value of the right monitoring point standard heating data in the monitoring point standard heating data interval D, wherein the monitoring point standard heating data includes any one of the standard supply pressure data, standard back pressure data, standard temperature data, standard return temperature data, and standard flow data of the heating network monitoring point;

[0039] S53, using a unified cost search algorithm to search the real-time heating data set C of the monitoring point in the real-time heating data set C of the monitoring point n1 to c n2 According to the monitoring point number, the standard heating data end value d of the left monitoring point in the standard heating data interval D of the monitoring point is in order 1 and the standard heating data end value d of the right monitoring point 2 Perform heating value comparison, and build a real-time heating abnormal state recognition result data set E of the monitoring point based on the heating value comparison result. n1 ,…,e n2 ), where e n1 Indicates the monitoring point data b' for the heating system status analysis n1 The corresponding monitoring point real-time heating abnormal state recognition result data, e n2 Indicates the monitoring point data b' for the heating system status analysis n2 Real-time abnormal heating status identification result data of the corresponding monitoring points;

[0040] When c n1 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n1 No abnormality;

[0041] when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n1 is abnormal;

[0042] When c n2 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n2 No abnormality;

[0043] when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n2 is abnormal.

[0044] The present invention uses industrial sensors to dynamically collect real-time heating parameters of monitoring points and combines a unified cost search algorithm with standard heating parameters of monitoring points to perform intelligent evaluation of abnormal heating states of monitoring points, thereby achieving the effect of intelligently and accurately identifying the result parameters of real-time abnormal heating states of monitoring points.

[0045] Preferably, the operation steps of performing abnormal heating state identification processing of the heating system based on the real-time abnormal heating state identification result data of the monitoring point and the heating system state analysis monitoring point data to generate abnormal heating state identification and monitoring position combination data of the heating system are as follows:

[0046] S61, the monitoring point real-time heating abnormal state recognition result data set E of the monitoring point real-time heating abnormal state recognition result data set E n1 To e n2 The heating system state analysis monitoring point data b' in the heating system state analysis monitoring point data set B' are ordered according to the monitoring point numbers. n1 and b' n2 Combine the data of the heating pipe network monitoring points in the heating system and the corresponding abnormal heating state identification results to generate the heating system abnormal heating state identification and monitoring position combination data set G = (g n1 ,…,g n2 ), where g n1 Indicates e n1 and b' n1 The corresponding heating system abnormal state recognition and monitoring location combined data, gn2 Indicates e n2 and b' n2 The corresponding heating system abnormal heating state identification and monitoring location combined data.

[0047] The present invention combines the heating status recognition results of the heating pipe network and the coordinate parameters of the monitoring points to generate the combined parameters of the abnormal heating status recognition and monitoring position of the heating system, so as to achieve the effect of accurately positioning the heating status of the heating pipe network in the heating scheduling system.

[0048] Preferably, the operation steps of constructing the heat supply dispatching system heat supply dispatching monitoring feedback data and pushing the feedback to the heat supply dispatching management center are as follows:

[0049] S71, the user end real-time heating scheduling data a diaodu Combined with the abnormal state recognition and monitoring position combination data set G of the heating system to generate the heating scheduling monitoring feedback data K of the heating scheduling system = (G, a diaodu );

[0050] S72, pushing the heating dispatch monitoring feedback data K of the heating dispatch system to the dispatch management center through the Internet of Things communication network and using a display device for feedback display, wherein the display device includes a display screen and a projection device.

[0051] The present invention achieves the effect of accurate and intuitive result feedback of the heating scheduling information at the user end of the heating scheduling system and the heating status of the heating pipe network monitoring point by scientifically constructing the heating scheduling monitoring feedback parameters of the heating scheduling system and pushing them to the heating scheduling management center through the Internet of Things and visually displaying the feedback through the display device.

[0052] A heating scheduling automation realization system based on data visualization, which is used to realize the heating scheduling automation realization method based on data visualization, and the system includes a user heating amount information processing module, a heating scheduling and monitoring processing module, and a heating scheduling monitoring feedback module;

[0053] The user heat supply information processing module includes a user-side heat collection unit, a different standard heat supply storage unit, and a user-side heat supply matching unit;

[0054] The user-end heat collection unit collects the user-end real-time heat data through the heat meter; the different standard heat storage unit is used to store different standard heat data; the user-end heat matching unit performs real-time heat matching processing of the user end of the heating scheduling system according to the user-end real-time heat data and different standard heat data, and generates real-time standard heat data of the user end;

[0055] The heating scheduling and monitoring processing module includes a user-side heating scheduling information generation unit, a heating system preset monitoring point storage unit, a heating system monitoring point screening unit, a heating system monitoring point heating parameter collection unit, a heating system monitoring point heating parameter collection unit, a heating system monitoring point standard heating parameter storage unit, a heating system monitoring point heating anomaly identification unit, and a monitoring point heating anomaly identification and location parameter generation unit;

[0056] The user-side heating scheduling information generation unit constructs the user-side real-time heating scheduling data according to the user-side real-time standard heating data; the heating system preset monitoring point storage unit is used to store the heating system preset monitoring point data; the heating system monitoring point screening unit performs heating monitoring point object screening processing based on the heating system preset monitoring point data to generate the heating system state analysis monitoring point data; the heating system monitoring point heating parameter acquisition unit collects the monitoring point real-time heating data through industrial sensors; the heating system monitoring point standard heating parameter storage unit is used to store the monitoring point standard heating data; the heating system monitoring point heating abnormality identification unit performs the monitoring point heating abnormality state identification processing on the monitoring point real-time heating data and the monitoring point standard heating data to construct the monitoring point real-time heating abnormality state identification result data; the monitoring point heating abnormality identification and position parameter generation unit performs the heating abnormality spatial position identification processing on the heating system based on the monitoring point real-time heating abnormality state identification result data and the heating system state analysis monitoring point data to generate the heating abnormality state identification and monitoring position combination data of the heating system;

[0057] The heating scheduling monitoring feedback module includes a heating scheduling system heating scheduling monitoring data processing unit and a heating scheduling system heating scheduling monitoring result feedback unit;

[0058] The heating scheduling monitoring data processing unit of the heating scheduling system constructs the heating scheduling monitoring feedback data of the heating scheduling system based on the real-time heating scheduling data of the user end and the abnormal heating state identification and monitoring position combination data of the heating system; the heating scheduling monitoring result feedback unit of the heating scheduling system pushes the heating scheduling monitoring feedback data of the heating scheduling system to the heating scheduling management center and displays the feedback through the display device.

[0059] (III) Beneficial effects

[0060] The present invention provides a system and method for realizing automatic heating scheduling based on data visualization, which has the following beneficial effects:

[0061] 1. Scientifically collect the real-time heat consumption parameters of the user end online through the heat meter to provide reliable data for accurately matching the standard heating supply of the user end; scientifically preset and store different standard heating supply parameters and combine intelligent recognition algorithms with the real-time heat consumption parameters of the user end to perform heat consumption numerical analysis, efficiently and accurately analyze the required standard heating supply parameters of the user end, and improve the heating scheduling accuracy and efficiency of the heating scheduling system.

[0062] 2. Accurately generate real-time heating amount dispatching control instruction parameters at the user end; use intelligent search algorithm to intelligently and randomly select the heating pipe network monitoring points of the heating dispatching system, and realize scientific sampling of the heating pipe network monitoring point objects of the heating dispatching system; use industrial sensors to dynamically collect real-time heating parameters of monitoring points, combined with intelligent search algorithm and standard heating parameters of monitoring points to intelligently evaluate the abnormal heating status of monitoring points, and intelligently and accurately identify the real-time abnormal heating status identification result parameters of monitoring points, so as to realize scientific monitoring of the heating status of the heating pipe network of the heating dispatching system; combine the heating status identification results of the heating pipe network and the coordinate parameters of the monitoring points, generate the combined parameters of the abnormal heating status identification and monitoring position of the heating system, and realize accurate positioning of the heating status of the heating pipe network in the heating dispatching system.

[0063] 3. By scientifically constructing the heating dispatching monitoring and feedback parameters of the heating dispatching system, data support is provided for the visual feedback of the heating dispatching and heating monitoring information of the heating dispatching system; the heating dispatching monitoring and feedback parameters of the heating dispatching system are pushed to the heating dispatching management center through the Internet of Things and visualized and fed back through display devices, thereby realizing accurate and intuitive feedback of the heating dispatching information on the user side of the heating dispatching system and the heating status of the heating pipe network monitoring points, thereby improving the heating quality and safety of the heating dispatching system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of the modules of the heating scheduling automation realization system based on data visualization provided by the present invention;

[0065] Figure 2 A flow chart of a method for realizing automatic heating scheduling based on data visualization provided by the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] The embodiments of the system and method for realizing automatic heating scheduling based on data visualization are as follows:

[0068] Embodiment 1:

[0069] See also Figure 1 - Figure 2 , a method for realizing automatic heating scheduling based on data visualization, the method comprises the following steps:

[0070] S1, collect real-time heat data of the user end;

[0071] S2, performing real-time heat supply matching processing on the user side of the heating scheduling system according to the real-time heat consumption data on the user side and the different standard heat supply data, and generating real-time standard heat supply data on the user side;

[0072] S3, constructing user-side real-time heating supply scheduling data based on user-side real-time standard heating supply data;

[0073] S4, performing heating monitoring point object screening processing based on the preset monitoring point data of the heating system, and generating heating system status analysis monitoring point data;

[0074] S5, collecting real-time heating data of the monitoring point and performing abnormal heating state recognition processing on the standard heating data of the monitoring point, and constructing real-time abnormal heating state recognition result data of the monitoring point;

[0075] S6, based on the real-time abnormal heating state recognition result data of the monitoring point and the heating system state analysis monitoring point data, perform abnormal heating state recognition and monitoring position identification processing of the heating system to generate abnormal heating state recognition and monitoring position combination data of the heating system;

[0076] S7. Construct the heat supply dispatching system heat supply dispatching monitoring feedback data and push the feedback to the heat supply dispatching management center.

[0077] For further information, see Figure 1 - Figure 2 The steps to collect real-time heat data from the user end are as follows:

[0078] S11. Collect the real-time heat consumption data of specific user terminals in the heating system online through heat meters and generate real-time heat consumption data q of the user terminals, where the unit of q is kilowatts. The user terminals include any one of a single residential area, a single commercial area, a single school, and a single government unit.

[0079] The steps for matching the real-time heat supply of the user end of the heating scheduling system with the real-time heat supply data of the user end and generating the real-time standard heat supply data of the user end are as follows:

[0080] S21, establish different standard heating data sets A = (a 1 ,…,a m,…,a μ ), m=1,2,3,…,μ; where a m Indicates the mth different standard heating data, a m =[q m1 ,q m2 ], where [q m1 ,q m2 ] represents the user-side real-time heat data interval. When the user-side real-time heat data q∈[q m1 ,q m2 ], the user-side real-time heat data q corresponds to different standard heat supply data a m ;q m1 and q m2 It means [q m1 ,q m2 ] represents the left and right end values, μ represents the maximum value of the number of different standard heating data types, a m The unit is kilowatt;

[0081] S22, compare the user's real-time heat consumption data q with the different standard heat supply data a in the different standard heat supply data set A. m According to the user's real-time heat value matching, search for different standard heat supply data a corresponding to the user's real-time heat data q m , execute the search to find different standard heating data a m The specific steps are as follows:

[0082] S221, initialization, update algorithm maximum iteration number T;

[0083] S222, exploration stage, this stage is mainly to search for different standard heating data a in the space of different standard heating data set A m Perform a global search and search for different standard heat supply data a that matches the user's real-time heat data q m , the search mathematical formula is Where χ represents a random number in the interval (0,1), and χ determines the probability that the heat supply searching gorilla individual chooses the migration mechanism to an unknown location; F(t+1) is the number of different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A in the t+1th iteration. m Candidate positions of σ and Denote the upper and lower boundaries of the heat supply search gorilla in the search space of different standard heat supply data sets A; F(t) represents the t-th iteration of the heat supply search gorilla individual searching for different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A. mCandidate position of F r (t) represents the t-th iteration of the heat supply search gorilla population. The randomly selected heat supply search gorilla individual searches for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m Candidate position of 1 、r 2 、r 3 , rand represent random numbers in the interval (0,1) for algorithm iteration update; τ, υ, ξ represent algorithm iteration update adjustment factors; at the end of the exploration phase, calculate the fitness values ​​of all F(t+1) heat supply search gorilla individuals. If the fitness value satisfies F(t+1)>F(t), use the F(t+1) heat supply search gorilla individual to replace the F(t) heat supply search gorilla individual. The optimal individual heat supply search gorilla generated in the exploration phase is regarded as the silverback heat supply search gorilla, that is, search for different standard heat supply data a that matches the user-side real-time heat supply data q in the search space of different standard heat supply data sets A. m location;

[0084] S223, development stage, the development stage adopts two behaviors: following the silverback heat supply to search for gorillas and competing for the heat supply of adult females to search for gorillas;

[0085] S2231, follow the silverback heat supply to search for the gorilla, if The heat supply searching gorilla individual chooses to follow the mechanism of the silverback heat supply searching gorilla, and searches for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m , where τ represents the algorithm iteration update adjustment factor, The weight factor of the heat-seeking gorilla population choosing to follow the silverback heat-seeking gorilla and competing with the adult female heat-seeking gorilla; the behavior simulation calculation formula for choosing to follow the silverback heat-seeking gorilla is F(t+1)=υ×ω×(F(t)-F best )+F(t), where Fbest represents the heat supply of the silverback search gorilla individual searching for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m The best candidate location; ω represents the control factor that simulates the behavior of the gorilla searching for heat by following the silverback;

[0086] S2232, competition for adult females, if The heat supply searching gorilla mechanism of following competition for adult females is selected. The heat supply searching gorilla individuals entering adolescence compete with other male heat supply searching gorillas in the selection of adult females and search for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m , the competition behavior simulation calculation formula is: where φ is used to simulate the impact force of a male heat-seeking gorilla, is the coefficient vector of the degree of violence in the conflict; at the end of the development phase, the fitness values ​​of all F(t+1) heat supply search gorilla individuals are calculated. If the fitness value satisfies F(t+1)>F(t), the F(t+1) heat supply search gorilla individual is used to replace the F(t) heat supply search gorilla individual. The optimal male heat supply search gorilla generated in this stage is regarded as the silverback heat supply search gorilla, that is, different standard heat supply data a matching the user-side real-time heat supply data q is searched in the search space of different standard heat supply data sets A m ;

[0087] S224: meet the maximum number of iterations and output different standard heating data a that matches the user's real-time heating data q m ;

[0088] S23, the different standard heating data output in step S224 a m Generate real-time standard heating data a at the user end through data identification shishi , a shishi The unit is kilowatt.

[0089] Through the user-side heat collection unit, the heat meter is used to scientifically collect the real-time heat consumption parameters of the user side online, providing reliable data for accurately matching the standard heating temperature of the user side; different standard heating temperature storage units and user-side heat supply matching units cooperate with each other, scientifically preset and store different standard heating temperature parameters, and combine intelligent recognition algorithms with the user-side real-time heat consumption parameters to perform heat consumption numerical analysis, efficiently and accurately analyze the required standard heating temperature parameters of the user side, and improve the heat scheduling accuracy and efficiency of the heat scheduling system.

[0090] For further information, see Figure 1 - Figure 2 The operation steps for constructing the user-side real-time heating scheduling data based on the user-side real-time standard heating data are as follows:

[0091] S31, the user end real-time standard heating data a shishi The program editing software is used to convert the heating scheduling system to identify the heating scheduling instructions and build real-time heating scheduling data for the user end. diaodu, the program editing software includes any one of VisualStudio Code, PyCharm, and IntelliJ IDEA.

[0092] The steps for screening the heating monitoring point objects based on the preset monitoring point data of the heating system and generating the monitoring point data for the heating system status analysis are as follows:

[0093] S41, when the user end real-time heating scheduling data a diaodu When the construction is completed, the preset monitoring point data set B of the heating system is established = (b 1 ,…,b n ,…,b ν ), n=1,2,3,…,ν; where b n represents the nth preset monitoring point data of the heating system, ν represents the maximum number of preset monitoring point data of the heating system; b n =(x n ,y n ,z n ), where x n Indicates the horizontal coordinate corresponding to the preset monitoring point data of the nth heating system, y n Indicates the vertical coordinate corresponding to the data of the preset monitoring point of the nth heating system, z n Indicates the vertical coordinate corresponding to the nth preset monitoring point data of the heating system, where the preset monitoring point data of the heating system represents the monitoring coordinate point data used to monitor the abnormal heating state of the heating pipe network in the heating system;

[0094] S42, using a bidirectional search algorithm to randomly select and filter out the preset monitoring point data set B of the heating system. n And generate the heating system status analysis monitoring point data set B'=(b' n1 ,…,b' n2 ), 1≤n1≤n≤n2≤ν, where b' n1 represents the data of the n1th heating system status analysis monitoring point, b' n2 Represents the data of the n2th heating system status analysis monitoring point.

[0095] The steps for collecting the real-time heating data of the monitoring point and identifying the abnormal heating status of the monitoring point with the standard heating data of the monitoring point to construct the real-time abnormal heating status identification result data of the monitoring point are as follows:

[0096] S51, collect the monitoring point data of heating system status analysis online through industrial sensors b' n1 to b' n2 The real-time heating data of the monitoring points of the heating pipe network in the corresponding heating system is generated, and the real-time heating data set of the monitoring points is generated C = (c n1,…,c n2 ), where c n1 and c n2 Respectively represent the monitoring point data b' of the heating system status analysis n1 and b' n2 The corresponding real-time heating data of the monitoring point, including any one of the real-time supply pressure data, real-time back pressure data, real-time temperature data, real-time return temperature data and real-time flow data of the monitoring point of the heating network;

[0097] S52, establish the monitoring point standard heating data interval D = [d 1 ,…,d 2 ],d 1 and d 2 They respectively represent the end value of the left monitoring point standard heating data and the end value of the right monitoring point standard heating data in the monitoring point standard heating data interval D, and the monitoring point standard heating data includes any one of the standard supply pressure data, standard back pressure data, standard temperature data, standard return temperature data, and standard flow data of the heating network monitoring point;

[0098] S53, using a unified cost search algorithm to search the real-time heating data set C of the monitoring point real-time heating data c n1 to c n2 According to the monitoring point number, the standard heating data end value d of the left monitoring point in the standard heating data interval D 1 and the standard heating data end value d of the right monitoring point 2 Perform heating value comparison, and build a real-time heating abnormal state recognition result data set E of the monitoring point based on the heating value comparison result. n1 ,…,e n2 ), where e n1 Indicates the monitoring point data b' for the heating system status analysis n1 The corresponding monitoring point real-time heating abnormal state recognition result data, e n2 Indicates the monitoring point data b' for the heating system status analysis n2 Real-time abnormal heating status identification result data of the corresponding monitoring points;

[0099] When c n1 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n1 No abnormality;

[0100] when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n1 is abnormal;

[0101] When cn2 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n2 No abnormality;

[0102] when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n2 is abnormal.

[0103] Based on the real-time abnormal heating state recognition result data of the monitoring point and the heating system state analysis monitoring point data, the heating system abnormal heating state recognition and monitoring position combination data are generated in the following steps:

[0104] S61, the monitoring point real-time heating abnormal state recognition result data set E is used to identify the monitoring point real-time heating abnormal state. n1 To e n2 According to the monitoring point number, the heating system status analysis monitoring point data set B' is sorted and the heating system status analysis monitoring point data b' n1 and b' n2 Combine the data of the heating pipe network monitoring points in the heating system and the corresponding abnormal heating state identification results to generate the heating system abnormal heating state identification and monitoring position combination data set G = (g n1 ,…,g n2 ), where g n1 Indicates e n1 and b' n1 The corresponding heating system abnormal state recognition and monitoring location combined data, g n2 Indicates e n2 and b' n2 The corresponding heating system abnormal heating state identification and monitoring location combined data.

[0105] The user-side heating scheduling information generation unit accurately generates the user-side real-time heating scheduling control instruction parameters; the heating system preset monitoring point storage unit and the heating system monitoring point screening unit cooperate with each other, and use the intelligent search algorithm to intelligently and randomly select the heating pipe network monitoring points of the heating scheduling system, so as to realize the scientific sampling of the heating pipe network monitoring point objects of the heating scheduling system; the heating system monitoring point heating parameter acquisition unit and the heating system monitoring point heating anomaly identification unit cooperate with each other, and use industrial sensors to dynamically collect the real-time heating parameters of the monitoring points, combined with the intelligent search algorithm and the standard heating parameters of the monitoring points to intelligently evaluate the abnormal heating state of the monitoring points, and intelligently and accurately identify the real-time abnormal heating state identification result parameters of the monitoring points, so as to realize the scientific monitoring of the heating state of the heating pipe network of the heating scheduling system; the monitoring point heating anomaly identification and position parameter generation unit combines the heating state identification results of the heating pipe network and the monitoring point coordinate parameters, and generates the heating system heating anomaly state identification and monitoring position combination parameters, so as to realize the precise positioning of the heating state of the heating pipe network in the heating scheduling system.

[0106] For further information, see Figure 1-Figure 2 The steps to construct the heating dispatch system heating dispatch monitoring feedback data and push feedback to the heating dispatch management center are as follows:

[0107] S71, the user end real-time heating scheduling data a diaodu Combine the data set G of abnormal state recognition and monitoring position combination of the heating system to generate the heating scheduling monitoring feedback data K of the heating scheduling system = (G, a diaodu );

[0108] S72, the heating dispatching system heating dispatching monitoring feedback data K is pushed to the dispatching management center through the Internet of Things communication network and the feedback is displayed using a display device, which includes a display screen and a projection device.

[0109] Through the heating dispatching monitoring data processing unit of the heating dispatching system, the heating dispatching monitoring feedback parameters of the heating dispatching system are scientifically constructed to provide data support for the visual feedback of the heating dispatching and heating monitoring information of the heating dispatching system; the heating dispatching monitoring result feedback unit of the heating dispatching system pushes the heating dispatching monitoring feedback parameters of the heating dispatching system to the heating dispatching management center through the Internet of Things and displays them visually through display devices, thereby realizing accurate and intuitive result feedback on the heating dispatching information of the user end of the heating dispatching system and the heating status of the heating pipe network monitoring point, thereby improving the heating quality and safety of the heating dispatching system.

[0110] Embodiment 2:

[0111] See also Figure 1 - Figure 2, a heating scheduling automation realization system based on data visualization, which is used to realize the heating scheduling automation realization method based on data visualization, and the system includes a user heating information processing module, a heating scheduling and monitoring processing module, and a heating scheduling monitoring feedback module;

[0112] The user heat supply information processing module includes a user-side heat collection unit, a different standard heat supply storage unit, and a user-side heat supply matching unit;

[0113] The user-side heat collection unit collects the user-side real-time heat data through the heat meter; the different standard heat storage units are used to store different standard heat data; the user-side heat matching unit performs real-time heat matching processing on the user side of the heating scheduling system according to the user-side real-time heat data and different standard heat data, and generates real-time standard heat data for the user side;

[0114] The heating scheduling and monitoring processing module includes a user-side heating scheduling information generation unit, a heating system preset monitoring point storage unit, a heating system monitoring point screening unit, a heating system monitoring point heating parameter collection unit, a heating system monitoring point heating parameter collection unit, a heating system monitoring point standard heating parameter storage unit, a heating system monitoring point heating anomaly identification unit, and a monitoring point heating anomaly identification and position parameter generation unit;

[0115] A user-side heating scheduling information generating unit is used to construct user-side real-time heating scheduling data based on user-side real-time standard heating data; a heating system preset monitoring point storage unit is used to store heating system preset monitoring point data; a heating system monitoring point screening unit is used to perform heating monitoring point object screening processing based on the heating system preset monitoring point data, and generate heating system state analysis monitoring point data; a heating system monitoring point heating parameter acquisition unit is used to acquire real-time heating data of the monitoring point through industrial sensors; a heating system monitoring point standard heating parameter storage unit is used to store standard heating data of the monitoring point; a heating system monitoring point heating anomaly identification unit is used to perform heating anomaly state identification processing of the monitoring point based on the real-time heating data of the monitoring point and the standard heating data of the monitoring point, and construct real-time heating anomaly state identification result data of the monitoring point; a monitoring point heating anomaly identification and position parameter generation unit is used to perform heating anomaly spatial position identification processing of the heating system based on the real-time heating anomaly state identification result data of the monitoring point and the heating system state analysis monitoring point data, and generate heating anomaly state identification and monitoring position combination data of the heating system;

[0116] The heating dispatch monitoring feedback module includes a heating dispatch monitoring data processing unit of the heating dispatch system and a heating dispatch monitoring result feedback unit of the heating dispatch system;

[0117] The heating dispatching monitoring data processing unit of the heating dispatching system constructs the heating dispatching monitoring feedback data of the heating dispatching system based on the real-time heating amount dispatching data of the user end and the combined data of the abnormal heating state identification and monitoring position of the heating system; the heating dispatching monitoring result feedback unit of the heating dispatching system pushes the heating dispatching monitoring feedback data of the heating dispatching system to the heating dispatching management center and displays the feedback through the display device.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for realizing automatic heating scheduling based on data visualization, characterized in that: The method comprises the following steps: S1, collect real-time heat data of the user end; S2, performing real-time heat supply matching processing on the user side of the heating scheduling system according to the real-time heat consumption data on the user side and different standard heat supply data, and generating real-time standard heat supply data on the user side; S3, constructing user-side real-time heating supply scheduling data according to the user-side real-time standard heating supply data; S4, performing heating monitoring point object screening processing based on the preset monitoring point data of the heating system, and generating heating system status analysis monitoring point data; S5, collecting real-time heating data of the monitoring point and performing abnormal heating state recognition processing on the standard heating data of the monitoring point, and constructing real-time abnormal heating state recognition result data of the monitoring point; S6, performing abnormal heating state recognition result data of the monitoring point and the heating system state analysis monitoring point data on the heating system to identify the abnormal heating state of the heating system and generate abnormal heating state recognition and monitoring position combination data of the heating system; S7. Construct the heat supply dispatching system heat supply dispatching monitoring feedback data and push the feedback to the heat supply dispatching management center.

2. The method for realizing automatic heating scheduling based on data visualization according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Real-time heat consumption data of a specific user end in the heating system is collected online through a heat meter and real-time heat consumption data q of the user end is generated, where the unit of q is kilowatt.

3. The method for realizing automatic heating scheduling based on data visualization according to claim 2 is characterized in that: The S2 comprises the following steps: S21, establish different standard heating data sets A = (a1, ..., a m ,…,a μ ), m=1,2,3,…,μ; where a m Indicates the mth different standard heating data, a m =[q m1 ,q m2 ], where [q m1 ,q m2 ] represents the user end real-time heat data interval, when the user end real-time heat data q∈[q m1 ,q m2 ], the user end real-time heat data q corresponds to the different standard heat supply data a m ;q m1 and q m2 It means [q m1 ,q m2 ] represents the left and right end values, μ represents the maximum value of the number of different standard heating data types, a m The unit is kilowatt; S22, compare the user end real-time heat consumption data q with the different standard heat supply data a in the different standard heat supply data set A. m According to the user's real-time heat value matching, search for the different standard heat supply data a corresponding to the user's real-time heat data q m , execute the search to find the different standard heating data a m The specific steps are as follows: S221, initialization, update algorithm maximum iteration number T; S222, exploration stage, this stage is mainly to search for different standard heating data a in the space of different standard heating data set A m Perform a global search and search for different standard heat supply data a that matches the user's real-time heat data q m ; S223, development stage, the development stage adopts two behaviors: following the silverback heat supply to search for gorillas and competing for the heat supply of adult females to search for gorillas; S2231, follow the silverback heat supply to search for the gorilla, if The heat supply searching gorilla individual chooses to follow the mechanism of the silverback heat supply searching gorilla, and searches for different standard heat supply data a that matches the user's real-time heat supply data q in the search space of different standard heat supply data sets A. m , where τ represents the algorithm iteration update adjustment factor, The weight factor representing the heat-seeking gorilla population's choice to follow the silverback heat-seeking gorilla and compete with the adult female heat-seeking gorilla; S2232, competition for adult females, if The heat supply searching gorilla mechanism of following the competition of adult females is selected, and the heat supply searching gorilla individuals entering adolescence compete with other male heat supply searching gorillas in the problem of selecting adult females and search for different standard heat supply data a that matches the real-time heat supply data q of the user end in the search space of different standard heat supply data sets A. m ; S224: satisfy the maximum number of iterations and output different standard heat supply data a that matches the user's real-time heat data q m ; S23, the different standard heating data output in step S224 m Generate user-side real-time standard heating data a through data identification shishi , a shishi The unit is kilowatt.

4. The method for realizing automatic heating scheduling based on data visualization according to claim 3 is characterized in that: The S3 comprises the following steps: S31, the user end real-time standard heating data a shishi The program editing software is used to convert the heating scheduling system to identify the heating scheduling instructions and build real-time heating scheduling data for the user end. diaodu .

5. The method for realizing automatic heating scheduling based on data visualization according to claim 4 is characterized in that: The S4 comprises the following steps: S41, when the user terminal real-time heating scheduling data a diaodu When the construction is completed, the preset monitoring point data set B of the heating system is established = (b1,…,b n ,…,b ν ), n=1,2,3,…,ν; where b n represents the nth preset monitoring point data of the heating system, and ν represents the maximum number of preset monitoring point data of the heating system; S42, using a bidirectional search algorithm to randomly select and filter the preset monitoring point data set B of the heating system to obtain the preset monitoring point data set B of the heating system. n And generate the heating system status analysis monitoring point data set B'=(b' n1 ,…,b' n2 ), 1≤n1≤n≤n2≤ν, where b' n1 represents the data of the n1th heating system status analysis monitoring point, b' n2 Represents the data of the n2th heating system status analysis monitoring point.

6. The method for realizing automatic heating scheduling based on data visualization according to claim 5 is characterized in that: The S5 comprises the following steps: S51, collecting the state analysis monitoring point data of the heating system online through industrial sensors b' n1 to b' n2 The real-time heating data of the monitoring points of the heating pipe network in the corresponding heating system is generated, and the real-time heating data set of the monitoring points is generated C = (c n1 ,…,c n2 ), where c n1 and c n2 Respectively represent the heating system status analysis monitoring point data b' n1 and b' n2 The corresponding real-time heating data of the monitoring point, the real-time heating data of the monitoring point includes any one of the real-time supply pressure data, real-time back pressure data, real-time temperature data, real-time return temperature data, and real-time flow data of the heating pipe network monitoring point; S52, establish a monitoring point standard heating data interval D = [d1, ..., d2], d1 and d2 respectively represent the left monitoring point standard heating data end value and the right monitoring point standard heating data end value in the monitoring point standard heating data interval D, and the monitoring point standard heating data includes any one of the standard supply pressure data, standard back pressure data, standard temperature data, standard return temperature data, and standard flow data of the heating pipe network monitoring point; S53, using a unified cost search algorithm to search the real-time heating data set C of the monitoring point in the real-time heating data set C of the monitoring point n1 to c n2 According to the monitoring point number, the heating value is compared with the left monitoring point standard heating data end value d1 and the right monitoring point standard heating data end value d2 in the monitoring point standard heating data interval D in order, and the real-time heating abnormal state recognition result data set E of the monitoring point is constructed according to the heating value comparison result. n1 ,…,e n2 ), where e n1 Indicates the monitoring point data b' for the heating system status analysis n1 The corresponding monitoring point real-time heating abnormal state recognition result data, e n2 Indicates the monitoring point data b' for the heating system status analysis n2 Real-time abnormal heating status identification result data of the corresponding monitoring points; When c n1 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n1 No abnormality; when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n1 is abnormal; When c n2 ∈D, it means that the heating data of the monitoring point of the heating network meets the standard heating data requirements, and the real-time heating abnormal state recognition result data e of the monitoring point is output n2 No abnormality; when When , it means that the heating data of the monitoring point of the heating network does not meet the standard heating data requirements, then the real-time abnormal heating state recognition result data of the monitoring point is output. n2 is abnormal.

7. The method for realizing automatic heating scheduling based on data visualization according to claim 6 is characterized in that: The S6 comprises the following steps: S61, the monitoring point real-time heating abnormal state recognition result data set E of the monitoring point real-time heating abnormal state recognition result data set E n1 To e n2 The heating system state analysis monitoring point data b' in the heating system state analysis monitoring point data set B' are ordered according to the monitoring point numbers. n1 and b' n2 Combine the data of the heating pipe network monitoring points in the heating system and the corresponding abnormal heating state identification results to generate the heating system abnormal heating state identification and monitoring position combination data set G = (g n1 ,…,g n2 ), where g n1 Indicates e n1 and b' n1 The corresponding heating system abnormal state recognition and monitoring location combined data, g n2 Indicates e n2 and b' n2 The corresponding heating system abnormal heating state identification and monitoring location combined data.

8. The method for realizing automatic heating scheduling based on data visualization according to claim 7 is characterized in that: The S7 comprises the following steps: S71, the user end real-time heating scheduling data a diaodu Combined with the abnormal state recognition and monitoring position combination data set G of the heating system to generate the heating scheduling monitoring feedback data K of the heating scheduling system = (G, a diaodu ); S72, pushing the heating dispatch monitoring feedback data K of the heating dispatch system to the dispatch management center through the Internet of Things communication network and using a display device for feedback display.

9. A heating scheduling automation realization system based on data visualization, used to realize the heating scheduling automation realization method based on data visualization according to any one of claims 1 to 8, characterized in that: The system includes a user heating information processing module, a heating scheduling and monitoring processing module, and a heating scheduling monitoring feedback module.

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