A smart heating operation system and method based on the Internet of Things

Abstract

The present invention discloses an intelligent heating operation system and method based on the Internet of Things, which is applied to the field of intelligent heating operation, and the method comprises: dividing each building according to a map to obtain heating data of corresponding users; monitoring the heating data and tracking energy consumption data, and calculating the total energy consumption of the heating system; establishing a mathematical model to analyze the heating data; establishing a prediction model for the heating system; establishing a regulation model with phased variable flow, and determining the supply and return water temperature values ​​of corresponding users; implementing data transmission between the heating system and users through the Internet of Things, and issuing early warning notifications to users in real time; realizing a data diagnosis engine based on energy consumption data tracking and mathematical model analysis, deeply mining and analyzing the heating operation data, diagnosing the operating status of the heating system, detecting abnormal data and issuing early warnings to user terminals, automatically determining users with overheating or poor heating through a real-time optimization layer, and automatically or manually adjusting the flow size of a valve.

Description

Technical Field

[0001] The present invention relates to the field of smart heating operation technology, and in particular to a smart heating operation system and method based on the Internet of Things. Background Art

[0002] With the continuous and rapid development of my country's construction industry, the proportion of heating buildings has continued to increase, the area of ​​centralized heating in northern cities has continued to increase, and the heating area has rapidly expanded to the south. The primary energy used in the heating industry is mainly coal. In order to break through the serious constraints of energy and environment on social and economic development and achieve sustainable development, my country is comprehensively promoting the transformation of energy production and consumption to clean, low-carbon, and non-fossil methods. The following problems exist in the existing heating operation methods:

[0003] (1) There is a lack of energy management informationization during the heating operation process. Most heating data are recorded manually, which cannot guarantee the timeliness and accuracy of heating data;

[0004] (2) The operation and scheduling of traditional heating methods is based on the experience of the operating personnel to schedule the heat source, heating pipeline network, heating station and heat user system. It cannot automatically analyze the heat load characteristics of each building and the operation is cumbersome.

[0005] Reference patent application number CN202110480938.8 discloses a smart heating system based on Internet of Things technology, the specific content of which is: including constructing a weather index G(k) = G(light intensity (k), wind speed (k), temperature (k), rain and snow index (k)) function, and storing the calculated value of G(k) at hourly intervals; extracting and processing the accumulated heat history data Q(K) and G(K) stored in the cloud server to generate a (Q(K), G(K)) sequence pair; performing supervised training based on the generated sequence to generate a new load prediction sequence Q′(K), and predicting the load of the heat exchanger unit at future times; forming a background mechanism to judge the current operating environment.

[0006] This prior art optimizes the temperature control method based on equipment operation data analysis, but the data referenced by its solution is not comprehensive enough, the collected data does not have a certain degree of accuracy, and there is a lack of analysis of heating operation data. Therefore, this application provides a smart heating operation system and method based on the Internet of Things to achieve intelligent scheduling, intelligent control, intelligent analysis and intelligent services for the heating system. Summary of the invention

[0007] The purpose of this application is to provide a smart heating operation system and method based on the Internet of Things, aiming to solve the problems of inaccurate collected data and lack of automatic analysis of heating operation data.

[0008] To achieve the above objectives, this application provides the following technical solutions:

[0009] The present application provides a smart heating operation method based on the Internet of Things, comprising:

[0010] S1: Divide each building according to the map to obtain heating data of corresponding users;

[0011] S2: Monitor the heating data and track the energy consumption data, and calculate the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system;

[0012] S3: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations:

[0013]

[0014] A is the N*B network association matrix, which represents the connection relationship between each pipe segment and the node; B f is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; ΔH is the pipe section pressure drop vector; S is a diagonal matrix, and its diagonal elements S i is the resistance characteristic coefficient of each pipe section; |G| is a diagonal matrix, and its diagonal matrix is ​​the absolute value of the flow rate of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch;

[0015] Establish a prediction model for the heating system, which includes: basic control layer, prediction control layer and real-time optimization layer;

[0016] S4: Establish a regulation model for phased variable flow and determine the supply and return water temperature values ​​for the corresponding users;

[0017] S5: Implement data transmission between the heating system and user terminals through the Internet of Things, and issue early warning notifications to users in real time.

[0018] Furthermore, the establishment of a prediction model for the heating system specifically includes:

[0019] Basic control layer: local PID regulation of flow level, pressure level, and heat source water supply temperature;

[0020] Predictive control layer: dynamic control of water supply temperature, flow, heat source and thermal energy storage of the heating network;

[0021] Real-time optimization layer: Heating load prediction based on changes in outdoor ambient temperature.

[0022] Furthermore, the real-time optimization layer is optimized by the following formula:

[0023]

[0024] Where m is the mass flow rate in the heating network, T s is the water supply temperature at the load, c1 and c2 are m and T respectively. s The increase in unit cost, c p is the specific heat capacity of water, T r is the return water temperature at the load, T s * is the supply water temperature for heat loss, Q p is the predicted load value.

[0025] Furthermore, the step of establishing a regulation model for phased variable flow and determining the supply and return water temperature values ​​of the corresponding users includes:

[0026]

[0027] Where τ1 and τ2 are the supply and return water temperatures of the corresponding users, respectively. g is the actual water supply temperature of the user, t h is the actual return water temperature of the user, is the heating load ratio, e Z is the heating coefficient, and Y is the heat dissipation area.

[0028] Furthermore, S3 also includes: optimizing the mathematical model using a particle swarm algorithm, specifically: initializing the heating data settings to generate an initial population, calculating the fitness value of each particle in the population, determining the individual optimal particle and the global optimal particle, and updating the particles through the particle position and speed calculation formula, and finally outputting the optimal particle and the corresponding position.

[0029] Furthermore, the heating system parses the acquired heating data by using the TCP / IP protocol, stores the parsed data in a database, and pushes it to a user terminal after analysis for the user to review and use; the heating system uses a data compression strategy in data storage to encrypt and compress the sent data.

[0030] Furthermore, the user terminal includes: user login function, data display function, operation record query function, report printing function and maintenance function;

[0031] User login function: used to input account name and password, send a request to the server to log in, check the user's account and password in the server, confirm that the database contains the user name and password before allowing the user to log in, and update the user's login time in the database; data display function, used to obtain and display the corresponding heating system status information in real time and query historical data information; operation record query function, used to query the operation records of each user; report printing function, used for users to print the settlement data of the heating system, and can print daily settlement reports and monthly settlement reports; maintenance function, used for administrators to log in to the server and configure the system's operating parameters.

[0032] A smart heating operation system based on the Internet of Things is also provided, including:

[0033] Acquisition module: divide each building according to the map to obtain the heating data of the corresponding users;

[0034] Monitoring module: monitors the heating data and tracks the energy consumption data, and calculates the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system;

[0035] Analysis module: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations:

[0036]

[0037] A is the N*B network association matrix, which represents the connection relationship between each pipe segment and the node; B f is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; ΔH is the pipe section pressure drop vector; S is a diagonal matrix, and its diagonal elements S i is the resistance characteristic coefficient of each pipe section; |G| is a diagonal matrix, and its diagonal matrix is ​​the absolute value of the flow rate of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch;

[0038] Establish a prediction model for the heating system, which includes: basic control layer, prediction control layer and real-time optimization layer;

[0039] Regulation module: Establish a regulation model for phased variable flow and determine the supply and return water temperature values ​​of the corresponding users;

[0040] Transmission module: Implement data transmission between the heating system and users through the Internet of Things, and issue early warning notifications to users in real time.

[0041] The present application provides a smart heating operation system and method based on the Internet of Things, which has the following beneficial effects compared with the prior art:

[0042] (1) Realize smart heating, implement a data diagnosis engine based on energy consumption data tracking and mathematical model analysis, conduct in-depth mining and analysis of heating operation data, diagnose the operating status of the heating system, detect abnormal data and issue early warnings to user terminals;

[0043] (2) Establish a multi-level control architecture for the heating system based on a real-time optimization layer, a predictive control layer, and a basic control layer. This hierarchical mechanism optimizes economic gains by optimizing slowly changing variables at a higher level of the control and directly using the optimized values ​​as set points for faster changing dynamic processes, thereby reducing over-calculation of slowly changing dynamic processes while still tracking fast changing dynamic processes.

[0044] (3) Controlling the hydraulic balance between users of the heating system can make the system operate economically and save energy and reduce consumption; through real-time optimization, the user who is overheated or poorly heated is automatically determined, and the flow rate of the valve is automatically or manually adjusted to keep the indoor temperature of each household basically the same. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a smart heating operation method based on the Internet of Things according to an embodiment of the present application;

[0046] Figure 2 This is a structural diagram of an intelligent heating operation system based on the Internet of Things according to an embodiment of the present application.

[0047] The implementation, functional features and advantages of the present application will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] Reference Figure 1, which is a flow chart of a smart heating operation method based on the Internet of Things proposed in this application;

[0051] The present application provides a smart heating operation method based on the Internet of Things, the steps comprising:

[0052] S1: Divide each building according to the map to obtain heating data of corresponding users;

[0053] In this step, GIS positioning technology is used to divide each building, locate the exact position of the corresponding user, track energy consumption data in real time, achieve hydraulic balance of the heating system, and reasonably distribute the flow among users. This is the basic condition for implementing quantitative management and ensuring the quality of heating, and can play a role in energy saving and consumption reduction. Through GIS positioning technology and analysis of heating data, users with overheating and poor heating are automatically found, and the balance adjustment order is determined from near to far according to their size and the degree of temperature difference deviation. The flow of the valve is automatically or manually adjusted to keep the temperature of each household consistent with the indoor temperature; the heating data is stored in the database. When the data upload fails or the network is disconnected, the stored data is saved for at least 24 hours. The data is transmitted again after the network is normal, and the power is automatically cut off and the transmission is resumed. The uploaded heating data is uploaded to the database one by one at a time.

[0054] S2: Monitor the heating data and track the energy consumption data, and calculate the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system;

[0055] In this step, the pipe network part of the heating system is designed mainly based on the system scale, geographical factors, user types and the heat source used. In addition to its role in connecting the production side and the consumption side, the heating network also defines the interconnection between different components of the system. The heating network also has an impact on the energy consumption of the system; since most heating pipe networks work within a specific temperature range, the heat loss of the system can be considered as a function of the network scale rather than a function of time. Therefore, the total heat energy demand of the system is equal to the sum of the loads of different heat users plus the heat loss per unit length of the pipe network.

[0056] S3: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations:

[0057]

[0058] A is the N*B network association matrix, which represents the connection relationship between each pipe segment and the node; B f is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; ΔH is the pipe section pressure drop vector; S is a diagonal matrix, and its diagonal elements S i is the resistance characteristic coefficient of each pipe section; |G| is a diagonal matrix, and its diagonal matrix is ​​the absolute value of the flow rate of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch;

[0059] A prediction model is established for the heating system, and the model includes: a basic control layer, a prediction control layer and a real-time optimization layer; the prediction model established for the heating system specifically includes:

[0060] Basic control layer: local PID regulation of flow level, pressure level, and heat source water supply temperature;

[0061] Predictive control layer: dynamic control of water supply temperature, flow, heat source and thermal energy storage of the heating network;

[0062] Real-time optimization layer: heating load prediction based on outdoor ambient temperature changes;

[0063] The particle swarm algorithm is used to optimize the mathematical model, specifically: the heating data is initialized to generate an initial population, the fitness value of each particle in the population is calculated, the individual optimal particle and the global optimal particle are determined, and the particles are updated through the particle position and speed calculation formula, and finally the optimal particle and the corresponding position are output.

[0064] In this step, the particle swarm algorithm is used to optimize the mathematical model. The particle swarm algorithm (PSO) is an important choice for solving complex system optimization problems. The particle swarm algorithm is a population intelligent probabilistic search algorithm. Its core idea is to use the information sharing mechanism and let particles learn from each other to promote population development. This method retains the global search strategy based on the population, and its unique memory function can dynamically track the current situation and adjust the search strategy. In recent years, facing the needs of engineering applications, the particle swarm algorithm has made rapid progress in solving multi-objective, multi-constrained, and high-dimensional optimization problems; the particle swarm algorithm originated from the study of the foraging behavior of bird flocks. If the optimization problem is regarded as a flock of birds foraging in the air, then a foraging "bird" flying in the air is a "particle" in the particle swarm algorithm that searches in the solution space. The direction change of the flock of birds during flight is unpredictable, but its overall consistency is maintained, and the individuals maintain a suitable distance; suppose that each particle in the optimization algorithm flies at a certain speed in the n-dimensional space, Xi = (x i1 ,x i2 ,...,x im) is the current position of particle i. For the resistance identification problem of the heating system pipe network, it is the resistance coefficient of each pipe section; V i =(v i1 ,v i2 ,...,v im ) is the current flying speed of particle i, pbest i =(pbest i1 ,pbest i2 ,....pbest in ) is the optimal position that particle i has ever experienced, which is called the individual optimal position; for the objective function f(X), the current optimal position of particle i can be determined by the following formula:

[0065]

[0066] Where t is the number of generations of particle swarm evolution;

[0067] Assume that the number of particles in the group is N, and the best position gbest(t) experienced by all particles in the group is the approximate resistance coefficient of the pipe network, then f(gbest(t)) = min{f(pbest1(t)),f(pbest2(t)),kf(pbest2(t))};

[0068] The flight speed and position of the particles need to be dynamically adjusted according to the flight experience of the individual and the group. The update equations of their speed and position are:

[0069]

[0070] Where i is the i-th particle, j is the j-th dimension of the particle, and v ij (t) is the position component of particle i in the jth dimension, pbest ij (t) is the optimal position pbest of the j-th individual of particle i when it evolves to generation t i Quantity, pbest j (t) The j-th dimension component of the optimal position pbest component of the entire particle swarm when it evolves to the tth generation, c1 and c2 are learning factors.

[0071] S4: Establishing a regulation model with variable flow in stages to determine the supply and return water temperature values ​​of the corresponding users; the step of establishing a regulation model with variable flow in stages to determine the supply and return water temperature values ​​of the corresponding users includes:

[0072]

[0073] Where τ1 and τ2 are the supply and return water temperatures of the corresponding users, respectively. g is the actual water supply temperature of the user, t his the actual return water temperature of the user, is the heating load ratio, e Z is the heating coefficient, and Y is the heat dissipation area.

[0074] In this step, under the stable working condition of the heating system, ignoring the heat loss along the pipeline network, the following formula is used for the heating system: Q1′=Q2′=Q3′, Q1′=q′V(t n -t′ w ), Q2′=K′F(t pj -t n ), Q3′=G′c′(t′ g -t h ') / 3600 = 1.163G'(t' g -t h ′) where Q1′ is the design heat load of the building, Q2′ is the calculated temperature outside the heating room t w The heat released under the heating, Q3′ is the calculated temperature outside the heating room t w The heat delivered to the user under the condition of q′ is the volume heating index of the building, V is the outer volume of the building, t w Calculate the outdoor temperature for heating, t n is the calculated temperature in the heating room, t′ g is the water supply temperature of the heating user. If the user is directly connected to the heating network without a water mixing device, the water supply temperature of the heating network is equal to the water supply temperature of the heating user. If the user is directly connected to the heating network with a water mixing device, the water supply temperature of the heating network is greater than the water supply temperature of the heating user. h is the return water temperature of the heating user. If the heating user is directly connected to the heating network, the return water temperature of the heating network is equal to the return water temperature of the heating system. pj is the average temperature, G′ is the circulating water volume of the heating user, c is the mass ratio of hot water, K′ is the heat transfer coefficient, and F is the heat dissipation area.

[0075] During operation adjustment, the corresponding t w The ratio of the heating load under the condition to the heating design load is the relative heating load ratio Q, and the ratio of the flow rate is the relative flow rate G, then:

[0076]

[0077] Since the error caused by treating the heating index as a constant is very small in practice, it can be considered that: That is, the relative heating load ratio is equal to the relative indoor and outdoor temperature difference ratio.

[0078] S5: Implement data transmission between the heating system and users through the Internet of Things, and issue early warning notifications to users in real time.

[0079] In this step, the heating system uses the TCP / IP protocol to parse the acquired heating data, stores the parsed data in the database, and pushes it to the user terminal after analysis for the user to consult and use; the heating system uses a data compression strategy to encrypt and compress the sent data in the data storage; the user terminal includes: a user login function, which is used to enter the account name and password, send a request to the server to log in, check the user's account and password in the server, and allow the user to log in after confirming that the database contains the user name and password, and update the login time of the user in the database; a data display function, which is used to obtain and display the corresponding heating system status information in real time and query historical data information; an operation record query function, which is used to query the operation record report printing function and maintenance function of each user; a report printing function, which is used for users to print the settlement data of the heating system, and can print daily settlement reports and monthly settlement reports; a maintenance function, which is used for administrators to log in to the server and configure the system's operating parameters.

[0080] Reference Figure 2 The present invention also provides a smart heating operation system based on the Internet of Things, comprising:

[0081] Acquisition module: divide each building according to the map to obtain the heating data of the corresponding users;

[0082] Monitoring module: monitors the heating data and tracks the energy consumption data, and calculates the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system;

[0083] Analysis module: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations:

[0084]

[0085] A is the N*B network association matrix, which represents the connection relationship between each pipe segment and the node; B f is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; ΔH is the pipe section pressure drop vector; S is a diagonal matrix, and its diagonal elements S iis the resistance characteristic coefficient of each pipe section; |G| is a diagonal matrix, and its diagonal matrix is ​​the absolute value of the flow rate of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch;

[0086] Establish a prediction model for the heating system, which includes: basic control layer, prediction control layer and real-time optimization layer;

[0087] Regulation module: Establish a regulation model for phased variable flow and determine the supply and return water temperature values ​​of the corresponding users;

[0088] Transmission module: Implement data transmission between the heating system and users through the Internet of Things, and issue early warning notifications to users in real time.

[0089] To summarize, the present application obtains the heating data of the corresponding users, monitors the heating data, establishes a mathematical model to analyze the heating data, and establishes adjustment control according to the results of the analysis, so as to determine the supply and return water temperature values ​​of the corresponding users; realizes the hydraulic balance of the heating system, reasonably distributes the flow among the users, implements quantitative management, ensures the quality of heating, and can save energy and reduce consumption at the same time; and automatically determines the users with overheating or poor heating through the real-time optimization layer, automatically or manually adjusts the flow of the valve, so that the indoor temperature of each household remains basically the same; realizes the intelligent scheduling, intelligent control, intelligent analysis and intelligent service of the heating system.

[0090] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0091] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0092] Although the embodiments of the present application 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 application, and that the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A smart heating operation method based on the Internet of Things, characterized in that: include: S1: Divide each building according to the map to obtain heating data of corresponding users; S2: Monitor the heating data and track the energy consumption data, and calculate the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system; S3: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations: Among them, A is the N*B network association matrix, which represents the connection relationship between each pipe section and the node; is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; is the pipe section pressure drop vector; S is a diagonal matrix, whose diagonal elements are the resistance characteristic coefficients of each pipe section; |G| is a diagonal matrix, whose diagonal matrix is ​​the absolute value of the flow of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch; Establish a prediction model for the heating system, which includes: basic control layer, prediction control layer and real-time optimization layer; S4: Establish a regulation model for phased variable flow and determine the supply and return water temperature values ​​for the corresponding users; S5: Implement data transmission between the heating system and user terminals through the Internet of Things, and issue early warning notifications to users in real time; The establishment of a prediction model for the heating system specifically includes: Basic control layer: local PID regulation of flow level, pressure level, and heat source water supply temperature; Predictive control layer: dynamic control of water supply temperature, flow, heat source and thermal energy storage of the heating network; Real-time optimization layer: heating load prediction based on outdoor ambient temperature changes; The step of establishing a regulation model for phased variable flow and determining the supply and return water temperature values ​​of the corresponding users includes: Where τ1 and τ2 are the supply and return water temperatures of the corresponding users, respectively. g is the actual water supply temperature of the user, t h is the actual return water temperature of the user. is the heating load ratio, e Z is the heating coefficient, and Y is the heat dissipation area.

2. According to the method of claim 1, the method is characterized in that: The real-time optimization layer is optimized by the following formula: Where m is the mass flow rate in the heating network, T s is the water supply temperature at the load, c1 and c2 are m and T respectively. s The increase in unit cost, c p is the specific heat capacity of water, T r is the return water temperature at the load, is the supply water temperature for heat loss, Q p is the predicted load value.

3. According to the method of claim 1, the method is characterized in that: The S3 also includes: optimizing the mathematical model by using a particle swarm algorithm, specifically: initializing the heating data settings to generate an initial population, calculating the fitness value of each particle in the population, determining the individual optimal particle and the global optimal particle, and updating the particles through the particle position and speed calculation formula, and finally outputting the optimal particle and the corresponding position.

4. The method for intelligent heating operation based on the Internet of Things according to claim 1 is characterized in that: The heating system parses the acquired heating data by using the TCP / IP protocol, stores the parsed data in a database, and pushes it to the user terminal after analysis for the user to check and use; The heating system uses data compression strategies to encrypt and compress the transmitted data during data storage.

5. The method for intelligent heating operation based on the Internet of Things according to claim 1 is characterized in that: The user terminal includes: user login function, data display function, operation record query function, report printing function and maintenance function; User login function: used to input account name and password, send a request to the server to log in, check the user's account and password in the server, and allow the user to log in after confirming that the user name and password are included in the database, and update the user's login time in the database; data display function, used to obtain and display the corresponding heating system status information in real time and query historical data information; operation record query function, used to query the operation records of each user; report printing function, used for users to print the settlement data of the heating system, and can print daily settlement reports and monthly settlement reports; The maintenance function is used by administrators to log in to the server and configure the system's operating parameters.

6. A smart heating operation system based on the Internet of Things, characterized in that: The method for operating smart heating based on the Internet of Things as described in any one of claims 1 to 5, wherein the operating system comprises: Acquisition module: divide each building according to the map to obtain the heating data of the corresponding users; Monitoring module: monitors the heating data and tracks the energy consumption data, and calculates the total energy consumption of the heating system. The formula is: Where Q is the total energy consumption of the heating system, Q i For each user's load demand, Q loss is the heat loss of the heating system; Analysis module: Establish a mathematical model to analyze the heating data. According to the pipe network topology of the heating system, set a pipe network with N+1 nodes and B pipe sections. The number of each node is n. i , each pipe section is numbered l k , the flow in the pipe network is expressed by the following equations: A is the N*B network association matrix, which represents the connection relationship between each pipe segment and the node; B f is the loop matrix of the pipe network, which represents the relationship between the loop and the pipe section; G is the pipe section flow vector; K is the net flow vector of each node; ΔH is the pipe section pressure drop vector; S is a diagonal matrix, and its diagonal elements S i is the resistance characteristic coefficient of each pipe section; |G| is a diagonal matrix, and its diagonal matrix is ​​the absolute value of the flow rate of each pipe section; DH is the water pump vector of the pipe section; Z is the potential energy difference vector between two nodes in each pipe section branch; Establish a prediction model for the heating system, which includes: basic control layer, prediction control layer and real-time optimization layer; Regulation module: Establish a regulation model for phased variable flow and determine the supply and return water temperature values ​​of the corresponding users; Transmission module: Implement data transmission between the heating system and users through the Internet of Things, and issue early warning notifications to users in real time.

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