Heat supply system secondary network return water temperature prediction method and system based on indoor and outdoor temperature difference

Abstract

The invention belongs to the technical field of heat supply systems, and provides a heat supply system secondary network return water temperature prediction method and system based on indoor and outdoor temperature difference, and the method comprises the steps: obtaining the secondary network water supply temperature, the indoor temperature and the outdoor temperature of a heat supply system, and calculating the secondary network return water temperature of the heat supply system in combination with a heat transfer model; taking the obtained secondary network return water temperature as a state variable, and respectively constructing a state equation and an observation equation for predicting the secondary network return water temperature of the heat supply system; calculating an observed value of the secondary network return water temperature at the next moment according to the constructed state equation and observation equation; and respectively carrying out prediction updating on the constructed state equation and observation equation by adopting extended Kalman filtering to obtain an optimal secondary network return water temperature observation value at the next moment, namely completing the prediction of the secondary network return water temperature of the heat supply system.

Description

Technical Field

[0001] The invention belongs to the technical field of heating systems, and particularly relates to a method and system for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] Central heating is a main form of winter heating in some areas, and a complex heating system is used to achieve the central heating function; the ultimate goal of central heating is to make the indoor temperature reach the standard, and the quality of heating depends on multiple factors. To improve the heating quality, it is necessary to conduct a certain prediction on the heating system to guide the operation and regulation of the heating system. However, the non-linearity, strong internal factor correlation and multiple influencing factors of the heating system make it difficult to predict the heating system; at the same time, the long-distance transmission pipeline network of the heating system has a large time delay, and the data collected from the heating system has a certain amount of noise and error, which also increases the difficulty of the prediction work of the heating system.

[0004] Currently, the research on the prediction of heating systems generally focuses on heat load and temperature; among them, the heat load is the amount of heat required per unit time to reach the indoor temperature required by heat users at a certain determined outdoor temperature; the temperature includes indoor temperature and the return water temperature of the secondary network. However, when using the heat load to predict the heating system, it is necessary to consider the heating and insulation effect of the building, that is, different prediction strategies need to be adopted for different buildings, which lacks generality in the prediction process and has great limitations; when using the temperature to predict the heating system, it is necessary to use thermocouples in the return pipe to measure the return water temperature, and a large number of thermocouples are required to measure the temperature in the return pipe, which increases the cost of predicting the heating system.

[0005] Therefore, it is necessary to carry out relevant research on the prediction of heating systems regarding temperature. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a method and system for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, which can accurately estimate the return water temperature of the secondary network by measuring the indoor and outdoor temperatures without the need for secondary network temperature measurement equipment.

[0007] According to some embodiments, the first solution of the present invention provides a method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, and adopts the following technical solution:

[0008] A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, comprising:

[0009] Obtain the secondary network supply water temperature, indoor temperature, and outdoor temperature of the heating system, and combine with the heat transfer model to calculate the secondary network return water temperature of the heating system;

[0010] Taking the obtained secondary network return water temperature as the state variable, respectively construct the state equation and observation equation for predicting the secondary network return water temperature of the heating system;

[0011] According to the constructed state equation and observation equation, calculate the observed value of the secondary network return water temperature at the next moment;

[0012] Use the extended Kalman filter to perform prediction updates on the constructed state equation and observation equation respectively, and obtain the optimal observed value of the secondary network return water temperature at the next moment, that is, complete the prediction of the secondary network return water temperature of the heating system.

[0013] As a further technical limitation, the specific process of using the extended Kalman filter to perform prediction updates on the constructed state equation and observation equation respectively is:

[0014] Use the extended Kalman filter to calculate the predicted value of the secondary network return water temperature at the next moment of the state equation;

[0015] According to the obtained predicted value of the secondary network return water temperature at the next moment, combine with the observation equation to calculate the predicted value of the observed value of the secondary network return water temperature at the next moment;

[0016] According to the obtained observed value of the secondary network return water temperature at the next moment and the predicted value of the observed value of the secondary network return water temperature at the next moment, calculate the observation error covariance and determine the Kalman gain;

[0017] According to the obtained Kalman gain, iteratively correct the observed value of the secondary network return water temperature at the next moment. When the obtained observation error covariance converges, obtain the optimal observed value of the secondary network return water temperature at the next moment.

[0018] Furthermore, the Kalman gain is the weight used to balance the predicted value of the secondary network return water temperature at the next moment of the state equation and the observed value of the secondary network return water temperature at the next moment of the observation equation.

[0019] As a further technical limitation, the constructed state equation is Wherein, is the state variable at the next moment, F is the sampling matrix, x k is the state variable, ω k is the sampling noise, and the sampling noise ω k conforms to the normal distribution (0, Q k ), and Q k is the covariance of the sampling noise; the constructed observation equation is y k =x k +v k; where y k is the observed value of the state variable, v k is the observation error, and the observation error v k follows a normal distribution (0, R k ), and R k is the covariance of the observation error.

[0020] Furthermore, the sampling matrix adopts a modified identity matrix related to the sampling period, that is, the diagonal elements of the sampling matrix are all 1, and there is exactly one non-diagonal element that is the sampling period, and the other non-diagonal elements are all 0.

[0021] As a further technical limitation, the return water temperature T h,k of the secondary network of the heating system is where T h,k is the return water temperature of the secondary network at time k, T g,k is the supply water temperature of the secondary network at time k, a k is the first experimental coefficient, b k is the second experimental coefficient, and both a k and b k are related to the secondary network pipeline of the heating system; T in,k is the indoor temperature at time k, T out,k is the outdoor temperature at time k, and G k is the instantaneous flow rate of the secondary network at time k.

[0022] According to some embodiments, the second solution of the present invention provides a prediction system for the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, and adopts the following technical solution:

[0023] A prediction system for the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, comprising:

[0024] An acquisition module, which is configured to acquire the supply water temperature, indoor temperature, and outdoor temperature of the secondary network of the heating system, and calculate the return water temperature of the secondary network of the heating system in combination with a heat transfer model;

[0025] A construction module, which is configured to use the obtained return water temperature of the secondary network as the state variable, and respectively construct a state equation and an observation equation for predicting the return water temperature of the secondary network of the heating system;

[0026] A calculation module, which is configured to calculate the observed value of the return water temperature of the secondary network at the next moment according to the constructed state equation and observation equation;

[0027] A prediction module, which is configured to use the extended Kalman filter to respectively perform prediction updates on the constructed state equation and observation equation, and obtain the optimal observed value of the return water temperature of the secondary network at the next moment, that is, complete the prediction of the return water temperature of the secondary network of the heating system.

[0028] According to some embodiments, the third solution of the present invention provides a computer-readable storage medium, and adopts the following technical solution:

[0029] A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it realizes the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in the first solution of the present invention.

[0030] According to some embodiments, the fourth solution of the present invention provides an electronic device, and adopts the following technical solution:

[0031] An electronic device includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it realizes the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in the first solution of the present invention.

[0032] According to some embodiments, the fifth solution of the present invention provides a computer program product, and adopts the following technical solution:

[0033] A computer program product includes software code, and the program in the software code executes the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in the first solution of the present invention.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] Based on the heat transfer model, the present invention obtains the relationship between the return water temperature of the secondary network, the supply water temperature of the secondary network, the outdoor temperature, and the flow rate, constructs the state equation and the observation equation for predicting the return water temperature of the secondary network of the heating system, and combines the extended Kalman filter to optimize and correct the return water temperature, so as to complete the accurate estimation and prediction of the return water temperature of the secondary network by measuring the indoor and outdoor temperatures without the need for secondary network temperature measurement equipment; at the same time, the heating insulation effect of the building does not need to be considered during the process of estimating and predicting the return water temperature of the secondary network, and it is universal for different buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The specification drawings constituting a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation to this embodiment.

[0037] Figure 1 It is a flowchart of the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference in the first embodiment of the present invention;

[0038] Figure 2This is the structural block diagram of the secondary network return water temperature prediction system for the heating system based on the indoor-outdoor temperature difference in the second embodiment of the present invention. Detailed implementation manners

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless otherwise clearly specified in the context, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0042] In the present invention, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only relationship terms determined for the convenience of describing the structural relationship of each component or element of the present invention and do not specifically refer to any component or element of the present invention and should not be construed as a limitation of the present invention.

[0043] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0044] Embodiment 1

[0045] Embodiment 1 of the present invention introduces a method for predicting the secondary network return water temperature of a heating system based on the indoor-outdoor temperature difference.

[0046] As Figure 1 shown, a method for predicting the secondary network return water temperature of a heating system based on the indoor-outdoor temperature difference includes:

[0047] Obtain the secondary network supply water temperature, indoor temperature, and outdoor temperature of the heating system, and calculate the secondary network return water temperature of the heating system in combination with the heat transfer model;

[0048] Taking the obtained secondary network return water temperature as the state variable, respectively construct the state equation and observation equation for predicting the secondary network return water temperature of the heating system;

[0049] According to the constructed state equation and observation equation, calculate the observed value of the secondary network return water temperature at the next moment;

[0050] The extended Kalman filter is used to predict and update the constructed state equation and observation equation respectively, and the optimal observed value of the secondary network return water temperature at the next moment is obtained, that is, the prediction of the secondary network return water temperature of the heating system is completed.

[0051] To reduce the prediction cost and improve the prediction efficiency of the heating system, this embodiment proposes a method for predicting the secondary network return water temperature of the heating system based on the indoor-outdoor temperature difference. Without the secondary network temperature measurement equipment, the accurate estimation of the secondary network return water temperature is achieved by measuring the indoor and outdoor temperatures; based on the heat transfer model, state equation and observation equation, combined with the extended Kalman filter, the prediction and estimation of the secondary network return water temperature of the heating system are realized.

[0052] In the heat transfer model of the heating system, according to the law of conservation of energy, the secondary network return water temperature is equal to the secondary network supply water temperature minus the product of the indoor-outdoor temperature difference and the heat transfer coefficient, that is, secondary network return water temperature = secondary network supply water temperature - heat transfer coefficient × (indoor temperature - outdoor temperature). Before predicting the secondary network return water temperature in this embodiment, combined with the heat transfer model, the relationship between the secondary network return water temperature, secondary network supply water temperature, indoor temperature and outdoor temperature is where, T h,k is the secondary network return water temperature at time k, T g,k is the secondary network supply water temperature at time k, a k is the first experimental coefficient, b k is the second experimental coefficient, T in,k is the indoor temperature at time k, T out,k is the outdoor temperature at time k, G k is the instantaneous flow rate of the secondary network at time k.

[0053] By transforming the obtained secondary network return water temperature formula, we can get

[0054] According to the obtained secondary network return water temperature, the state equation for predicting the secondary network return water temperature of the heating system constructed in this embodiment is where, T g,k is the secondary network supply water temperature at time k, is the secondary network supply water temperature at the next moment of time k (i.e., time k + 1), T h,k is the secondary network return water temperature at time k, is the secondary network return water temperature at the next moment of time k (i.e., time k + 1), is the change rate of the secondary network return water temperature at time k, is the change rate of the secondary network return water temperature at the next moment of time k (i.e., time k + 1), G k is the instantaneous flow rate of the secondary network at time k, is the instantaneous flow rate of the secondary network at the next moment of time k (i.e., time k + 1), ωk is the sampling noise matrix, and the sampling noise is ω k obeys the normal distribution (0, Q k ), where Q k is the covariance matrix of the sampling noise, is the sampling matrix, and Δk is the sampling period.

[0055] It should be noted that the sampling matrix in this embodiment is a correction identity matrix related to the sampling period; that is, a matrix with all diagonal elements equal to 1, and exactly one non-diagonal element equal to the sampling period, and all other non-diagonal elements equal to 0.

[0056] The observation equation for predicting the return water temperature of the secondary network of the heating system constructed in this embodiment is where T g,k is the observed value of the supply water temperature of the secondary network at time k, T in,k is the observed value of the indoor temperature at time k, T out,k is the observed value of the outdoor temperature at time k, and v k is the observation error matrix, and the observation error v k obeys the normal distribution (0, R k ), where R k is the covariance matrix of the observation error.

[0057] In this embodiment, the extended Kalman filter is used for data fusion, and the optimal observed value of the return water temperature of the secondary network at the next moment is obtained based on prediction and update iteration, that is, the prediction of the return water temperature of the secondary network of the heating system is completed. Specifically:

[0058] Using the extended Kalman filter, calculate the predicted value of the return water temperature of the secondary network at time k + 1

[0059] According to the predicted value of the return water temperature of the secondary network at the next moment obtained Combined with the observation equation, calculate the observed value of the return water temperature of the secondary network at time k + 1 Predicted value

[0060] According to the observed value of the return water temperature of the secondary network at time k + 1 obtained and the predicted value of the observed value of the return water temperature of the secondary network at time k + 1 Calculate the observation error covariance R k , and determine the Kalman gain K k ;

[0061] According to the obtained Kalman gain K k , iteratively correct the observed value of the return water temperature of the secondary network at time k + 1​​ When the obtained observation error covariance R k converges (i.e., the observation error covariance R k tends to a certain value), the optimal secondary network return water temperature observation value at the next moment is obtained, that is, the prediction of the secondary network return water temperature of the heating system is completed.

[0062] It should be noted that the prediction and update iteration based on the extended Kalman belong to the technologies that those skilled in the art should know, and will not be elaborated in this embodiment.

[0063] It should be noted that the Kalman gain K in this embodiment k is used to balance the predicted value of the secondary network return water temperature at the (k + 1)-th moment of the state equation and the observed value of the secondary network return water temperature at the (k + 1)-th moment of the observation equation of the weights.

[0064] Based on the heat transfer model, this embodiment obtains the relationship between the secondary network return water temperature and the secondary network supply water temperature, outdoor temperature and flow rate, constructs the state equation and observation equation for predicting the secondary network return water temperature of the heating system, and combines the extended Kalman filter to optimize and correct the secondary return water temperature, so as to complete the accurate estimation and prediction of the secondary network return water temperature by measuring the indoor and outdoor temperatures without the secondary network temperature measurement equipment.

[0065] Embodiment 2

[0066] Embodiment 2 of the present invention introduces a prediction system for the secondary network return water temperature of a heating system based on the indoor-outdoor temperature difference.

[0067] As Figure 2 shown, a prediction system for the secondary network return water temperature of a heating system based on the indoor-outdoor temperature difference includes:

[0068] An acquisition module, which is configured to acquire the secondary network supply water temperature, indoor temperature and outdoor temperature of the heating system, and calculate the secondary network return water temperature of the heating system in combination with the heat transfer model;

[0069] A construction module, which is configured to use the obtained secondary network return water temperature as the state variable to respectively construct the state equation and observation equation for predicting the secondary network return water temperature of the heating system;

[0070] A calculation module, which is configured to calculate the observed value of the secondary network return water temperature at the next moment according to the constructed state equation and observation equation;

[0071] A prediction module, which is configured to use the extended Kalman filter to respectively perform prediction and update on the constructed state equation and observation equation to obtain the optimal secondary network return water temperature observation value at the next moment, that is, to complete the prediction of the secondary network return water temperature of the heating system.

[0072] The detailed steps are the same as those of the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference provided in Embodiment 1, and will not be elaborated here.

[0073] Embodiment 3

[0074] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0075] A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in Embodiment 1 of the present invention.

[0076] The detailed steps are the same as those of the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference provided in Embodiment 1, and will not be elaborated here.

[0077] Embodiment 4

[0078] Embodiment 4 of the present invention provides an electronic device.

[0079] An electronic device includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in Embodiment 1 of the present invention.

[0080] The detailed steps are the same as those of the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference provided in Embodiment 1, and will not be elaborated here.

[0081] Embodiment 5

[0082] Embodiment 5 of the present invention provides a computer program product.

[0083] A computer program product includes software code, and the program in the software code implements the steps in the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference as described in Embodiment 1 of the present invention.

[0084] The detailed steps are the same as those of the method for predicting the return water temperature of the secondary network of the heating system based on the indoor-outdoor temperature difference provided in Embodiment 1, and will not be elaborated here.

[0085] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0086] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0087] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0090] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

[0091] The above are only the preferred embodiments of this embodiment and are not used to limit this embodiment. For those skilled in the art, this embodiment can have various changes and alterations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this embodiment shall be included within the protection scope of this embodiment.

Claims

1. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, characterized in that, Including: Obtain the secondary network supply water temperature, indoor temperature, and outdoor temperature of the heating system, and calculate the secondary network return water temperature of the heating system in combination with the heat transfer model; Taking the obtained secondary network return water temperature as the state variable, respectively construct the state equation and observation equation for predicting the secondary network return water temperature of the heating system; According to the constructed state equation and observation equation, calculate the observed value of the secondary network return water temperature at the next moment; Use the extended Kalman filter to perform prediction updates on the constructed state equation and observation equation respectively, and obtain the optimal observed value of the secondary network return water temperature at the next moment, that is, complete the prediction of the secondary network return water temperature of the heating system.

2. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference as described in claim 1, characterized in that, The specific process of using the extended Kalman filter to perform prediction updates on the constructed state equation and observation equation respectively is as follows: Use the extended Kalman filter to calculate the predicted value of the secondary network return water temperature at the next moment of the state equation; According to the obtained predicted value of the secondary network return water temperature at the next moment, in combination with the observation equation, calculate the predicted value of the observed value of the secondary network return water temperature at the next moment; According to the obtained observed value of the secondary network return water temperature at the next moment and the predicted value of the observed value of the secondary network return water temperature at the next moment, calculate the observation error covariance and determine the Kalman gain; According to the obtained Kalman gain, iteratively correct the observed value of the secondary network return water temperature at the next moment. When the obtained observation error covariance converges, obtain the optimal observed value of the secondary network return water temperature at the next moment.

3. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference as described in claim 2, characterized in that, The Kalman gain is the weight used to balance the predicted value of the secondary network return water temperature at the next moment of the state equation and the observed value of the secondary network return water temperature at the next moment of the observation equation.

4. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference as described in claim 1, characterized in that, The constructed state equation is where is the state variable at the next moment, F is the sampling matrix, x k is the state variable, ω k is the sampling noise, and the sampling noise ω k follows a normal distribution (0, Q k ), and Q k is the covariance of the sampling noise; the constructed observation equation is y k = x k + v k ; where y k is the observed value of the state variable, v k is the observation error, and the observation error v k follows a normal distribution (0, R k ), and R k is the covariance of the observation error.

5. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference, characterized in that, The sampling matrix uses a modified identity matrix related to the sampling period, that is, the diagonal elements of the sampling matrix are all 1, and there is only one non-diagonal element that is the sampling period, and the other non-diagonal elements are all 0.

6. A method for predicting the return water temperature of the secondary network of a heating system based on the indoor-outdoor temperature difference as described in claim 1, characterized in that, The return water temperature T of the secondary network of the heating system h,k is wherein, T h,k is the return water temperature of the secondary network at time k, T g,k is the supply water temperature of the secondary network at time k, a k is the first experimental coefficient, b k is the second experimental coefficient, T in,k is the indoor temperature at time k, T out,k is the outdoor temperature at time k, G k is the instantaneous flow rate of the secondary network at time k.

7. A secondary network return water temperature prediction system for a heating system based on the indoor-outdoor temperature difference, characterized in that, Including: An acquisition module, which is configured to obtain the secondary network supply water temperature, indoor temperature, and outdoor temperature of the heating system, and calculate the secondary network return water temperature of the heating system in combination with the heat transfer model; A construction module, which is configured to take the obtained secondary network return water temperature as the state variable, and respectively construct the state equation and observation equation for predicting the secondary network return water temperature of the heating system; A calculation module, which is configured to calculate the observed value of the secondary network return water temperature at the next moment according to the constructed state equation and observation equation; A prediction module, which is configured to use the extended Kalman filter to perform prediction updates on the constructed state equation and observation equation respectively, and obtain the optimal observed value of the secondary network return water temperature at the next moment, that is, complete the prediction of the secondary network return water temperature of the heating system.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for predicting the secondary network return water temperature of the heating system based on the indoor-outdoor temperature difference as described in any one of claims 1-6.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for predicting the secondary network return water temperature of the heating system based on the indoor-outdoor temperature difference as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the method for predicting the secondary network return water temperature of the heating system based on the indoor-outdoor temperature difference as described in any one of claims 1-6.

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