Control method of water mixing heat supply system

Abstract

The invention discloses a control method of a mixed water heat supply system. The control method comprises the steps that operation data of a plurality of mixed water pipe networks in the mixed water heat supply system within a preset time period are obtained; clustering the operation data of the plurality of water mixing pipe networks in a preset time period, and determining clustering operation data corresponding to the plurality of water mixing pipe networks; performing similarity analysis based on the clustering operation data corresponding to the plurality of water mixing pipe networks to obtain a similarity analysis result; based on the similarity analysis result and the energy consumption parameters of the multiple water mixing pipe networks, target water mixing pipe networks corresponding to the multiple water mixing pipe networks are determined, and according to the target operation parameters of the target water mixing pipe networks, the operation states of the water mixing pipe networks corresponding to the target water mixing pipe networks are controlled. The problem that in the prior art, when the mixed water heat supply system is controlled, a complex calculation process is needed, the mixed water heat supply system cannot be controlled in time, and then energy waste is easily caused is solved.

Description

Technical Field

[0001] The present application relates to the technical field of heating systems, and in particular to a control method for a mixed water heating system. Background Art

[0002] The large size of the heating system and the high base of energy consumption have prompted the heating industry and academia to work together to explore more efficient and environmentally friendly operation modes. In this context, the mixed water heating system, as an innovative technology, has gradually attracted attention and been widely used. Mixed water heating achieves the heating temperature required by users by mixing high-temperature heat source water and low-temperature return water in the main water supply pipeline, thereby achieving precise energy utilization and effective temperature control. This technology has been successful in many projects, demonstrating its potential in energy conservation and emission reduction.

[0003] In the related technology, it is usually to establish an energy consumption model of the mixed water heating system, and then determine the operating parameters of the mixed water heating system based on the energy consumption model. The energy consumption model of this method can reflect the operating status of the entire system, perform energy consumption prediction, and thus achieve energy-saving regulation. However, since the pipe network model is established based on the heat network system model, and the energy consumption calculation of the heat network system model is performed by simulating the entire system from the heat source to the heat user, a relatively complex calculation process is required, which not only has a large amount of calculation, but also a long calculation time, making it difficult to achieve efficient operation of the online operation and regulation of the mixed water heating system.

[0004] Therefore, how to accurately and efficiently control the operating status of the mixed water heating system and thereby reduce the energy consumption of the mixed water heating system has become a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiment of the present application provides a control method for a mixed water heating system to at least solve the problem in the related art that a complex calculation process is required when controlling the mixed water heating system, the mixed water heating system cannot be controlled in a timely manner, and energy waste is easily caused.

[0006] According to an embodiment of the embodiments of the present application, a control method for a mixed water heating system is provided, including: obtaining operating data of multiple mixed water pipes in the mixed water heating system within a preset time period; clustering the operating data of the multiple mixed water pipes within the preset time period respectively, and determining the clustered operating data corresponding to the multiple mixed water pipes respectively; performing similarity analysis based on the clustered operating data corresponding to the multiple mixed water pipes respectively, and obtaining similarity analysis results; determining target mixed water pipes corresponding to the multiple mixed water pipes respectively based on the similarity analysis results and energy consumption parameters of the multiple mixed water pipes, and controlling the operating status of the mixed water pipes corresponding to the target mixed water pipes according to the target operating parameters of the target mixed water pipes.

[0007] In an exemplary embodiment, the preset time period includes multiple time points; the operation data of the multiple mixed water pipe networks within the preset time period are clustered respectively to determine the clustered operation data corresponding to the multiple mixed water pipe networks, including: for any one of the multiple mixed water pipe networks, determining the operation data corresponding to the targeted mixed water pipe network at multiple time points; based on the operation data corresponding to the targeted mixed water pipe network at multiple time points, determining the cluster set of the targeted mixed water pipe network; the cluster set includes K data points, wherein K is greater than 1 and K is a positive integer; based on the cluster set of the targeted mixed water pipe network, determining the clustered operation data of the targeted mixed water pipe network.

[0008] In an exemplary embodiment, the method of determining the cluster set of the mixed water pipe network targeted based on the operation data corresponding to the targeted mixed water pipe network at multiple time points respectively includes: determining at least two data points from the multiple time points; selecting any other time point from other time points to be determined as a target time point, and calculating the distance parameters between the target time point and the at least two data points respectively based on the operation data of the target time point and the current operation data of the at least two data points; wherein the other time points are time points other than the data points among the multiple time points; according to the distance parameters, the operation data of the other time points are assigned to matching data points, and the average operation data of all time points in the data points are calculated; based on the average operation data, the current operation data of the data point is updated, a new other time point is selected from other time points to be determined as a target time point, and the step of calculating the distance parameters between the target time point and the at least two data points respectively based on the operation data of the target time point and the current operation data of the at least two data points is returned, and the step of stopping until the first preset condition is met to determine the cluster set of the targeted mixed water pipe network.

[0009] In an exemplary embodiment, the similarity analysis is performed based on the clustered operation data respectively corresponding to the multiple mixed water pipe networks to obtain a similarity analysis result, including: for any one of the multiple mixed water pipe networks, based on the clustered operation data of the targeted mixed water pipe network and the clustered operation data of mixed water pipe networks other than the targeted mixed water pipe network among the multiple mixed water pipe networks, determining the similarity coefficient between the targeted mixed water pipe network and the mixed water pipe networks other than the targeted mixed water pipe network.

[0010] In an exemplary embodiment, the method of determining the similarity coefficient between the mixed water network targeted and the mixed water networks other than the targeted mixed water network based on the clustered operation data of the targeted mixed water network and the clustered operation data of the mixed water networks other than the targeted mixed water network in the multiple mixed water networks includes: determining the mixed water network to be calculated from the mixed water networks other than the targeted mixed water network; combining the clustered operation data of the targeted mixed water network and the clustered operation data of the mixed water network to be calculated with The method comprises the steps of: bringing the clustering operation data of the mixed water network targeted and the clustering operation data of the mixed water network to be calculated into the pre-constructed similarity expression to determine the similarity coefficient between the mixed water network targeted and the mixed water network to be calculated; re-determining a new mixed water network to be calculated from mixed water networks other than the targeted mixed water network, and returning to the step of bringing the clustering operation data of the targeted mixed water network and the clustering operation data of the mixed water network to be calculated into the pre-constructed similarity expression to determine the similarity coefficient between the mixed water network targeted and the mixed water network to be calculated, and stopping when the second preset condition is met.

[0011] In an exemplary embodiment, the similarity analysis result includes a similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network; the determination of the target mixed water networks corresponding to the multiple mixed water networks respectively based on the similarity analysis result and the energy consumption parameters of the multiple mixed water networks includes: for any mixed water network, based on the similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network, selecting a preset number of initial target mixed water networks from mixed water networks other than the targeted mixed water network; extracting the initial energy consumption parameters of the preset number of initial target mixed water networks from the energy consumption parameters of the multiple mixed water networks, and performing energy consumption analysis on the preset number of initial target mixed water networks based on the initial energy consumption parameters; and determining the target mixed water network of the targeted mixed water network from the preset number of initial target mixed water networks based on the energy consumption analysis result.

[0012] In an exemplary embodiment, the target mixed water network of the mixed water network is determined from the preset number of initial target mixed water networks based on the energy consumption analysis result, including: obtaining indoor temperature data in the mixed water heating system; based on the indoor temperature data and the energy consumption analysis result, determining the target mixed water network of the mixed water network from the preset number of initial target mixed water networks.

[0013] According to another embodiment of the embodiment of the present application, a control device for a mixed water heating system is also provided, including: an acquisition module, used to acquire operating data of multiple mixed water pipes in the mixed water heating system within a preset time period; a determination module, used to cluster the operating data of the multiple mixed water pipes within the preset time period, and determine the clustered operating data corresponding to the multiple mixed water pipes; an analysis module, used to perform similarity analysis based on the clustered operating data corresponding to the multiple mixed water pipes, and obtain similarity analysis results; a control module, used to determine target mixed water pipes corresponding to the multiple mixed water pipes based on the similarity analysis results and energy consumption parameters of the multiple mixed water pipes, and control the operating status of the mixed water pipes corresponding to the target mixed water pipes according to the target operating parameters of the target mixed water pipes.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] In an embodiment of the present application, by acquiring the operating data of multiple mixed water pipe networks in a mixed water heating system within a preset time period, cluster analysis is performed on them to determine the clustered operating data of each mixed water pipe network, and then the clustered operating data is used as the basis for similarity analysis, combined with energy consumption parameters, to find the target mixed water pipe network corresponding to each mixed water pipe network, and according to the operating parameters of the target mixed water pipe network, the operating state of the mixed water pipe network corresponding to the target mixed water pipe network is controlled. In other words, in the process of controlling the operating state of the mixed water pipe network, not only the similarity of the clustered operating data is considered, but also the energy consumption parameters are comprehensively analyzed. By selecting a mixed water pipe network that meets the requirements in both similarity and energy consumption as the target mixed water pipe network, the operating state adjustment of the mixed water control system can be quickly and accurately guided, and unnecessary energy waste can be reduced, thereby achieving a significant reduction in the overall energy consumption of the mixed water heating system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a control method of a mixed water heating system according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a control method of a mixed water heating system according to an embodiment of the present application;

[0021] Figure 3 It is a structural block diagram of a control device for a mixed water heating system according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a control method for a mixed water heating system according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0025] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] In this embodiment, a control method for a mixed water heating system is provided, which is applied to the above-mentioned computer device. Figure 2 : is a flow chart of a control method for a mixed water heating system according to an embodiment of the present application, the flow chart includes the following steps:

[0028] Step S202, obtaining operation data of multiple mixed water pipe networks in the mixed water heating system within a preset time period;

[0029] Step S204, clustering the operation data of the plurality of mixed water pipe networks within a preset time period respectively, and determining the clustered operation data corresponding to the plurality of mixed water pipe networks respectively;

[0030] Step S206, performing similarity analysis based on the clustered operation data corresponding to the plurality of mixed water pipe networks to obtain a similarity analysis result;

[0031] Step S208, based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, determine the target mixed water pipe networks corresponding to the multiple mixed water pipe networks respectively, and control the operating state of the mixed water pipe network corresponding to the target mixed water pipe network according to the target operating parameters of the target mixed water pipe network.

[0032] In an embodiment of the present application, by acquiring the operating data of multiple mixed water pipe networks in a mixed water heating system within a preset time period, cluster analysis is performed on them to determine the clustered operating data of each mixed water pipe network, and then the clustered operating data is used as the basis for similarity analysis, combined with energy consumption parameters, to find the target mixed water pipe network corresponding to each mixed water pipe network, and according to the operating parameters of the target mixed water pipe network, the operating state of the mixed water pipe network corresponding to the target mixed water pipe network is controlled. In other words, in the process of controlling the operating state of the mixed water pipe network, not only the similarity of the clustered operating data is considered, but also the energy consumption parameters are comprehensively analyzed. By selecting a mixed water pipe network that meets the requirements in both similarity and energy consumption as the target mixed water pipe network, the operating state adjustment of the mixed water control system can be quickly and accurately guided, and unnecessary energy waste can be reduced, thereby achieving a significant reduction in the overall energy consumption of the mixed water heating system.

[0033] In an exemplary embodiment, the preset time period includes multiple time points; the operation data of the multiple mixed water pipe networks within the preset time period are clustered respectively to determine the clustered operation data corresponding to the multiple mixed water pipe networks, including: for any one of the multiple mixed water pipe networks, determining the operation data corresponding to the targeted mixed water pipe network at multiple time points; based on the operation data corresponding to the targeted mixed water pipe network at multiple time points, determining the cluster set of the targeted mixed water pipe network; the cluster set includes K data points, wherein K is greater than 1 and K is a positive integer; based on the cluster set of the targeted mixed water pipe network, determining the clustered operation data of the targeted mixed water pipe network.

[0034] The preset time period is a representative time window selected during data analysis, which can be a day, a week or a longer period, depending on the operating rules of the mixed water heating system and the data analysis requirements. The time period includes multiple time points, which can be equally spaced, such as once an hour, or can be determined based on the frequency of data collection for actual operation. The selection of time points should cover various states and modes of operation of the mixed water heating system to facilitate a more comprehensive analysis of its operating characteristics.

[0035] For each mixed water network within the selected time period, it is necessary to collect its operating data at multiple time points, including but not limited to key parameters such as flow, temperature, pressure and valve opening. These data form the basis of the operating status of the mixed water heating system and provide material for subsequent cluster analysis. Before cluster analysis, data cleaning is an essential step, including the removal of abnormal data, data filling and unified time scale processing to ensure the accuracy and reliability of the analysis results.

[0036] The K-means clustering algorithm is used to analyze the collected mixed water pipe network operation data. K-means is a distance-based clustering method whose goal is to divide the data set into K clusters so that the distance between the data points within each cluster is minimized, while the distance between the data points in different clusters is as large as possible. After completing K-means clustering, each mixed water pipe network will have a set of clusters corresponding to its operating status. The K data points in the cluster set represent the typical characteristics of the mixed water pipe network under different operating modes.

[0037] In the above embodiment, the operation modes of the mixed water heating system at different time points, as well as the differences and connections between these modes, can be identified. This not only helps to understand the operation rules of the system, but also provides data support for the formulation of predictive control strategies, and by selecting the optimal operation mode, accurate control of the operation of the mixed water heating system and energy saving and emission reduction can be achieved.

[0038] In an exemplary embodiment, the method of determining the cluster set of the mixed water pipe network targeted based on the operation data corresponding to the targeted mixed water pipe network at multiple time points respectively includes: determining at least two data points from the multiple time points; selecting any other time point from other time points to be determined as a target time point, and calculating the distance parameters between the target time point and the at least two data points respectively based on the operation data of the target time point and the current operation data of the at least two data points; wherein the other time points are time points other than the data points among the multiple time points; according to the distance parameters, the operation data of the other time points are assigned to matching data points, and the average operation data of all time points in the data points are calculated; based on the average operation data, the current operation data of the data point is updated, a new other time point is selected from other time points to be determined as a target time point, and the step of calculating the distance parameters between the target time point and the at least two data points respectively based on the operation data of the target time point and the current operation data of the at least two data points is returned, and the step of stopping until the first preset condition is met to determine the cluster set of the targeted mixed water pipe network.

[0039] It is understandable that at least two data points are randomly selected from the multiple time points as the initial cluster centers. These data points will serve as reference points at the beginning of the K-means algorithm and will be used for clustering allocation of subsequent data points. The number of clusters K of the K-means algorithm is determined based on preset analysis requirements, and K is usually greater than 1 and is a positive integer to ensure that different operating modes can be identified.

[0040] Next, one of the remaining time points is selected as the target time point, and then the distance between the running data at the target time point and the currently selected cluster center is calculated. The distance parameter can use Euclidean distance, Manhattan distance, or other distance measurement methods, depending on the characteristics of the running data and the needs of cluster analysis. The purpose of this calculation is to determine which cluster center the running data at the target time point is closest to.

[0041] According to the calculated distance parameter, the operation data at the target time point is assigned to the cluster corresponding to the cluster center closest to it. This step ensures that each operation data point is correctly classified into the group that is most similar to its operation mode.

[0042] After allocating the running data of the target time point, the average value of the running data of all time points allocated to the same cluster is calculated as the new cluster center. This process can reflect the average running status of the data points within the cluster, so that the cluster center can more accurately represent the characteristics of the cluster.

[0043] Repeat the above steps, continuously select new target time points, calculate distance parameters, assign data points, and update cluster centers until the data at all time points are assigned to the corresponding clusters. Then, check the change of cluster centers. If the change of cluster centers is less than the preset threshold or reaches the maximum number of iterations, stop the iteration, and the cluster set at this time is the final cluster set of the mixed water pipe network.

[0044] The condition for stopping iteration (the first preset condition) is to ensure that the algorithm can converge to a stable clustering result. This condition is usually set as the change of the cluster center is less than a very small value (for example, 0.001), or the number of iterations reaches a preset value (for example, 100 times). When one of these conditions is met, the algorithm considers that the clustering result has stabilized, and the iteration can be stopped and the final cluster set can be output.

[0045] After completing the above iterative process, each mixed water network will have a cluster set containing K cluster centers, which represent the characteristics of the network under different operation modes. The cluster set provides a key data structure for subsequent similarity analysis and operation status prediction.

[0046] In the above embodiments, clustering can be used to automatically identify the operating mode in the mixed water heating system, providing a basis for similarity measurement and optimization of operating parameters, thereby realizing intelligent control and energy saving and emission reduction of the mixed water heating system.

[0047] In an exemplary embodiment, the similarity analysis is performed based on the clustered operation data respectively corresponding to the multiple mixed water pipe networks to obtain the similarity analysis result, including: for any one of the multiple mixed water pipe networks, based on the clustered operation data of the targeted mixed water pipe network and the clustered operation data of mixed water pipe networks other than the targeted mixed water pipe network among the multiple mixed water pipe networks, determining the similarity coefficient between the targeted mixed water pipe network and the mixed water pipe networks other than the targeted mixed water pipe network.

[0048] In an exemplary embodiment, the method of determining the similarity coefficient between the mixed water network targeted and the mixed water networks other than the targeted mixed water network based on the clustered operation data of the targeted mixed water network and the clustered operation data of the mixed water networks other than the targeted mixed water network in the multiple mixed water networks includes: determining the mixed water network to be calculated from the mixed water networks other than the targeted mixed water network; combining the clustered operation data of the targeted mixed water network and the clustered operation data of the mixed water network to be calculated with The method comprises the steps of: bringing the clustering operation data of the mixed water network targeted and the clustering operation data of the mixed water network to be calculated into the pre-constructed similarity expression to determine the similarity coefficient between the mixed water network targeted and the mixed water network to be calculated; re-determining a new mixed water network to be calculated from mixed water networks other than the targeted mixed water network, and returning to the step of bringing the clustering operation data of the targeted mixed water network and the clustering operation data of the mixed water network to be calculated into the pre-constructed similarity expression to determine the similarity coefficient between the mixed water network targeted and the mixed water network to be calculated, and stopping when the second preset condition is met.

[0049] It should be noted that after completing the cluster analysis of a mixed water pipe network (the targeted mixed water pipe network), a mixed water pipe network to be calculated is selected from the remaining mixed water pipe networks. The mixed water pipe network to be calculated will be used to measure similarity with the targeted mixed water pipe network.

[0050] A predefined similarity expression, usually a formula based on the Pearson correlation coefficient, is used to calculate the similarity between the clustered operation data of the two mixed water networks. The Pearson correlation coefficient measures the linear correlation between two variables, with a value range of -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0051] Substitute the clustered operation data of the mixed water pipe network and the mixed water pipe network to be calculated into the above similarity expression to calculate the similarity coefficient between the two. This calculation process is essentially to evaluate the similarity between the two mixed water pipe networks in terms of operation characteristics.

[0052] After calculating the similarity coefficient between the first mixed water pipe network to be calculated and the mixed water pipe network being calculated, the next mixed water pipe network to be calculated is selected from the remaining mixed water pipe networks, and the similarity coefficient calculation process is repeated. This cycle is repeated until all mixed water pipe networks have been calculated.

[0053] During the cyclic calculation process, a second preset condition is set as the basis for stopping the iteration. This condition can be that the similarity coefficients of all mixed water pipe networks have been calculated, or that the change in the similarity coefficient is less than a preset threshold in several consecutive calculations, indicating that the algorithm has converged and no further calculation is required. When the second preset condition is met, the algorithm stops and outputs all calculated similarity coefficients.

[0054] In an exemplary embodiment, the similarity analysis result includes a similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network; the determination of the target mixed water networks corresponding to the multiple mixed water networks respectively based on the similarity analysis result and the energy consumption parameters of the multiple mixed water networks includes: for any mixed water network, based on the similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network, selecting a preset number of initial target mixed water networks from mixed water networks other than the targeted mixed water network; extracting the initial energy consumption parameters of the preset number of initial target mixed water networks from the energy consumption parameters of the multiple mixed water networks, and performing energy consumption analysis on the preset number of initial target mixed water networks based on the initial energy consumption parameters; and determining the target mixed water network of the targeted mixed water network from the preset number of initial target mixed water networks based on the energy consumption analysis result.

[0055] It is understandable that based on the cluster analysis previously performed, clustered operation data of all mixed water pipe networks have been obtained. Next, the similarity coefficient between the mixed water pipe network in question and other mixed water pipe networks in the system is calculated using the Pearson correlation coefficient or a similar indicator. The similarity coefficient can quantify the similarity of the operation modes between networks, with values ​​close to 1 indicating high similarity and values ​​close to 0 indicating no correlation.

[0056] For any mixed water network, according to its similarity coefficient with other mixed water networks in the system, a preset number (e.g. 10%) of mixed water networks with the highest similarity coefficient are selected as the initial target mixed water networks. This preset number can be adjusted according to the system scale and analysis accuracy requirements. Selecting networks with high similarity as the initial target means that these networks have something in common with the targeted mixed water network in terms of operation mode and may be the best candidates for energy-saving operation.

[0057] The energy consumption parameters of the preset number of initial target mixed water pipe networks are extracted. These parameters include but are not limited to electricity consumption, heat consumption, etc., which reflect the energy usage of the mixed water pipe network. Based on these energy consumption parameters, the energy consumption of the initial target mixed water pipe network can be analyzed to evaluate its energy utilization efficiency while meeting the heating demand.

[0058] After energy consumption analysis of a preset number of initial target mixed water pipe networks, the mixed water pipe network with the lowest energy consumption is further selected as the target mixed water pipe network based on the energy consumption analysis results. This selection not only takes into account the similarity of the operation mode, but also combines the actual energy consumption data to ensure that the target mixed water pipe network not only has a similar operation mode, but also is a model of energy-saving operation.

[0059] Finally, the determined operating parameters of the target mixed water network are applied to the targeted mixed water network to guide it to adjust its operating state. In this way, the targeted mixed water network can learn from the operating mode of the target mixed water network while meeting the heating demand, achieve more efficient energy utilization, and achieve the purpose of energy conservation and emission reduction.

[0060] In a specific application, assuming that there are 50 mixed water networks in the mixed water heating system, for a certain mixed water network A, first calculate its similarity coefficient with the other 49 mixed water networks. Then, select the top 5 mixed water networks with the highest similarity coefficient as the initial target mixed water networks B1, B2, B3, B4, and B5. Next, extract energy consumption parameters, such as electricity and heat energy consumption, from these 5 initial target mixed water networks for energy consumption analysis. Assuming that the analysis results show that the energy consumption of the B3 network is the lowest, and the room temperature data also meets the user's needs, then the B3 network is determined as the target mixed water network of the mixed water network A. Finally, according to the operating parameters of the B3 network (such as valve opening, pump speed, etc.), adjust the operating status of the mixed water network A in order to achieve an operating efficiency similar to that of the B3 network and achieve the energy saving goal.

[0061] In an exemplary embodiment, the target mixed water network of the mixed water network is determined from the preset number of initial target mixed water networks based on the energy consumption analysis result, including: obtaining indoor temperature data in the mixed water heating system; based on the indoor temperature data and the energy consumption analysis result, determining the target mixed water network of the mixed water network from the preset number of initial target mixed water networks.

[0062] Among them, indoor temperature data is collected from the mixed water heating system. This data usually comes from the temperature sensors installed in the user's room, which can reflect in real time whether the heating effect meets the user's needs. Indoor temperature is an important indicator to measure the performance of the mixed water heating system because it is directly related to user comfort and satisfaction.

[0063] After completing the cluster analysis of the mixed water pipe network, a preset number (e.g., 10%) of initial target mixed water pipe networks have been selected based on the similarity coefficient. Next, the energy consumption parameters of these initial target mixed water pipe networks need to be further analyzed to determine which pipe network has the lowest energy consumption while meeting the user's indoor temperature requirements. Energy consumption analysis involves evaluating the energy efficiency of each initial target mixed water pipe network, including consumption of electrical energy, thermal energy, etc., to identify the mixed water pipe network with the best energy consumption.

[0064] Based on the indoor temperature data and energy consumption analysis results, a preset number of initial target mixed water pipe networks are double-screened. First, confirm whether the indoor temperature of these pipe networks meets the preset comfort standards. For example, if the standard indoor temperature is set between 20°C and 22°C, then the mixed water pipe networks that keep the indoor temperature within this range are given priority. Secondly, the energy consumption of these qualified mixed water pipe networks is compared, and the one with the lowest energy consumption is selected as the target mixed water pipe network. This is because, even if the operating modes of two mixed water pipe networks are similar, if there are differences in their energy consumption and indoor temperature control effects, then the pipe network with lower energy consumption and more stable indoor temperature is more suitable as a reference target.

[0065] After the above screening process, the final target mixed water pipe network is not only highly similar to the targeted mixed water pipe network in terms of operation mode, but also performs best in terms of energy efficiency and indoor temperature control. This target mixed water pipe network will become the optimization direction of the targeted mixed water pipe network, guiding its adjustment of operation parameters in order to achieve or approach the operation efficiency and heating effect of the target mixed water pipe network.

[0066] In order to better understand the process of the control method of the above-mentioned mixed water heating system, the control method of the above-mentioned mixed water heating system is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0067] In recent years, the energy consumption of heating has been huge, and the importance of energy conservation and emission reduction in heating systems has been increasingly highlighted. Due to the large size of the heating system and the large base of energy consumption, heating-related practitioners are constantly planning new modes of heating system operation. Mixed water systems have also become an important research direction, and there are currently many successful practice cases. Therefore, it is also necessary to study an effective control method for mixed water heating systems.

[0068] In order to solve the above technical problems, the present application provides a control method for a mixed water heating system, comprising the following steps:

[0069] Step 1: Establish a basic model for the operation of the mixed water heating network.

[0070] Based on the mixed water pipe network operation monitoring system, the operation data of each monitoring point is obtained, including flow, temperature, pressure, valve opening and other data, and the operation data is cleaned, including abnormal data judgment, data filling, and unified time scale processing.

[0071] Perform K-means cluster analysis on the operation data of one of the mixed water pipe networks, and divide the historical operation data set of the mixed water pipe network into K clusters, that is, randomly initialize K data points as the initial centroids. For each data point in the data set, calculate its distance to the centroid and assign it to the cluster with the nearest centroid. For each cluster, calculate the mean of all data points, update the centroid data back to the initial step, and repeat the calculation until the centroid no longer changes significantly. Traverse this process until all different mixed water pipe networks have completed K-means cluster analysis and calculated all centroids.

[0072] Step 2: Measure the similarity between the basic models.

[0073] For two similar cluster centers, the similarity coefficients between basic models belonging to different mixed water pipe networks are calculated based on the Pearson correlation coefficient principle:

[0074]

[0075] Where: x i ,y i The cluster operation data of the pipe network model belonging to two similar cluster centers, i is the data volume of the cluster operation data.

[0076] This process is repeated until all N similar cluster centers have completed the calculation of the correlation coefficients between each other, and the correlation coefficients are sorted. The closer the correlation coefficient is to 1, the greater the correlation between the two items.

[0077] Step 3: Predict regulatory data based on similarity indicators.

[0078] At the same time, after finding the energy consumption operation data of the 10% mixed water networks with the largest correlation coefficient and sorting them, the mixed water network with the lowest energy consumption is the reference target. If room temperature data exists, the room temperature data should also become one of the conditions for screening the reference target. Therefore, for a certain mixed water system, no matter what operating state it is in, the above steps can be used to continuously find a certain operating state of another mixed water system that is most similar to it, and then the operation adjustment parameters of the mixed water system found can be used to determine how to guide the adjustment.

[0079] Beneficial effects: This application measures the similarity of historical data of different mixed water pipe networks, compares and analyzes the operating status data of the mixed water heating system to determine similar pipe networks, and selects the mixed water pipe network with the lowest energy consumption as the target reference pipe network, making full use of the operating data for value mining, making the adjustment of the mixed water pipe network more precise and meeting the heating needs of residents.

[0080] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0081] The present application also provides a structural block diagram of a control device for a mixed water heating system. Figure 3 is a structural block diagram of a control device for a mixed water heating system according to an embodiment of the present application; Figure 3 As shown, including:

[0082] An acquisition module 32 is used to acquire operation data of multiple mixed water pipe networks in a mixed water heating system within a preset time period;

[0083] A determination module 34, configured to cluster the operation data of the plurality of mixed water pipe networks within a preset time period, and determine the clustered operation data corresponding to the plurality of mixed water pipe networks;

[0084] An analysis module 36, configured to perform similarity analysis based on the clustered operation data respectively corresponding to the plurality of mixed water pipe networks to obtain a similarity analysis result;

[0085] The control module 38 is used to determine the target mixed water pipe networks corresponding to the multiple mixed water pipe networks respectively based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, and control the operating state of the mixed water pipe network corresponding to the target mixed water pipe network according to the target operating parameters of the target mixed water pipe network.

[0086] Through the above-mentioned device, by obtaining the operation data of multiple mixed water pipe networks in the mixed water heating system within a preset time period, cluster analysis is performed on them, and the cluster operation data of each mixed water pipe network is determined. Then, the cluster operation data is used as the basis for similarity analysis, combined with energy consumption parameters, to find the target mixed water pipe network corresponding to each mixed water pipe network, and according to the operation parameters of the target mixed water pipe network, the operation state of the mixed water pipe network corresponding to the target mixed water pipe network is controlled. In other words, in the process of controlling the operation state of the mixed water pipe network, not only the similarity of the cluster operation data is considered, but also the energy consumption parameters are comprehensively analyzed. By selecting a mixed water pipe network that meets the requirements in terms of similarity and energy consumption as the target mixed water pipe network, the operation state adjustment of the mixed water control system can be quickly and accurately guided, and unnecessary energy waste can be reduced, thereby achieving a significant reduction in the overall energy consumption of the mixed water heating system.

[0087] In an exemplary embodiment, the preset time period includes multiple time points; the determination module 34 is also used to determine, for any one of the multiple mixed water pipe networks, the operating data corresponding to the mixed water pipe network at multiple time points; based on the operating data corresponding to the mixed water pipe network at multiple time points, determine the cluster set of the mixed water pipe network; the cluster set includes K data points, where K is greater than 1 and K is a positive integer; based on the cluster set of the mixed water pipe network, determine the clustered operating data of the mixed water pipe network.

[0088] In an exemplary embodiment, the determination module 34 is further configured to determine at least two data points from the plurality of time points;

[0089] Select any other time point from other time points to determine it as the target time point, and calculate the distance parameters between the target time point and the at least two data points based on the operating data of the target time point and the current operating data of the at least two data points; wherein the other time points are time points other than the data points among the multiple time points; according to the distance parameters, distribute the operating data of the other time points to the matching data points, and calculate the average operating data of all the time points in the data points; update the current operating data of the data points based on the average operating data, select a new other time point from other time points to determine it as the target time point, and return the step of calculating the distance parameters between the target time point and the at least two data points based on the operating data of the target time point and the current operating data of the at least two data points, and stop until the first preset condition is met to determine the cluster set of the mixed water pipe network.

[0090] In an exemplary embodiment, the analysis module 36 is also used to determine, for any one of the multiple mixed water pipe networks, a similarity coefficient between the targeted mixed water pipe network and mixed water pipe networks other than the targeted mixed water pipe network based on the clustered operation data of the targeted mixed water pipe network and the clustered operation data of mixed water pipe networks other than the targeted mixed water pipe network among the multiple mixed water pipe networks.

[0091] In an exemplary embodiment, the analysis module 36 is also used to determine the mixed water network to be calculated from mixed water networks other than the targeted mixed water network; bring the clustering operation data of the targeted mixed water network and the clustering operation data of the mixed water network to be calculated into a pre-constructed similarity expression to determine the similarity coefficient between the targeted mixed water network and the mixed water network to be calculated; re-determine a new mixed water network to be calculated from mixed water networks other than the targeted mixed water network, and return to the step of bringing the clustering operation data of the targeted mixed water network and the clustering operation data of the mixed water network to be calculated into the pre-constructed similarity expression to determine the similarity coefficient between the targeted mixed water network and the mixed water network to be calculated, until the second preset condition is met.

[0092] In an exemplary embodiment, the similarity analysis result includes a similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network; the control module 38 is also used to select a preset number of initial target mixed water networks from mixed water networks other than the targeted mixed water network for any mixed water network based on the similarity coefficient between the targeted mixed water network and mixed water networks other than the targeted mixed water network; extract the initial energy consumption parameters of the preset number of initial target mixed water networks from the energy consumption parameters of the multiple mixed water networks, and perform energy consumption analysis on the preset number of initial target mixed water networks based on the initial energy consumption parameters; and determine the target mixed water network of the targeted mixed water network from the preset number of initial target mixed water networks based on the energy consumption analysis result.

[0093] In an exemplary embodiment, the control module 38 is also used to obtain indoor temperature data in the mixed water heating system; based on the indoor temperature data and the energy consumption analysis result, determine the target mixed water network from the preset number of initial target mixed water networks.

[0094] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0095] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0096] S1, obtaining operation data of multiple mixed water pipe networks in a mixed water heating system within a preset time period;

[0097] S2, clustering the operation data of the plurality of mixed water pipe networks within a preset time period respectively, and determining the clustered operation data corresponding to the plurality of mixed water pipe networks respectively;

[0098] S3, performing similarity analysis based on the clustered operation data corresponding to the plurality of mixed water pipe networks, to obtain a similarity analysis result;

[0099] S4, based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, determine the target mixed water pipe networks corresponding to the multiple mixed water pipe networks respectively, and control the operating status of the mixed water pipe network corresponding to the target mixed water pipe network according to the target operating parameters of the target mixed water pipe network.

[0100] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0101] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0102] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0103] S1, obtaining operation data of multiple mixed water pipe networks in a mixed water heating system within a preset time period;

[0104] S2, clustering the operation data of the plurality of mixed water pipe networks within a preset time period respectively, and determining the clustered operation data corresponding to the plurality of mixed water pipe networks respectively;

[0105] S3, performing similarity analysis based on the clustered operation data corresponding to the plurality of mixed water pipe networks, to obtain a similarity analysis result;

[0106] S4, based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, determine the target mixed water pipe networks corresponding to the multiple mixed water pipe networks respectively, and control the operating status of the mixed water pipe network corresponding to the target mixed water pipe network according to the target operating parameters of the target mixed water pipe network.

[0107] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0108] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0109] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0110] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0111] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0112] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0113] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for a mixed water heating system, characterized in that: include: Obtaining operation data of multiple mixed water pipe networks in a mixed water heating system within a preset time period; Clustering the operation data of the plurality of mixed water pipe networks within a preset time period respectively, and determining the clustered operation data respectively corresponding to the plurality of mixed water pipe networks; Performing similarity analysis based on the clustered operation data corresponding to the plurality of mixed water pipe networks to obtain a similarity analysis result; Based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, the target mixed water pipe networks corresponding to the multiple mixed water pipe networks are determined, and the operating status of the mixed water pipe networks corresponding to the target mixed water pipe networks is controlled according to the target operating parameters of the target mixed water pipe networks.

2. The method according to claim 1, characterized in that The preset time period includes multiple time points; The clustering of the operation data of the plurality of mixed water pipe networks within a preset time period to determine the clustered operation data corresponding to the plurality of mixed water pipe networks includes: For any one of the multiple mixed water pipe networks, determining operation data corresponding to the mixed water pipe network at multiple time points; Determine a cluster set of the mixed water pipe network based on the operation data corresponding to the mixed water pipe network at multiple time points; the cluster set includes K data points, where K is greater than 1 and K is a positive integer; Based on the cluster set of the targeted mixed water pipe network, cluster operation data of the targeted mixed water pipe network is determined.

3. The method according to claim 2, characterized in that The step of determining a cluster set of the mixed water pipe network based on the operation data corresponding to the mixed water pipe network at multiple time points includes: determining at least two data points from the plurality of time points; Select any other time point from the other time points to determine as the target time point, and calculate the distance parameters between the target time point and the at least two data points based on the operation data of the target time point and the current operation data of the at least two data points; wherein the other time point is a time point other than the data point among the multiple time points; According to the distance parameter, the operation data of the other time points are assigned to the matching data points, and the average operation data of all the time points in the data points are calculated; The current operating data of the data point is updated based on the average operating data, a new other time point is selected from other time points to be determined as a target time point, and the operating data based on the target time point and the current operating data of the at least two data points are returned, and the step of calculating the distance parameters between the target time point and the at least two data points respectively is stopped until the first preset condition is met, and the cluster set of the mixed water network is determined.

4. The method according to claim 1, characterized in that: The similarity analysis is performed based on the clustered operation data corresponding to the plurality of mixed water pipe networks to obtain similarity analysis results, including: For any one of the multiple mixed water pipe networks, based on the clustered operation data of the targeted mixed water pipe network and the clustered operation data of mixed water pipe networks other than the targeted mixed water pipe network among the multiple mixed water pipe networks, the similarity coefficient between the targeted mixed water pipe network and the mixed water pipe networks other than the targeted mixed water pipe network is determined.

5. The method according to claim 4, characterized in that The determining, based on the clustered operation data of the targeted mixed water pipe network and the clustered operation data of mixed water pipe networks other than the targeted mixed water pipe network among the multiple mixed water pipe networks, a similarity coefficient between the targeted mixed water pipe network and mixed water pipe networks other than the targeted mixed water pipe network includes: Determine the mixed water pipe network to be calculated from the mixed water pipe networks other than the targeted mixed water pipe network; Substituting the clustered operation data of the mixed water pipe network and the clustered operation data of the mixed water pipe network to be calculated into a pre-constructed similarity expression, and determining a similarity coefficient between the mixed water pipe network and the mixed water pipe network to be calculated; Re-determine a new mixed water network to be calculated from mixed water networks other than the targeted mixed water network, and return the step of bringing the clustering operation data of the targeted mixed water network and the clustering operation data of the mixed water network to be calculated into a pre-constructed similarity expression to determine the similarity coefficient between the targeted mixed water network and the mixed water network to be calculated, and stop when the second preset condition is met.

6. The method according to claim 1, characterized in that The similarity analysis result includes a similarity coefficient between the targeted mixed water pipe network and mixed water pipe networks other than the targeted mixed water pipe network; The determining, based on the similarity analysis result and the energy consumption parameters of the plurality of mixed water pipe networks, target mixed water pipe networks corresponding to the plurality of mixed water pipe networks respectively comprises: For any mixed water pipe network, based on a similarity coefficient between the targeted mixed water pipe network and mixed water pipe networks other than the targeted mixed water pipe network, a preset number of initial target mixed water pipe networks are selected from mixed water pipe networks other than the targeted mixed water pipe network; The initial energy consumption parameters of the preset number of initial target mixed water pipes are extracted from the energy consumption parameters of the multiple mixed water pipes, and energy consumption analysis is performed on the preset number of initial target mixed water pipes based on the initial energy consumption parameters; based on the energy consumption analysis results, the target mixed water network of the mixed water pipes is determined from the preset number of initial target mixed water pipes.

7. The method according to claim 6, characterized in that The step of determining the target mixed water pipe network from the preset number of initial target mixed water pipe networks based on the energy consumption analysis result includes: Acquiring indoor temperature data in the mixed water heating system; Based on the indoor temperature data and the energy consumption analysis result, the target mixed water pipe network is determined from the preset number of initial target mixed water pipe networks.

8. A control device for a mixed water heating system, characterized in that: include: An acquisition module, used to acquire operation data of multiple mixed water pipe networks in a mixed water heating system within a preset time period; A determination module, used to cluster the operation data of the plurality of mixed water pipe networks within a preset time period, and determine the clustered operation data corresponding to the plurality of mixed water pipe networks; An analysis module, configured to perform similarity analysis based on the clustered operation data respectively corresponding to the plurality of mixed water pipe networks to obtain a similarity analysis result; A control module is used to determine the target mixed water pipe networks corresponding to the multiple mixed water pipe networks respectively based on the similarity analysis results and the energy consumption parameters of the multiple mixed water pipe networks, and control the operating status of the mixed water pipe networks corresponding to the target mixed water pipe networks according to the target operating parameters of the target mixed water pipe networks.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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