Heating circulating water monitoring method of heating equipment and heating system

Abstract

The invention relates to a heating circulating water monitoring method of heating equipment and a heating system, and relates to the field of heating equipment. The method is applied to a cloud central server, and comprises the following steps: acquiring a real-time outlet water temperature sequence reported by to-be-monitored heating equipment through the Internet of Things; extracting a water temperature rise rate, a water temperature drop rate, a water temperature rise rate standard score and a water temperature drop rate standard score corresponding to the real-time outlet water temperature sequence; and inputting the water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score and the water temperature drop rate standard score into a pre-trained neural network model to obtain a heating circulating water monitoring result and a target water pump flow rate of the to-be-monitored heating equipment. Whether the heat conduction capacity of the heating circulating water of the user is normal or not can be judged, and it is ensured that the user has a better heating effect.

Description

Technical Field

[0001] The present application relates to the field of heating equipment, and in particular to a heating circulating water monitoring method and a heating system of heating equipment. Background Art

[0002] At present, heating water plays a vital role in heating equipment such as wall-mounted boilers. The thermal conductivity of heating water directly affects the system's operating efficiency, heating effect, energy consumption and equipment life. Insufficient thermal conductivity of heating water will make it difficult to transfer heat from the heating water to the room, resulting in low heating efficiency and difficulty in meeting user heating needs. On the other hand, insufficient thermal conductivity of the heating water system often means insufficient circulating water or abnormal water flow. Under the same heating power and the same hysteresis setting, the outlet water temperature of the heating equipment reaches the temperature faster and stops working, and the start and stop are more frequent, reducing the life of the heating equipment.

[0003] Therefore, there is an urgent need for a heating circulating water monitoring method for heating equipment to monitor whether the heating circulating water is abnormal. Summary of the invention

[0004] Based on this, it is necessary to provide a heating circulating water monitoring method and a heating system for heating equipment to address the above technical problems.

[0005] In a first aspect, a method for monitoring heating circulating water of a heating device is provided, the method being applied to a cloud central server, the method comprising:

[0006] Obtain the real-time water outlet temperature sequence reported by the heating equipment to be monitored through the Internet of Things;

[0007] Extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time water outlet temperature sequence;

[0008] The water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score and the water temperature drop rate standard score are input into a pre-trained neural network model to obtain the heating circulating water monitoring results and the target water pump flow rate of the heating equipment to be monitored.

[0009] As an optional implementation manner, the extracting of the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time outlet water temperature sequence includes:

[0010] Identifying water temperature peaks and water temperature troughs in the real-time outlet water temperature sequence;

[0011] Counting respectively a first slope sequence from the water temperature trough to the next water temperature peak, and a second slope sequence from the water temperature peak to the next water temperature trough;

[0012] Determine the mean slope of the first slope sequence as the water temperature rising rate;

[0013] Determine the mean slope of the second slope sequence as the water temperature drop rate;

[0014] Calculate the standard score of the water temperature rise rate of the heating equipment to be monitored in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score;

[0015] Calculate the standard score of the water temperature drop rate of the heating equipment to be monitored in the sample population of the water temperature drop rates of all heating equipment as the water temperature drop rate standard score.

[0016] As an optional implementation, the training method of the neural network model includes:

[0017] Obtain the historical water outlet temperature sequence and historical water pump flow rate reported by each user's heating equipment during a preset period of time;

[0018] For each heating device, extract the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device;

[0019] Determine the heating circulation water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, the preset first standard score threshold and the preset second standard score threshold;

[0020] The water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score, the water temperature drop rate standard score, the water pump flow rate and the heating circulating water evaluation result are field-joined to obtain a heating circulating water monitoring feature vector;

[0021] The heating circulating water monitoring feature vectors of each user's heating equipment are used as a data set, divided into training data and test data, and the neural network model is trained;

[0022] After the training is completed, the neural network model is saved and deployed to the cloud.

[0023] As an optional implementation manner, for each heating device, extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device includes:

[0024] Identify the water temperature peaks and water temperature troughs of the historical outlet water temperature sequence;

[0025] The third slope sequence from the water temperature trough to the next water temperature peak and the fourth slope sequence from the water temperature peak to the next water temperature trough are respectively counted;

[0026] Determine the slope mean of the third slope sequence as the water temperature rise rate corresponding to the historical water outlet temperature sequence of the heating equipment;

[0027] Determine the slope mean of the fourth slope sequence as the water temperature drop rate corresponding to the historical water outlet temperature sequence of the heating equipment;

[0028] Calculate the standard score of the water temperature rise rate of the heating equipment in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score;

[0029] The standard score of the water temperature drop rate of the heating equipment in the sample population of the water temperature drop rates of all heating equipment is calculated as the water temperature drop rate standard score.

[0030] As an optional implementation manner, determining the heating circulating water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, a preset first standard score threshold and a preset second standard score threshold includes:

[0031] If the water temperature rise rate standard score is greater than the first standard score threshold, it is determined that the heating equipment has insufficient heating circulation water under the current heating power;

[0032] If the water temperature drop rate standard score is greater than the second standard score threshold, it is determined that the heating water heat dissipation rate of the heating equipment is insufficient;

[0033] If the heating circulating water of the heating equipment is insufficient and / or the heat dissipation rate of the heating water is insufficient, the evaluation result of the heating circulating water of the heating equipment is abnormal, otherwise it is normal.

[0034] As an optional implementation, the neural network model is a radial basis function neural network model.

[0035] As an optional implementation, the heating circulating water monitoring results include normal and abnormal.

[0036] As an optional implementation, the method further includes:

[0037] The target water pump flow rate is sent to the heating equipment to be monitored, so that the heating equipment to be monitored adjusts the heating circulation water flow rate according to the target water pump flow rate.

[0038] In a second aspect, a heating system is provided, the system comprising heating equipment and a cloud central server to which the method described in any one of the first aspects is applied.

[0039] The present application provides a method and system for monitoring the heating circulating water of a heating equipment. The technical solution provided by the embodiment of the present application at least brings the following beneficial effects: obtaining the real-time outlet water temperature sequence reported by the heating equipment to be monitored through the Internet of Things; extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time outlet water temperature sequence; inputting the water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score and the water temperature drop rate standard score into a pre-trained neural network model to obtain the monitoring result of the heating circulating water of the heating equipment to be monitored and the target water pump flow rate. The embodiment of the present application can quickly and accurately judge the problem of insufficient heating circulating water or abnormal heat dissipation rate of the heating equipment by real-time monitoring of the water temperature rise rate and drop rate of the heating equipment, and combining the standard score of the water temperature change, so as to realize the accurate evaluation of the operating status of the heating equipment. Through the training and cloud deployment of the neural network model, the system can automatically optimize the operating parameters of the heating equipment, improve the overall efficiency of the heating system, reduce the equipment failure rate, reduce manual intervention, and improve the intelligent level of heating management.

[0040] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of the structure of a heating system provided in an embodiment of the present application;

[0043] Figure 2 A flow chart of a method for monitoring heating circulating water of a heating equipment provided in an embodiment of the present application;

[0044] Figure 3 A flow chart of a method for extracting characteristics of heating circulating water provided in an embodiment of the present application;

[0045] Figure 4 A flowchart of a training method for a neural network model provided in an embodiment of the present application;

[0046] Figure 5 A flowchart of another neural network model training method provided in an embodiment of the present application;

[0047] Figure 6A flowchart of another method for training a neural network model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The heating circulating water monitoring method of the heating equipment provided in the embodiment of the present application can be applied to the cloud central server. Figure 1 As shown, the heating equipment 110 can report the outlet water temperature to the cloud central server 130 through the Internet of Things 120. The cloud central server 130 is deployed with a pre-trained neural network model, which can diagnose whether the heating circulating water is abnormal based on the outlet water temperature reported by the heating equipment 110, and give a target water pump flow rate so that the monitored heating equipment can adjust the heating circulating water flow rate according to the target water pump flow rate.

[0050] The following will describe in detail a method for monitoring heating circulating water of a heating device provided by an embodiment of the present application in combination with a specific implementation method. Figure 2 A flow chart of a method for monitoring heating circulating water of a heating device provided in an embodiment of the present application, the method is applied to a cloud central server, such as Figure 2 As shown, the specific steps are as follows:

[0051] Step 201, obtaining the real-time water outlet temperature sequence reported by the heating equipment to be monitored through the Internet of Things.

[0052] In practice, by collecting data on the real-time outlet water temperature sequence, the cloud central server can obtain the outlet water temperature of the heating equipment at each time point. These data can be transmitted to the cloud central server in real time through communication methods such as the Internet of Things. The characteristic of real-time data flow is that it can reflect the changes in water temperature in a timely manner, thereby providing data support for subsequent analysis.

[0053] Step 202: extract the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time water outlet temperature sequence.

[0054] In implementation, by analyzing the real-time outlet water temperature sequence, the key characteristic values ​​of water temperature changes are extracted, including the water temperature rise rate (water temperature rise speed), the water temperature drop rate (water temperature drop speed), the water temperature rise rate standard score, and the water temperature drop rate standard score. These features are used to characterize the operating status of the heating equipment in order to identify abnormal situations. The water temperature rise rate and drop rate respectively represent the rate of increase and decrease of the outlet water temperature in a specific time period. By calculating the temperature change per unit time, the heating or cooling efficiency of the equipment can be determined. The standard score is based on statistical principles, comparing the water temperature change rate with historical data or other heating equipment of the same period to identify unusual temperature change trends.

[0055] Step 203, input the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score into the pre-trained neural network model to obtain the heating circulating water monitoring results and target water pump flow rate of the heating equipment to be monitored.

[0056] In implementation, data such as the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score, and water temperature drop rate standard score are input into the pre-trained neural network model, which analyzes and predicts these input data, and outputs the heating circulating water monitoring results and the target water pump flow rate of the heating equipment. For example: the pre-trained neural network model accepts real-time input of water temperature rise rate (such as 3°C / minute), water temperature drop rate (such as 1°C / minute), water temperature rise rate standard score (such as 1.5), and water temperature drop rate standard score (such as -1.2). The model analyzes these inputs and determines that the current system is in a normal heating state, and predicts that the monitoring result of the heating circulating water is "normal", and the target water pump flow rate is 2.5m 3 / h.

[0057] As an optional implementation, Figure 3 A flow chart of a method for extracting characteristics of heating circulating water provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the specific steps are as follows:

[0058] Step 301, identifying the water temperature peaks and water temperature troughs in the real-time outlet water temperature sequence.

[0059] In practice, by analyzing the real-time outlet water temperature sequence, water temperature peaks and water temperature troughs can be identified. A peak and trough detection algorithm can be used to identify local extreme points of water temperature based on a preset threshold, with the peak representing the highest point and the trough representing the lowest point.

[0060] Step 302 , respectively counting a first slope sequence from a water temperature trough to a next water temperature peak, and a second slope sequence from a water temperature peak to a next water temperature trough.

[0061] In practice, the slope represents the rate of change of temperature per unit time, and the slope from the trough to the peak of the water temperature represents the rate of temperature rise of the heating equipment during the heating process, which can be expressed as k 1 ={k 11 , k 12 , …, k 1n}, the slope from the peak to the trough indicates the cooling rate, which can be expressed as k 2 ={k 21 , k 22 , …, k 2n}.

[0062] Step 303: determine the mean slope of the first slope sequence as the water temperature rise rate.

[0063] In implementation, the water temperature rise rate of the monitored heating equipment is determined by calculating the mean of the first slope sequence. This value represents the average temperature rise rate of the equipment in different heating stages and is used to evaluate the heating efficiency of the equipment, which can be expressed as ak 1 .

[0064] Step 304: determine the mean slope of the second slope sequence as the water temperature drop rate.

[0065] In implementation, the water temperature drop rate of the monitored heating equipment is determined by calculating the mean of the second slope sequence. This value represents the average cooling speed of the equipment and is used to judge the heat dissipation performance of the equipment, which can be expressed as ak 2 .

[0066] Step 305, calculating the standard score of the water temperature rise rate of the heating equipment to be monitored in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score.

[0067] In practice, the standard score is a method of comparing a specific data point with the overall data, reflecting the relative position of the data point in the overall data. The water temperature rise rate of the heating equipment to be monitored is compared with the water temperature rise rate samples of all heating equipment to calculate the standard score, which can be expressed as z 1 . The standard score can be used to evaluate whether the heating effect of the current equipment is abnormal. For example, the water temperature rise rate of the heating equipment to be monitored is 2°C / minute, the average water temperature rise rate of the sample population is 1.8°C / minute, and the standard deviation is 0.3°C / minute. Through calculation, the standard score of the water temperature rise rate of the heating equipment to be monitored is 0.67, indicating that the heating effect of the equipment is slightly higher than the average level.

[0068] Step 306, calculating the standard score of the water temperature drop rate of the heating equipment to be monitored in the sample population of the water temperature drop rates of all heating equipment as the water temperature drop rate standard score.

[0069] In implementation, the water temperature drop rate of the heating equipment to be monitored is compared with the water temperature drop rate samples of all heating equipment, and the standard score is calculated, which can be expressed as z 2 This score is used to determine whether the cooling performance of the equipment is normal. For example, the water temperature drop rate of the heating equipment to be monitored is -1.77℃ / minute, the average water temperature drop rate of the sample population is -1.5℃ / minute, and the standard deviation is 0.25℃ / minute. By calculation, the standard score of the water temperature drop rate is -1.08, indicating that the heat dissipation speed of the heating equipment to be monitored is faster than the overall average level.

[0070] As an optional implementation, Figure 4 A flowchart of a training method for a neural network model provided in an embodiment of the present application, such as Figure 4 As shown, the specific steps are as follows:

[0071] Step 401, obtaining the historical water outlet temperature sequence and historical water pump flow rate of each user's heating equipment reported in the preset time period.

[0072] In practice, the cloud central server can obtain the historical water outlet temperature data and corresponding water pump flow rate information of each user's heating equipment within a preset period (such as the past 7 days) from the database. These data are regularly reported by each heating equipment during the previous operation process, recorded and stored in the database of the cloud central server. The collection and storage of these historical data can provide a basis for subsequent analysis and modeling.

[0073] Step 402: for each heating device, extract the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device.

[0074] In implementation, the system analyzes the historical water outlet temperature sequence of each heating device, calculates the water temperature rise rate and water temperature drop rate, and then compares them with the overall historical data sample to obtain the water temperature rise rate standard score and water temperature drop rate standard score. The water temperature rise rate and drop rate represent the efficiency of the heating equipment when heating and cooling, respectively. The long-term performance of these indicators can be obtained by analyzing historical data. The standard score measures the relative performance of the equipment by comparing it with the overall data of other equipment, helping to identify abnormal or inefficient equipment.

[0075] Step 403, determining the heating circulation water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, the preset first standard score threshold and the preset second standard score threshold.

[0076] During implementation, the cloud central server compares the standard score of the water temperature rise rate and the standard score of the water temperature drop rate of the equipment with the set thresholds to determine the evaluation results of the heating circulating water of the equipment. The evaluation results are used to determine whether the heating and cooling performance of the equipment meets expectations. For example, if the standard score exceeds the set range, the system will mark it as an abnormal device or an inefficient device, triggering further inspection or maintenance. For example, the threshold of the standard score of the water temperature rise rate is 1.5, and the threshold of the standard score of the water temperature drop rate is -1.5. The standard score of the water temperature rise rate of a certain device is 0.8, and the standard score of the water temperature drop rate is -1.1. The evaluation result is "normal", indicating that the equipment is in good operating condition and does not require further adjustment.

[0077] Step 404, concatenate the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score, water temperature drop rate standard score, water pump flow rate and heating circulating water evaluation results to obtain a heating circulating water monitoring feature vector.

[0078] In implementation, the above data can be field-joined in a specific order to form a complete feature vector. This feature vector includes the water temperature change characteristics of the equipment, pump speed data, and evaluation results, which are used for subsequent model training. By combining multiple indicators related to equipment performance into a feature vector, a comprehensive status description can be generated for each heating equipment. The feature vector not only includes the basic operating parameters of the heating equipment, but also combines historical performance and evaluation results, which helps to improve the prediction accuracy of the model. For example: The feature vector of a heating equipment includes a water temperature rise rate of 2°C / minute, a water temperature drop rate of -1.8°C / minute, a rise rate standard score of 0.8, a drop rate standard score of -1.1, and a water pump flow rate of 2.5m 3 / h, the evaluation result is "Normal". The system concatenates these fields into a feature vector: [2,-1.8,0.8,-1.1,2.5, "Normal"].

[0079] Step 405: The heating circulating water monitoring feature vectors of each user's heating equipment are used as a data set, divided into training data and test data, and a neural network model is trained.

[0080] In the implementation, the cloud central server aggregates the feature vectors of all users' heating equipment into a data set, which is then divided into a training set and a test set according to a certain ratio. The neural network model uses the training set to learn to capture the rules between the equipment's operating status and the evaluation results. Among them, the training set is used to allow the model to learn the relationship between the different characteristics of the heating equipment and the performance evaluation, while the test set is used to verify the accuracy and generalization ability of the model. For example: feature vector data of 1,000 heating equipment are collected, 80% of which are used for training and 20% for testing. The neural network model continuously adjusts its weight parameters through the training set, and finally can show good prediction accuracy on the test data.

[0081] Step 406: After the training is completed, the neural network model is saved and deployed to the cloud.

[0082] In practice, after the model training is completed, the cloud central server saves the trained neural network model and deploys it to the cloud environment. The cloud model can be called at any time to perform real-time analysis and prediction of newly reported equipment data. Cloud deployment enables the model to respond in real time and update remotely. By deploying the model to the cloud, the operation and maintenance personnel of the heating system can monitor the equipment operation status at any time and take corresponding optimization measures based on the model's prediction results, such as automatically adjusting the operating parameters of the heating equipment or issuing maintenance reminders.

[0083] As an optional implementation, Figure 5 A flowchart of another neural network model training method provided in an embodiment of the present application, such as Figure 5 As shown, in step 402, for each heating device, the specific steps of extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device are as follows:

[0084] Step 501, identifying the water temperature peaks and water temperature troughs of the historical water outlet temperature sequence.

[0085] In practice, water temperature peaks and water temperature troughs can be identified by analyzing the historical water outlet temperature series.

[0086] Step 502 , respectively counting the third slope sequence from the water temperature trough to the next water temperature peak, and the fourth slope sequence from the water temperature peak to the next water temperature trough.

[0087] In practice, the slope represents the rate of change of temperature per unit time, and the slope from the trough to the peak of the water temperature represents the rate of temperature rise of the heating equipment during the heating process, which can be expressed as k 3 ={k 31 , k 32 , …, k 3n}, the slope from the peak to the trough indicates the cooling rate, which can be expressed as k 4 ={k 41 , k 42 , …, k 4n}.

[0088] Step 503: Determine the slope mean of the third slope sequence as the water temperature rise rate corresponding to the historical water outlet temperature sequence of the heating equipment.

[0089] In implementation, the water temperature rise rate of the heating device is determined by calculating the mean of the first slope sequence. This value represents the average temperature rise rate of the device in different heating stages and is used to evaluate the heating efficiency of the device, which can be expressed as ak 3 .

[0090] Step 504: determine the slope mean of the fourth slope sequence as the water temperature drop rate corresponding to the historical water outlet temperature sequence of the heating equipment.

[0091] In implementation, the water temperature drop rate of the heating device is determined by calculating the mean of the second slope sequence. This value represents the average cooling speed of the device and is used to judge the heat dissipation performance of the device, which can be expressed as ak 4 .

[0092] Step 505, calculating the standard score of the water temperature rise rate of the heating equipment in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score.

[0093] In implementation, the water temperature rise rate of the heating equipment is compared with the water temperature rise rate samples of all heating equipment, and the standard score is calculated, which can be expressed as z 3 .

[0094] Step 506, calculating the standard score of the water temperature drop rate of the heating equipment in the sample population of the water temperature drop rates of all heating equipment as the water temperature drop rate standard score.

[0095] In implementation, the water temperature drop rate of the heating equipment is compared with the water temperature drop rate samples of all heating equipment, and the standard score is calculated, which can be expressed as z 4 .

[0096] As an optional implementation, Figure 6 A flowchart of another method for training a neural network model provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, in step 403, the specific steps of determining the heating circulating water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, the preset first standard score threshold and the preset second standard score threshold are as follows:

[0097] Step 601: If the water temperature rise rate standard score is greater than the first standard score threshold, it is determined that the heating equipment is insufficient for heating circulation water under the current heating power.

[0098] In practice, the water temperature rise rate reflects the rate of temperature increase during the heating process of the heating equipment. If the rise rate is too fast, it means that the flow of the heating circulating water is insufficient and cannot effectively absorb the heat generated by the heating of the equipment, resulting in excessively high local temperatures and affecting the overall heating effect. For example: the standard score of the water temperature rise rate of the heating equipment is 2.0, and the preset first standard score threshold is 1.5. Since the standard score of the rise rate exceeds the threshold, it is determined that the heating circulating water of the equipment is insufficient. It is recommended to check whether there is a blockage or insufficient flow problem in the circulating water system.

[0099] Step 602: If the water temperature drop rate standard score is greater than the second standard score threshold, it is determined that the heat dissipation rate of the heating water of the heating equipment is insufficient.

[0100] In practice, the water temperature drop rate reflects the speed at which the temperature drops during the cooling process of the equipment. If the heat dissipation rate is too slow, it means that the heat stays in the system for too long and cannot be effectively released to the outside space, which will lead to reduced efficiency of the heating system and even overheating. For example: the water temperature drop rate standard score of the heating equipment is 1.7, and the preset second standard score threshold is 1.5. Since the drop rate standard score of the equipment exceeds the threshold, it is determined that its heat dissipation rate is insufficient.

[0101] Step 603: If the heating circulating water of the heating equipment is insufficient and / or the heat dissipation rate of the heating water is insufficient, the evaluation result of the heating circulating water of the heating equipment is abnormal, otherwise it is normal.

[0102] In practice, the flow and heat dissipation efficiency of heating circulating water are key parameters for the operation of heating equipment. By comparing the standard scores of the water temperature rise rate and the drop rate, it is possible to quickly determine whether the performance of the heating equipment during the heating and heat dissipation process is normal. If any aspect performs abnormally, the system will issue a reminder to prevent the equipment from being in an inefficient or abnormal state for a long time. For example: The standard score for the water temperature rise rate of a certain heating equipment is 2.0, which exceeds the threshold of 1.5 for the rise rate, and the standard score for the water temperature drop rate is 1.3, which does not exceed the threshold of 1.5 for the drop rate. Therefore, the system determines that the evaluation result of the heating circulating water of the equipment is "abnormal" to remind the user that the heating equipment in the user's home is insufficient in heating circulating water.

[0103] As an optional implementation, the neural network model is a radial basis function neural network model.

[0104] As an optional implementation, the heating circulating water monitoring results include normal and abnormal.

[0105] As an optional implementation, the cloud central server may send the target water pump flow rate to the heating equipment to be monitored, so that the heating equipment to be monitored adjusts the heating circulating water flow rate according to the target water pump flow rate.

[0106] The embodiment of the present application provides a method for monitoring the heating circulating water of a heating equipment, which is applied to a cloud central server, and the method includes: obtaining the real-time water outlet temperature sequence reported by the heating equipment to be monitored through the Internet of Things; extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time water outlet temperature sequence; inputting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score into a pre-trained neural network model to obtain the monitoring results of the heating circulating water of the heating equipment to be monitored and the target water pump flow rate. The embodiment of the present application can quickly and accurately judge the problem of insufficient heating circulating water or abnormal heat dissipation rate of the heating equipment by real-time monitoring of the water temperature rise rate and drop rate of the heating equipment, and combining the standard score of the water temperature change, so as to realize the accurate evaluation of the operating status of the heating equipment. Through the training and cloud deployment of the neural network model, the system can automatically optimize the operating parameters of the heating equipment, improve the overall efficiency of the heating system, reduce the equipment failure rate, reduce manual intervention, and improve the intelligent level of heating management.

[0107] It should be understood that although Figures 2 to 6 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 2 to 6 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0108] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0109] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0111] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0112] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring heating circulating water of a heating equipment, characterized in that: The method is applied to a cloud central server, and the method comprises: Obtain the real-time water outlet temperature sequence reported by the heating equipment to be monitored through the Internet of Things; Extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time water outlet temperature sequence; The water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score and the water temperature drop rate standard score are input into a pre-trained neural network model to obtain the heating circulating water monitoring results and the target water pump flow rate of the heating equipment to be monitored.

2. The method according to claim 1, characterized in that The step of extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the real-time water outlet temperature sequence includes: Identifying water temperature peaks and water temperature troughs in the real-time outlet water temperature sequence; Counting respectively a first slope sequence from the water temperature trough to the next water temperature peak, and a second slope sequence from the water temperature peak to the next water temperature trough; Determine the mean slope of the first slope sequence as the water temperature rising rate; Determine the mean slope of the second slope sequence as the water temperature drop rate; Calculate the standard score of the water temperature rise rate of the heating equipment to be monitored in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score; Calculate the standard score of the water temperature drop rate of the heating equipment to be monitored in the sample population of the water temperature drop rates of all heating equipment as the water temperature drop rate standard score.

3. The method according to claim 1, characterized in that: The training method of the neural network model comprises: Obtain the historical water outlet temperature sequence and historical water pump flow rate reported by each user's heating equipment during a preset period of time; For each heating device, extract the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device; Determine the heating circulation water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, the preset first standard score threshold and the preset second standard score threshold; The water temperature rise rate, the water temperature drop rate, the water temperature rise rate standard score, the water temperature drop rate standard score, the water pump flow rate and the heating circulating water evaluation result are field-joined to obtain a heating circulating water monitoring feature vector; The heating circulating water monitoring feature vectors of each user's heating equipment are used as a data set, divided into training data and test data, and the neural network model is trained; After the training is completed, the neural network model is saved and deployed to the cloud.

4. The method according to claim 3, characterized in that: For each heating device, extracting the water temperature rise rate, water temperature drop rate, water temperature rise rate standard score and water temperature drop rate standard score corresponding to the historical water outlet temperature sequence of the heating device, including: Identify the water temperature peaks and water temperature troughs of the historical outlet water temperature sequence; The third slope sequence from the water temperature trough to the next water temperature peak and the fourth slope sequence from the water temperature peak to the next water temperature trough are respectively counted; Determine the slope mean of the third slope sequence as the water temperature rise rate corresponding to the historical water outlet temperature sequence of the heating equipment; Determine the slope mean of the fourth slope sequence as the water temperature drop rate corresponding to the historical water outlet temperature sequence of the heating equipment; Calculate the standard score of the water temperature rise rate of the heating equipment in the sample population of the water temperature rise rates of all heating equipment as the water temperature rise rate standard score; The standard score of the water temperature drop rate of the heating equipment in the sample population of the water temperature drop rates of all heating equipment is calculated as the water temperature drop rate standard score.

5. The method according to claim 3, characterized in that: The step of determining the heating circulation water evaluation result of the heating equipment according to the water temperature rise rate standard score, the water temperature drop rate standard score, a preset first standard score threshold, and a preset second standard score threshold comprises: If the water temperature rise rate standard score is greater than the first standard score threshold, it is determined that the heating equipment has insufficient heating circulation water under the current heating power; If the water temperature drop rate standard score is greater than the second standard score threshold, it is determined that the heating water heat dissipation rate of the heating equipment is insufficient; If the heating circulating water of the heating equipment is insufficient and / or the heat dissipation rate of the heating water is insufficient, the evaluation result of the heating circulating water of the heating equipment is abnormal, otherwise it is normal.

6. The method according to claim 3, characterized in that The neural network model is a radial basis function neural network model.

7. The method according to claim 1, characterized in that The heating circulating water monitoring results include normal and abnormal.

8. The method according to claim 1, characterized in that: The method further comprises: The target water pump flow rate is sent to the heating equipment to be monitored, so that the heating equipment to be monitored adjusts the heating circulation water flow rate according to the target water pump flow rate.

9. A heating system, characterized in that: The system includes a heating device and a cloud central server to which the method according to any one of claims 1 to 8 is applied.

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