A static pressure reset frequency conversion control method for a centralized flue system

Abstract

The present invention discloses a static pressure resetting frequency conversion control method for a centralized flue system, and relates to the technical field of centralized flue control and maintenance, including collecting centralized flue data, constructing a centralized flue mathematical model, setting a static pressure monitoring point, detecting a 24-hour fan opening rate, and calculating a static pressure control value of the static pressure monitoring point; substituting the real-time opening rate during the operation of the fan into the centralized flue model to calculate a real-time static pressure value and a static pressure control value for comparison and adjustment of the fan operation; predicting centralized flue faults based on the collected centralized flue data, and performing predictive maintenance according to the fault prediction results. The present invention performs statistics on the simultaneous use coefficient of the residential centralized flue system, calculates the static pressure control value at the corresponding moment, resets the static pressure value according to the time, and performs fault prediction on the centralized flue system in real time, performs predictive maintenance on the centralized flue, and achieves the control effect of variable frequency energy saving, which greatly reduces the probability of centralized flue faults.

Description

Technical Field

[0001] The invention relates to the technical field of centralized flue control and maintenance, and in particular to a static pressure resetting frequency conversion control method for a centralized flue system. Background Art

[0002] The kitchen exhaust system of high-rise residential buildings has developed from the decentralized power of the range hood to the composite power of the range hood and the roof fan. During the use of the system, the range hoods on each floor are not fully turned on, so there is a simultaneous use coefficient. With the promotion of low-energy housing and green and low-carbon development, the variable frequency operation of roof fans has emerged. The classic fan variable frequency technology includes static pressure control method and total air volume method. The static pressure control method is divided into fixed static pressure method and variable static pressure method. The fixed static pressure method is to select a suitable position on the air duct as the static pressure monitoring point. The system control terminal adjusts the operating frequency of the fan according to the requirements of the system setting to ensure that the static pressure setting value of the monitoring point remains unchanged, and each terminal air supply device outputs at this setting value. Air output, but for a long time, the location of the static pressure monitoring point and the determination of the static pressure setting value have been a thorny problem. The variable static pressure method is similar to the fixed static pressure method, but the static pressure setting value can be adjusted. The setting value of the fixed static pressure method is constantly changed to be adjusted with the overall load of the system, and the static pressure setting value is minimized as much as possible to reduce system energy consumption. However, the difficulty and core problem of the variable static pressure method lies in how to continuously reset the static pressure setting value of the monitoring point. Generally speaking, the existing technology cannot meet the variable frequency control requirements of the centralized flue, and cannot maintain predictive monitoring of the centralized flue operation to perform predictive maintenance of the centralized flue. Therefore, a static pressure reset variable frequency control method for a centralized flue system of the present invention is proposed. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing static pressure resetting frequency conversion control method of the centralized flue system, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology cannot meet the frequency conversion control requirements of the centralized flue, and cannot maintain predictive monitoring of the operation of the centralized flue to perform predictive maintenance of the centralized flue.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a static pressure reset frequency conversion control method for a centralized flue system, which includes collecting centralized flue data, constructing a centralized flue mathematical model, setting a static pressure monitoring point, detecting the 24-hour fan opening rate to calculate the static pressure control value of the static pressure monitoring point; substituting the real-time opening rate of the fan into the centralized flue model to calculate the real-time static pressure value and compare it with the static pressure control value to adjust the fan operation; predicting centralized flue faults based on the collected centralized flue data, and performing predictive maintenance according to the fault prediction results; establishing a three-dimensional model of the centralized flue, displaying the collected data and fault prediction results, and storing all the data.

[0006] As a preferred solution of the static pressure reset frequency conversion control method of the centralized flue system of the present invention, wherein: the collection of centralized flue data and the construction of a centralized flue mathematical model refer to collecting the dimensions of the centralized flue main and branch flue, the resistance coefficient of the check valve, the performance parameters of the range hood, the resistance coefficient of the oil fume purification equipment, the performance parameters of the fan and the frequency conversion control target air volume through sensors and measuring equipment to construct a centralized flue mathematical model, and the construction of the centralized flue mathematical model includes:

[0007] The centralized flue is divided into calculation nodes, and the fan is assumed to be opened evenly. The node opening and closing coefficient is calculated according to the fan opening rate;

[0008] Calculate the ratio of each node air volume to the main pipe air volume;

[0009] Calculate the dynamic pressure coefficient in the main pipe and the dynamic pressure ratio between the branch pipe and the main pipe;

[0010] Determine the direct and converging tee resistance coefficients of each node, calculate the pressure distribution of each node in the direct and converging directions based on the node opening and closing coefficients, and obtain the total exhaust resistance;

[0011] Calculate the static pressure control value of the range hood at the end of the branch pipe through the air volume;

[0012] At this point, the mathematical model of the centralized flue has been completed.

[0013] As a preferred solution of the static pressure reset variable frequency control method of the centralized flue system of the present invention, wherein: the static pressure monitoring point is set, and the static pressure control value of the static pressure monitoring point is calculated by detecting the 24-hour fan opening rate, including:

[0014] Set the end of the centralized flue roof and the end range hood as static pressure monitoring points;

[0015] Temperature self-meters are installed around the stove to collect temperature data. The user turns on the range hood when using the stove. When the temperature is higher than the indoor and outdoor temperatures, it is considered that the user is using the stove and the range hood is turned on. When the temperature drops sharply, it is considered that the user turns off the stove and the range hood is turned off.

[0016] The number of users connected to the centralized flue that turn on the range hoods at the same time within 24 hours is summarized at each time to obtain the fan opening rate at each time within 24 hours;

[0017] The fan opening rate at each moment within 24 hours is input into the centralized flue model to obtain the static pressure control value at each moment within 24 hours at the static pressure monitoring point.

[0018] As a preferred scheme of the static pressure resetting frequency conversion control method of the centralized flue system described in the present invention, wherein: the real-time static pressure value calculated by substituting the real-time opening rate during the operation of the fan into the centralized flue model and comparing it with the static pressure control value to adjust the fan operation refers to real-time monitoring of the fan opening coefficient during the actual operation of the centralized flue, monitoring the real-time static pressure value through a static pressure sensor, comparing the real-time static pressure value with the static pressure control value at that moment, setting the static pressure error, when the real-time static pressure value is less than the difference between the static pressure control value and the static pressure error, reducing the fan operation frequency, when the real-time static pressure value is greater than the sum of the static pressure control value and the static pressure error, increasing the fan operation frequency, and generating a fan adjustment record after the adjustment is completed.

[0019] As a preferred solution of the static pressure reset frequency conversion control method of the centralized flue system of the present invention, wherein: the prediction of centralized flue failure based on the collected centralized flue data includes:

[0020] Collect the historical data of the centralized flue, clean and filter them, randomly divide them into training data set and test data set, and label the data of the training data set and test data set by categories, including normal and abnormal;

[0021] Randomly select data from the training data set to create training subsets, and build a decision tree for each training subset;

[0022] Calculate the decision tree segmentation information gain for decision tree segmentation: in is the information gain of decision tree D, is the entropy of decision tree D, and is the sub-decision tree after segmentation, is the total number of data in the decision tree D, and The total number of data in the posterior decision tree, c is the number of labeled categories, is the proportion of labeled data of the i-th category in the decision tree D, and the decision tree segmentation is completed by maximizing the information gain;

[0023] Iterate the decision tree segmentation until the decision tree information gain is lower than the set threshold, then stop the decision tree segmentation, and combine the decision trees to form a fault prediction model: in For the model prediction results, Representative majority voting, represents the prediction result of the i-th decision tree, and t is the total number of decision trees in the fault prediction model;

[0024] The collected centralized flue data are input into the fault prediction model to predict centralized flue failure.

[0025] As a preferred solution of the static pressure reset frequency conversion control method of the centralized flue system of the present invention, wherein: the predictive maintenance according to the fault prediction result includes:

[0026] If the fault prediction result is that the centralized flue system is predicted to operate normally, the centralized flue system data will be collected and tested regularly, and a centralized flue system test record will be generated after each test and sent to the maintenance personnel for review. The maintenance personnel will attach an electronic signature and timestamp;

[0027] If the fault prediction result is that the centralized flue system is operating abnormally, the maintenance personnel will be notified immediately and the collected data will be sent to the maintenance personnel to analyze the predicted fault location for predictive maintenance, and the periodic collection and detection of centralized flue data will be adjusted to real-time collection and detection. When the maintenance personnel perform centralized flue maintenance, the centralized flue will be kept in a closed state, and the centralized flue fault will be re-predicted after the maintenance is completed. If the prediction result is normal, a centralized flue maintenance record will be generated. If the prediction result is still normal, the staff will be notified to continue the centralized flue analysis and maintenance until the prediction result is normal.

[0028] As a preferred scheme of the static pressure resetting frequency conversion control method of the centralized flue system described in the present invention, wherein: the establishment of a three-dimensional model of the centralized flue and the display of the collected data and fault prediction results refers to establishing a three-dimensional model of the centralized flue based on the collected centralized flue geometric data, mapping the centralized flue data and fault prediction results to the three-dimensional model through data mapping and visualization for display, and deploying the three-dimensional model to the terminal for user viewing.

[0029] As a preferred solution of the static pressure resetting frequency conversion control method of the centralized flue system described in the present invention, wherein: the storage of all data refers to the database storing the collected centralized flue data, centralized flue mathematical model, fan frequency adjustment record, centralized flue fault prediction results, predictive maintenance measures and centralized flue three-dimensional model, and setting security access rights and access passwords. The database backs up all stored data in the cloud and regularly performs integrity and consistency checks on the stored data and cloud data.

[0030] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the static pressure reset frequency conversion control method of the centralized flue system when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the static pressure reset variable frequency control method for the centralized flue system.

[0032] The beneficial effects of the present invention are as follows: the present invention statistics the simultaneous use coefficient of the residential centralized flue system, calculates the static pressure control value at the corresponding moment and resets the static pressure value according to the time, and predicts the failure of the centralized flue system in real time, performs predictive maintenance of the centralized flue, achieves the control effect of variable frequency energy saving, and greatly reduces the probability of failure of the centralized flue. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 The flowchart is a schematic diagram of the static pressure resetting variable frequency control method for the centralized flue system.

[0035] Figure 2 This is a statistical diagram of the fan activation rate of a residential building.

[0036] Figure 3 This is a schematic diagram of the relationship between the static pressure control value and the fan opening rate. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Example

[0040] Reference Figure 1~Figure 3 , which is the first embodiment of the present invention, and which provides a static pressure reset frequency conversion control method for a centralized flue system. The static pressure reset frequency conversion control method for a centralized flue system includes:

[0041] S1. Collect centralized flue data, build a centralized flue mathematical model, set static pressure monitoring points, detect the 24-hour fan opening rate, and calculate the static pressure control value of the static pressure monitoring point;

[0042] Specifically, collecting centralized flue data and constructing a centralized flue mathematical model means collecting the centralized flue main pipe, branch pipe flue dimensions, check valve resistance coefficient, range hood performance parameters, fume purification equipment resistance coefficient, fan performance parameters, and variable frequency control target air volume through sensors and measuring equipment to construct a centralized flue mathematical model. The construction of the centralized flue mathematical model includes:

[0043] The centralized flue is divided into n computing nodes. The fan is assumed to be evenly opened. The node opening and closing status is calculated according to the fan opening rate. When the end range hood is in the open state, the value is 1; when the end range hood is in the closed state, the value is 0: in The on / off status of the range hood at the end of the i-th floor;

[0044] Calculate the ratio of each node air volume to the main pipe air volume: in is the air volume ratio of the i-th layer, is the air volume of the i-th layer, is the air volume of the concentrated flue at the i-th layer, b is the branch pipe, m is the main pipe, is the sum of the concentrated flue air volume from the jth floor to the ith floor;

[0045] Calculate the dynamic pressure coefficient in the main pipe and the dynamic pressure ratio between the branch pipe and the main pipe: in is the main pipe dynamic pressure coefficient, is the dynamic pressure ratio between the branch pipe and the main pipe, To concentrate the density of oil smoke in the flue, is the cross-sectional area of ​​the main pipe, is the cross-sectional area of ​​the branch pipe;

[0046] Determine the DC tee resistance coefficient of each node And the resistance coefficient of the confluence tee :

[0047] Where L is the length of the calculation node, K is the roughness of the concentrated flue wall, is the hydraulic diameter of the concentrated flue, is the Reynolds number of the i-th node segment, is the pipe elbow resistance coefficient;

[0048] Based on the node opening and closing coefficient, the pressure distribution of each node in the direct flow and converging directions is calculated to obtain the total exhaust resistance:

[0049] in, represents the total flue pressure at the outlet of the i-th branch pipe, is the resistance coefficient along the i-th layer node;

[0050] For the i Layer node branch resistance coefficient, For the i The external residual pressure of the layer node range hood;

[0051] Define the n+1th virtual branch, whose opening coefficient is 1, and the hood resistance and outlet dynamic pressure loss are equivalent to the straight-through resistance coefficient. It can be taken as 0, and the straight-through direction equation in the node is: in is the roof hood drag coefficient;

[0052] According to the equations of each node, the coefficient matrix is ​​defined as:

[0053] Where M is a constant coefficient matrix, and is a diagonal matrix with the opening and closing coefficients of each layer as eigenvalues, and the diagonal matrix is ​​used to describe the operating status of each terminal range hood;

[0054] Calculate four key drag distributions, along the drag distribution vector , DC resistance distribution vector , the confluence resistance distribution vector , and the branch resistance distribution vector :

[0055] in ; Therefore, the equations in the direct direction are transformed into: is the product of the constant coefficient matrix and the pressure distribution vector; the equations for the confluence direction are transformed into: The vector representing the total pressure of the flue at the outlet of each branch pipe, Refers to the vector composed of the total exhaust resistance of users on each floor; the total exhaust resistance of users on each floor in the public flue is calculated from this: Calculate the static pressure control value of the range hood at the end of the branch pipe by the air volume: in is the pressure head at the roof fan operating point, , , as well as is the range hood performance curve coefficient; at this point, the centralized flue mathematical model is completed.

[0056] By collecting key data such as flue size, resistance coefficient and fan performance, the constructed mathematical model can accurately reflect the physical behavior and performance of the actual system. This enables the model to effectively predict the performance of the system under different operating conditions and provide a basis for optimization. By accurately controlling the opening state of each node, the air volume and pressure can be managed more effectively, reducing energy consumption and operating costs. Calculating the ratio of the air volume of each node to the main air volume and the dynamic pressure coefficient helps to accurately control the pressure distribution in the system and optimize the power efficiency of the system. Through accurate pressure distribution calculation, we can better understand the energy loss and potential performance bottlenecks in the system, and then make targeted improvements. Determining the static pressure control value of the range hood at the end of each branch pipe can ensure that each smoke exhaust point can work effectively and avoid affecting the smoke exhaust efficiency due to insufficient or excessive pressure.

[0057] Furthermore, a static pressure monitoring point is set to monitor the 24-hour fan opening rate. The static pressure control value calculated at the static pressure monitoring point includes:

[0058] Set the end of the centralized flue roof and the end range hood as static pressure monitoring points;

[0059] Temperature self-meters are installed around the stove to collect temperature data. The user turns on the range hood when using the stove. When the temperature is higher than the indoor and outdoor temperatures, it is considered that the user is using the stove and the range hood is turned on. When the temperature drops sharply, it is considered that the user turns off the stove and the range hood is turned off.

[0060] The continuous and smooth temperature curve is converted into a 0-1 coded digital step signal, where "0" indicates no cooking behavior and the flue is not in use; "1" indicates cooking is in progress and the flue is in use; by summing up the data signals of the residents in the flue at the same time, the number of households using the flue at the same time can be known, so as to calculate the simultaneous use coefficient of the flue, and the opening and closing of the flue is identified by the slope of the curve. When the slope changes from a negative number or 0 to a positive number, the turning point marks the beginning of the cooking behavior, and the encoded signal has a step from 0 to 1; when the slope changes from a positive number or 0 to a negative number, the turning point marks the end of the cooking behavior, and the encoded signal has a step from 1 to 0. The number of users connected to the centralized flue who turn on the range hoods at the same time within 24 hours is summarized at each time to obtain the fan opening rate at each time within 24 hours;

[0061] The fan opening rate at each moment within 24 hours is input into the centralized flue model to obtain the static pressure control value at each moment within 24 hours at the static pressure monitoring point.

[0062] Ensure that the static pressure in the centralized flue system can be accurately monitored and controlled, thereby optimizing the system's operating efficiency and reducing energy consumption. These monitoring points help to adjust the fan power and control the air flow of the system in real time to cope with different usage requirements and changes in the external environment. By automatically recording the temperature changes around the stove, the inventor can determine the usage status of the range hood without direct intervention, thereby improving the system's automation level. This monitoring method simplifies user interaction and improves the accuracy and convenience of system response, which is particularly suitable for busy kitchen environments. The use of digital signals makes data analysis simpler and more intuitive, making it easier to quickly identify changes in cooking activities through algorithm processing. This technology can be used for further data analysis, such as frequency of use statistics and predictive models, to provide data support for the maintenance and energy efficiency management of kitchen equipment. By analyzing data throughout the day, a more accurate and dynamic fan control strategy can be obtained to match actual usage requirements, ensuring that the flue system can maintain appropriate exhaust efficiency at any time, avoiding energy waste, while maintaining indoor air quality.

[0063] S2. Substitute the real-time opening rate of the fan into the centralized flue model to calculate the real-time static pressure value and compare it with the static pressure control value during the operation of the fan to adjust the operation of the fan;

[0064] Specifically, the real-time static pressure value is calculated according to the real-time opening rate of the fan during operation, and is compared with the static pressure control value to adjust the fan operation. This means that the fan opening coefficient is monitored in real time during the actual operation of the centralized flue, and the real-time static pressure value is monitored by a static pressure sensor. The real-time static pressure value is compared with the static pressure control value at that moment, and the static pressure error is set. When the real-time static pressure value is less than the difference between the static pressure control value and the static pressure error, the fan operation frequency is reduced. When the real-time static pressure value is greater than the sum of the static pressure control value and the static pressure error, the fan operation frequency is increased. After the adjustment is completed, a fan adjustment record is generated.

[0065] It allows system administrators to understand the current operating status of the system and make immediate adjustments to improve the system's response speed and flexibility. Real-time data monitoring is the key to ensuring the continuous and stable operation of the centralized flue system. By comparing real-time data and target control values, the fan operation can be effectively adjusted to match actual needs, optimize energy use, and reduce energy waste. It ensures that the system can maintain optimal performance under different operating conditions, especially during peak kitchen usage periods. It can effectively manage the air quality and comfort of the kitchen, record detailed information for each adjustment, and help with system performance analysis and future system optimization. The fan adjustment record not only provides historical data on system operation, but also provides a basis for system fault diagnosis and performance evaluation, which is helpful for future maintenance and upgrades.

[0066] S3. Predict centralized flue failures based on the collected centralized flue data, and perform predictive maintenance based on the failure prediction results;

[0067] Specifically, the prediction of centralized flue failure based on the collected centralized flue data includes:

[0068] Collect the historical data of the centralized flue, clean and filter them, randomly divide them into training data set and test data set, and label the data of the training data set and test data set by categories, including normal and abnormal;

[0069] Randomly select data from the training data set to create training subsets, and build a decision tree for each training subset;

[0070] Calculate the decision tree segmentation information gain for decision tree segmentation: in is the information gain of decision tree D, is the entropy of decision tree D, and is the sub-decision tree after segmentation, is the total number of data in the decision tree D, and The total number of data in the posterior decision tree, c is the number of labeled categories, is the proportion of labeled data of the i-th category in the decision tree D, and the decision tree segmentation is completed by maximizing the information gain;

[0071] Iterate the decision tree segmentation until the decision tree information gain is lower than the set threshold, then stop the decision tree segmentation, and combine the decision trees to form a fault prediction model:

[0072] in For the model prediction results, Representative majority voting, represents the prediction result of the i-th decision tree, and t is the total number of decision trees in the fault prediction model;

[0073] The collected centralized flue data are input into the fault prediction model to predict centralized flue failure.

[0074] Data preparation and cleaning ensure that the data quality of the input model is high and improve the prediction accuracy of the model. Through data cleaning, irrelevant or erroneous data is filtered out to ensure the purity of the training and test data sets, providing a basis for efficient machine learning model training. As a simple and easy-to-understand machine learning algorithm, decision trees are suitable for handling classification problems and can make effective classification decisions based on actual data characteristics. Each decision tree is independent of other decision trees, which increases the overall diversity of the model and reduces the risk of overfitting. The calculation of information gain helps determine the most effective data segmentation method that should be considered when building a decision tree, which helps to improve the generalization ability of the model on unseen data. By optimizing information gain, the model can identify the most critical features, thereby improving the accuracy and efficiency of prediction. By integrating multiple decision trees, the stability and accuracy of the model are improved, so that the model has better prediction performance when facing unknown data. The prediction results of multiple models are integrated using the majority voting method, which reduces the risk of single model deviation and enhances the credibility and accuracy of the prediction results.

[0075] Furthermore, predictive maintenance based on the fault prediction results includes:

[0076] If the fault prediction result is that the centralized flue system is predicted to operate normally, the centralized flue system data will be collected and tested regularly, and a centralized flue system test record will be generated after each test and sent to the maintenance personnel for review. The maintenance personnel will attach an electronic signature and timestamp;

[0077] If the fault prediction result is that the centralized flue system is operating abnormally, the maintenance personnel will be notified immediately and the collected data will be sent to the maintenance personnel to analyze the predicted fault location for predictive maintenance, and the periodic collection and detection of centralized flue data will be adjusted to real-time collection and detection. When the maintenance personnel perform centralized flue maintenance, the centralized flue will be kept in a closed state, and the centralized flue fault will be re-predicted after the maintenance is completed. If the prediction result is normal, a centralized flue maintenance record will be generated. If the prediction result is still normal, the staff will be notified to continue the centralized flue analysis and maintenance until the prediction result is normal.

[0078] When the system predicts normal operation, maintenance personnel regularly collect and detect the data of the centralized flue system and generate detection records. Regular detection helps to identify potential minor changes or deviations in a timely manner, and can intervene in the early stages of the problem to prevent large-scale failures. The generated detection records provide detailed data support, which is convenient for historical data analysis and long-term system performance monitoring. If the system predicts an abnormality, the system will immediately notify the maintenance personnel and provide relevant data. The maintenance personnel analyze the fault location based on the data and perform maintenance. Rapid response to the abnormality predicted by the system can significantly reduce the impact of the fault on the operation. Real-time data collection and analysis ensure the accuracy of maintenance measures, improve maintenance efficiency and system reliability. During maintenance, the centralized flue remains closed, and real-time data is collected and detected to monitor and analyze the maintenance effect. Real-time monitoring provides immediate feedback during the maintenance process, which helps technicians adjust maintenance strategies and ensure that the problem is completely resolved. In addition, continuous data monitoring can capture the immediate improvement effect after maintenance, enhance the overall performance of the system, and ensure the effectiveness of maintenance measures through re-prediction. If the prediction is still abnormal, further maintenance measures can be taken quickly to ensure that the problem is fundamentally solved. This cyclic prediction and maintenance process greatly improves the stability and reliability of the system.

[0079] S4. Establish a three-dimensional model of the centralized flue, display the collected data and fault prediction results, and store all the data;

[0080] Specifically, establishing a three-dimensional model of the centralized flue and displaying the collected data and fault prediction results means establishing a three-dimensional model of the centralized flue based on the collected centralized flue geometry data, mapping the centralized flue data and fault prediction results to the three-dimensional model for display through data mapping and visualization, and deploying the three-dimensional model to the terminal for users to view.

[0081] By accurately reproducing the physical layout and structure of the centralized flue system, the 3D model provides an intuitive way to understand the complex system architecture and the relationships between components. The operating data and fault prediction results are intuitively displayed on the 3D model, allowing maintenance personnel to quickly identify problem areas and understand their possible impacts. This step is extremely critical for real-time monitoring of system status. It provides an effective way to interpret data and helps technicians respond quickly when necessary. By deploying the 3D model to user terminals such as computers, tablets or smartphones, multiple stakeholders can access the model and data anytime and anywhere, allowing operators, maintenance teams and management to share the same view, promoting information sharing and decision-making, while also improving the transparency of the system and user participation.

[0082] Furthermore, storing all data means that the database will store the collected centralized flue data, centralized flue mathematical models, fan frequency adjustment records, centralized flue fault prediction results, predictive maintenance measures and centralized flue three-dimensional models, and set security access rights and access passwords. The database will back up all stored data in the cloud and regularly check the integrity and consistency of the stored data and cloud data.

[0083] Ensure that all critical data, such as operating data, models, records, etc., are securely stored for future analysis and reference. This step is crucial for long-term monitoring of system performance, fault analysis, and predictive maintenance. It provides maintenance personnel with necessary information support. By setting access permissions and passwords, data is protected from unauthorized access, thereby ensuring data security and privacy. It ensures that only authorized personnel can access and modify critical system data, thereby preventing data leakage or malicious tampering, providing another layer of data security, and ensuring that local data can be restored from the cloud in the event of loss or damage, making data recovery faster and more convenient. Especially in the event of system failures or other disasters, regular data integrity and consistency checks help ensure that all stored information is accurate and complete, preventing problems caused by data errors and inconsistencies. Example

[0084] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0086] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0087] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A static pressure reset frequency conversion control method for a centralized flue system, characterized in that: include, Collect centralized flue data, build a centralized flue mathematical model, set static pressure monitoring points, detect the 24-hour fan opening rate, and calculate the static pressure control value of the static pressure monitoring point; Substitute the real-time opening rate of the fan into the centralized flue model to calculate the real-time static pressure value and compare it with the static pressure control value to adjust the fan operation; Predict centralized flue failures based on collected centralized flue data, and perform predictive maintenance based on the failure prediction results; Establish a centralized flue 3D model, display the collected data and fault prediction results, and store all data; The collecting of centralized flue data and constructing of centralized flue mathematical model refers to collecting the dimensions of the centralized flue main pipe and branch pipe, the resistance coefficient of the check valve, the performance parameters of the range hood, the resistance coefficient of the oil fume purification equipment, the performance parameters of the fan and the target air volume of the variable frequency control through sensors and measuring equipment to construct a centralized flue mathematical model. The construction of the centralized flue mathematical model includes: The centralized flue is divided into calculation nodes, and the fan is assumed to be opened evenly. The node opening and closing coefficient is calculated according to the fan opening rate; Calculate the ratio of each node air volume to the main pipe air volume; Calculate the dynamic pressure coefficient in the main pipe and the dynamic pressure ratio between the branch pipe and the main pipe; Determine the direct and converging tee resistance coefficients of each node, calculate the pressure distribution of each node in the direct and converging directions based on the node opening and closing coefficients, and obtain the total exhaust resistance; Calculate the static pressure control value of the range hood at the end of the branch pipe through the air volume; At this point, the mathematical model of the centralized flue has been completed; The static pressure monitoring point is set, and the static pressure control value of the static pressure monitoring point is calculated by detecting the 24-hour fan opening rate. Set the end of the centralized flue roof and the end range hood as static pressure monitoring points; Temperature self-meters are installed around the stove to collect temperature data. The user turns on the range hood when using the stove. When the temperature is higher than the indoor and outdoor temperatures, it is considered that the user is using the stove and the range hood is turned on. When the temperature drops sharply, it is considered that the user turns off the stove and the range hood is turned off. The number of users connected to the centralized flue that turn on the range hoods at the same time within 24 hours is summarized at each time to obtain the fan opening rate at each time within 24 hours; Input the fan opening rate at each time within 24 hours into the centralized flue model to obtain the static pressure control value at each time within 24 hours at the static pressure monitoring point; Substituting the real-time opening rate during the operation of the fan into the centralized flue model to calculate the real-time static pressure value and compare it with the static pressure control value to adjust the fan operation refers to real-time monitoring of the fan opening coefficient during the actual operation of the centralized flue, monitoring the real-time static pressure value through a static pressure sensor, comparing the real-time static pressure value with the static pressure control value at that moment, setting the static pressure error, and reducing the fan operation frequency when the real-time static pressure value is less than the difference between the static pressure control value and the static pressure error, and increasing the fan operation frequency when the real-time static pressure value is greater than the sum of the static pressure control value and the static pressure error, and generating a fan adjustment record after the adjustment is completed; The method of predicting centralized flue failure based on the collected centralized flue data includes: Collect the historical data of the centralized flue, clean and filter them, randomly divide them into training data set and test data set, and label the data of the training data set and test data set by categories, including normal and abnormal; Randomly select data from the training data set to create training subsets, and build a decision tree for each training subset; Calculate the decision tree segmentation information gain for decision tree segmentation: in is the information gain of decision tree D, is the entropy of decision tree D, and is the sub-decision tree after segmentation, is the total number of data in the decision tree D, and The total number of data in the posterior decision tree, c is the number of labeled categories, is the proportion of labeled data of the i-th category in the decision tree D, and the decision tree segmentation is completed by maximizing the information gain; Iterate the decision tree segmentation until the decision tree information gain is lower than the set threshold, then stop the decision tree segmentation, and combine the decision trees to form a fault prediction model: in For the model prediction results, Representative majority voting, represents the prediction result of the i-th decision tree, and t is the total number of decision trees in the fault prediction model; Inputting the collected centralized flue data into a fault prediction model to predict centralized flue faults; The predictive maintenance according to the fault prediction result includes: If the fault prediction result is that the centralized flue system is predicted to operate normally, the centralized flue system data will be collected and tested regularly, and a centralized flue system test record will be generated after each test and sent to the maintenance personnel for review. The maintenance personnel will attach an electronic signature and timestamp; If the fault prediction result is that the centralized flue system is operating abnormally, the maintenance personnel will be notified immediately and the collected data will be sent to the maintenance personnel to analyze the predicted fault location for predictive maintenance, and the periodic collection and detection of centralized flue data will be adjusted to real-time collection and detection. When the maintenance personnel perform centralized flue maintenance, the centralized flue will be kept in a closed state, and the centralized flue fault will be re-predicted after the maintenance is completed. If the prediction result is normal, a centralized flue maintenance record will be generated. If the prediction result is still normal, the staff will be notified to continue the centralized flue analysis and maintenance until the prediction result is normal.

2. The static pressure reset frequency conversion control method of the centralized flue system according to claim 1, characterized in that: The establishing of a three-dimensional model of a centralized flue and display of collected data and fault prediction results refers to establishing a three-dimensional model of a centralized flue based on the collected centralized flue geometric data, mapping the centralized flue data and fault prediction results to the three-dimensional model through data mapping and visualization for display, and deploying the three-dimensional model to a terminal for user viewing.

3. The static pressure reset frequency conversion control method of the centralized flue system according to claim 2, characterized in that: The storage of all data refers to the database storing the collected centralized flue data, centralized flue mathematical models, fan frequency adjustment records, centralized flue fault prediction results, predictive maintenance measures and centralized flue three-dimensional models, and setting security access rights and access passwords. The database will back up all stored data in the cloud and regularly perform integrity and consistency checks on the stored data and cloud data.

4. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the static pressure reset frequency conversion control method for the centralized flue system according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the static pressure reset variable frequency control method for a centralized flue system according to any one of claims 1 to 3 are implemented.

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