A method and system for predicting average air volume in roadways

Abstract

This application relates to the field of data processing technology and discloses a method and system for predicting average air volume in roadways. The method includes: collecting roadway parameters to obtain resistance range data; quantifying air volume uncertainty to obtain the air volume range; converting it into triangular fuzzy numbers to obtain fuzzy air volume distribution data; fusing it using a weighted operator to obtain fused average air volume data; and obtaining predicted air volume data based on historical sequence error correction. This application improves the accuracy of average air volume prediction in roadways.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a roadway average air volume prediction method and system. BACKGROUND

[0002] The existing roadway average air volume prediction method is mainly based on the traditional fluid mechanics principle and numerical calculation model. By collecting basic data such as the geometric parameters of the roadway, the ventilation resistance coefficient, and the fan performance parameters, the complex mine ventilation system is simplified into a network model composed of nodes and branches by using the ventilation network theory, a mathematical model is established by applying the air volume balance equation and the air pressure balance equation, and the network equation set is solved by the iterative calculation method to obtain the air volume distribution of each roadway branch. This method can provide a certain prediction accuracy under stable working conditions, and provides a basic technical support for the design and operation management of mine ventilation system.

[0003] However, the data acquisition accuracy is low and there is great uncertainty. The roadway parameters such as roughness coefficient and cross-sectional area often depend on empirical estimation or single measurement, and cannot accurately reflect the actual parameter variation range. Secondly, the prediction model is too simplified, regarding the roadway resistance and air volume as deterministic values, ignoring the influence of measurement error and parameter fluctuation on the prediction result. Thirdly, there is a lack of effective multi-data source fusion mechanism, and different types of data obtained by multiple sensors cannot be fully utilized to improve the prediction accuracy.

[0004] Due to the inability to effectively handle the measurement uncertainty of the roadway parameters, the input data reliability of the prediction model is insufficient. There is a lack of special data fusion algorithm for roadway characteristics, so that the multi-sensor data cannot be effectively integrated. At the same time, there is a lack of error correction mechanism based on historical data, so that the prediction result cannot be dynamically adjusted and optimized according to the actual operation. SUMMARY

[0005] The present application provides a roadway average air volume prediction method and system, which solves the problems of insufficient data uncertainty processing capability, low multi-source data fusion efficiency, and poor prediction accuracy stability in the existing roadway air volume prediction method, and improves the accuracy and reliability of the roadway average air volume prediction.

[0006] In a first aspect, the present application provides a roadway average air volume prediction method, which comprises:

[0007] The roadway cross-sectional area, the roadway length, and the roadway roughness coefficient are collected and processed by a multi-sensor network to obtain roadway resistance interval data;

[0008] The roadway air volume uncertainty is quantitatively processed according to the roadway resistance interval data to obtain a roadway air volume interval range;

[0009] The roadway air volume interval range is subjected to triangular fuzzy number conversion processing to obtain roadway air volume fuzzy distribution data.

[0010] The roadway air volume fuzzy distribution data is subjected to fusion processing by a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data.

[0011] The roadway average air volume fusion data is used for error correction processing on a roadway historical air volume sequence to obtain roadway average air volume prediction data.

[0012] In a second aspect, the present application provides a roadway average air volume prediction system, which comprises:

[0013] A collection module is configured to collect and process a roadway cross-sectional area, a roadway length and a roadway roughness coefficient by a multi-sensor network to obtain roadway resistance interval data.

[0014] A quantification module is configured to quantitatively process roadway air volume uncertainty according to the roadway resistance interval data to obtain a roadway air volume interval range.

[0015] A conversion module is configured to subject the roadway air volume interval range to triangular fuzzy number conversion processing to obtain roadway air volume fuzzy distribution data.

[0016] A fusion module is configured to subject the roadway air volume fuzzy distribution data to fusion processing by a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data.

[0017] A correction module is configured to use the roadway average air volume fusion data to perform error correction processing on a roadway historical air volume sequence to obtain roadway average air volume prediction data.

[0018] In a third aspect, a roadway average air volume prediction device is provided, which comprises a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the roadway average air volume prediction device to perform the above-mentioned roadway average air volume prediction method.

[0019] In a fourth aspect, a computer readable storage medium is provided, which stores instructions, and when the instructions are run on a computer, the computer performs the above-mentioned roadway average air volume prediction method.

[0020] In the technical scheme provided in the application, the roadway resistance interval data is obtained by collecting and processing the roadway cross-sectional area, the roadway length and the roadway roughness coefficient through the multi-sensor network, effectively solving the problem of insufficient data reliability caused by single-point measurement of the roadway parameters in the traditional method, converting the original deterministic parameter value into interval data capable of reflecting the actual measurement uncertainty, and providing more real and reliable basic data support for subsequent air volume prediction. According to the roadway resistance interval data, the roadway air volume interval range is obtained by quantitatively processing the uncertainty of the roadway air volume, the uncertainty theory is innovatively introduced into the field of roadway air volume calculation, the influence of parameter uncertainty on the air volume prediction result is effectively transmitted and quantified through interval operation, and the technical defect of ignoring error accumulation in the traditional deterministic calculation method is avoided. The roadway air volume interval range is converted into roadway air volume fuzzy distribution data through triangular fuzzy number conversion processing, further enriching the expression form of the air volume data, and more accurately depicting the probability distribution characteristics of the roadway air volume through the fuzzy mathematics theory, thereby laying a theoretical foundation for realizing accurate air volume fusion.

[0021] The roadway average air volume fusion data is obtained by fusing the roadway air volume fuzzy distribution data through the roadway weight-induced ordered weighted average operator, the operator fully considers the importance difference of different roadways in the ventilation system, highlights the role of the main ventilation roadway through a dynamic weight distribution mechanism, effectively reduces the adverse influence of the measurement error of the secondary roadway on the overall prediction result, and significantly improves the accuracy and rationality of the multi-roadway air volume data fusion. According to the roadway average air volume fusion data, the roadway average air volume prediction data is obtained by error correction processing on the roadway historical air volume sequence, the time series analysis and error compensation mechanism are innovatively introduced, the current prediction result can be dynamically corrected according to the change law of the historical operation data, the technical limitations that the traditional static prediction method cannot adapt to the change of the roadway ventilation condition are effectively solved, the adaptive optimization of the roadway air volume prediction is realized, and the stability of the prediction precision is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical scheme of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating labor on the basis of these drawings.

[0023] Figure 1 An embodiment schematic diagram of the roadway average air volume prediction method in the embodiment of the application;

[0024] Figure 2 A membership function schematic diagram of the triangular fuzzy number in the embodiment of the application;

[0025] Figure 3An embodiment of a roadway average air volume prediction system in the present application is shown in the figure;

[0026] Figure 4 An embodiment of a roadway average air volume prediction device in the present application is shown in the figure. DETAILED DESCRIPTION

[0027] The present application provides a roadway average air volume prediction method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned figures are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the sake of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 An embodiment of a roadway average air volume prediction method in the present application includes:

[0029] Step S101, collecting and processing the roadway cross-sectional area, roadway length and roadway roughness coefficient through a multi-sensor network to obtain roadway resistance interval data;

[0030] Step S102, quantitatively processing the roadway air volume uncertainty according to the roadway resistance interval data to obtain a roadway air volume interval range;

[0031] Step S103, performing triangular fuzzy number conversion processing on the roadway air volume interval range to obtain roadway air volume fuzzy distribution data;

[0032] Step S104, performing fusion processing on the roadway air volume fuzzy distribution data through a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data;

[0033] Step S105, performing error correction processing on a roadway historical air volume sequence according to the roadway average air volume fusion data to obtain roadway average air volume prediction data.

[0034] It can be understood that the execution subject of the present application can be a roadway average air volume prediction system, and can also be a terminal or a server, and the specific place is not limited. The embodiments of the present application take the server as the execution subject for example.

[0035] Specifically, the cross-sectional area, length and roughness coefficient of the roadway are collected by the multi-sensor network. The cross-sectional area, length and roughness coefficient of the roadway are the key parameters affecting the air volume calculation. These data are collected by the sensor network in real time and converted into interval data to provide an interval range for each parameter. This processing method effectively reduces the error that may be generated by a single measurement. The obtained interval data of the roadway resistance are analyzed and calculated to obtain the interval range of the air volume and quantify the influence of different factors on the air volume prediction. The traditional prediction method usually assumes that the air volume is a deterministic value, while the present method uses interval data to more comprehensively reflect the uncertainty of the air volume and improve the reliability of the prediction results. After obtaining the interval range of the air volume, triangular fuzzy number conversion processing is performed to represent the fuzziness of the air volume by defining upper and lower limit values and the most likely value, and to obtain the fuzzy distribution data of the air volume.

[0036] The ordered weighted average operator with roadway weight induction is used to fuse the fuzzy distribution data of the air volume. According to the importance of each roadway in the entire ventilation system, a weight value is assigned to each roadway, and the weight of the main ventilation roadway is larger, while the weight of the secondary ventilation roadway is smaller. The weighted average operator is used to fuse the fuzzy distribution data of the air volume of different roadways to obtain the unified average air volume data of the roadway. This process effectively handles the differences between the multi-roadway data and ensures that the influence of the main roadway is given priority, thereby improving the overall prediction accuracy. Based on the fused average air volume data of the roadway, the error of the historical air volume sequence is corrected. Through time series analysis, the periodic change pattern of the air volume is identified, the time decay coefficient is calculated, and the time weight decay function is constructed. The historical air volume data is analyzed by using the decay function to obtain the historical error sequence, and the error is compensated by using the trend prediction method to correct the prediction value, and finally more accurate average air volume prediction data of the roadway are obtained.

[0037] Taking the ventilation system of a certain mine as an example, the multi-sensor network is used to collect the key parameters such as the cross-sectional area, length and roughness coefficient of the mine roadway in real time. Through the sensors deployed inside the mine, data can be obtained in real time under different roadways and different working conditions and transmitted to the central control system. These data are not presented in the form of a single numerical value, but in the form of interval data, i.e., an interval range is provided for each measured value. This processing method can effectively reduce the error of a single measurement and more truly reflect the influence of environmental factors on the measurement results.

[0038] Obtain the roadway resistance interval data, and quantify the uncertainty of air volume. In this process, the system analyzes the resistance interval data of the mine roadway, combines with the air volume balance equation for calculation, and obtains the interval range of air volume. Unlike traditional air volume prediction methods, this method uses interval operation instead of treating air volume as a single deterministic value, thus more comprehensively reflecting the influence of uncertain factors on air volume prediction results. Through this quantitative processing, the system can handle air volume fluctuations caused by equipment measurement errors, environmental changes or other uncertain factors, and thus provide more accurate and reliable air volume interval prediction.

[0039] After obtaining the air volume interval, the air volume interval data is converted into triangular fuzzy numbers. By defining the upper and lower bounds of air volume and its most likely value, fuzzy mathematical methods are used to convert these air volume data into fuzzy distribution data. This process can more accurately represent the uncertainty of air volume, and by calculating the membership function of air volume, further refine the probability distribution of air volume data. Through this conversion process, not only can the measurement range of air volume be expressed, but also the probability characteristics of air volume changes can be considered, making the data more consistent with actual conditions in subsequent processing. The system obtains fuzzy distribution data of air volume, which provides necessary basis for subsequent data fusion and correction.

[0040] The system fuses the fuzzy distribution data of air volume of different roadways through weighted average operator. Since the mine ventilation system is composed of multiple roadways, and different roadways have different importance in the ventilation system, each roadway needs to be assigned different weights. For main ventilation roadways, the system assigns larger weights, while for secondary ventilation roadways, the weights are smaller. Through the ordered weighted average operator, the air volume data of multiple roadways are weighted and fused, and finally the unified average air volume data of the roadway is obtained.

[0041] The system corrects the error of historical air volume sequence. Through time series analysis, the system identifies the periodic variation pattern of air volume, and calculates the time decay coefficient based on these patterns to construct a time weight decay function. This function can analyze the historical air volume data by weighting according to time changes, and obtain the error sequence of air volume data. The system uses trend prediction method to compensate for the error, thereby adjusting the air volume prediction value. This correction process can eliminate the deviation caused by measurement error, external environmental change or unstable equipment running condition, etc., to ensure that the final air volume prediction data is more consistent with the actual situation. Through these processing steps, the system can provide accurate air volume prediction data to help mine management personnel monitor and adjust the ventilation system in real time.

[0042] In the embodiments of the present application, the cross-sectional area, length and roughness coefficient of the roadway are collected and processed by a multi-sensor network to obtain roadway resistance interval data, effectively solving the problem that single measurement data in traditional methods cannot reflect actual environmental changes and measurement errors. Traditional air volume prediction methods usually rely on single deterministic data, which cannot take into account factors such as measurement errors and equipment fluctuations in the complex environment of mine roadways. Through the deployment of a multi-sensor network, multiple key parameters within the roadway can be collected in real time and converted into interval data. This processing method not only reduces the errors that may be caused by single measurement, but also more realistically reflects the uncertainty of actual measurement, providing more reliable basic data for subsequent air volume prediction. After obtaining the roadway resistance interval data, the system quantifies the uncertainty of the roadway air volume through the air volume balance equation to obtain the interval range of the air volume. Unlike traditional methods, this method quantifies the air volume using interval data, avoiding the limitations of treating air volume as a deterministic value. In this way, the system can comprehensively reflect the uncertainty factors caused by the environment and equipment, thereby improving the reliability of the air volume prediction results. The system converts the interval range of the roadway air volume into a triangular fuzzy number and uses fuzzy mathematical methods to accurately represent the range of air volume changes. In this process, the system extracts the upper and lower bounds of the air volume by combining historical air volume data and calculates the most likely value of the air volume to ultimately construct the fuzzy distribution data of the air volume. This processing method makes the air volume data more consistent with the actual measurement characteristics and can express the probability characteristics of air volume changes. According to the importance of each roadway in the ventilation system, the system assigns different weights to different roadways. Through a weighted average operator, the system weights and fuses the air volume fuzzy distribution data of each roadway to ultimately obtain unified roadway average air volume data, improving the accuracy of the prediction. The system corrects the error of the historical air volume sequence through time series analysis, identifies the periodic change pattern of the air volume and calculates the time decay coefficient, and uses this coefficient to weight the historical data to obtain the historical error sequence. The system compensates for the error through a trend prediction method to correct the air volume prediction value, ultimately providing accurate roadway air volume prediction results, thereby providing effective decision support for the management and optimization of mine ventilation systems.

[0043] In a specific embodiment, the process of step S101 can specifically include the following steps:

[0044] (1) Perform error range calculation and processing on the roadway cross-sectional area measurement value to obtain roadway cross-sectional area interval boundary values;

[0045] (2) Based on the roadway cross-sectional area interval boundary values, correct the roadway length measurement data to obtain roadway length correction parameters;

[0046] (3) Perform change range analysis and processing on the roadway roughness coefficient to obtain the roadway roughness interval coefficient;

[0047] (4) Interval operation processing is performed on the tunnel resistance value based on the tunnel cross-sectional area interval boundary value and the tunnel length correction parameter, to obtain a tunnel resistance lower limit value and a tunnel resistance upper limit value;

[0048] (5) Interval number construction processing is performed based on the tunnel resistance lower limit value and the tunnel resistance upper limit value, to obtain the tunnel resistance interval data.

[0049] Specifically, the cross-sectional area of the tunnel is measured by a multi-sensor network. The sensor will collect the cross-sectional area data of the tunnel in real time and convert it into interval data. This conversion can effectively reduce the influence of single measurement error and more accurately reflect the actual cross-sectional area of the tunnel. In this way, the system can monitor the changes in the geometric shape of the tunnel in real time and provide a reliable basis for subsequent air volume calculation.

[0050] The length of the tunnel is measured by a sensor network, and the system converts the total length of the tunnel into interval data to ensure that the length data of each tunnel paragraph is accurately recorded. The use of interval data further improves the fault tolerance of possible measurement errors, ensuring accuracy in actual environments.

[0051] In order to accurately calculate the wind pressure loss, the sensor will measure the roughness of the tunnel and convert the measured roughness coefficient into interval data. This method takes into account the influence of roughness on air volume calculation and avoids errors that may be caused by single numerical measurement, so that the air volume prediction result can be more in line with the actual situation. By using interval data, the system can fully consider the changes of roughness coefficient and improve the prediction accuracy.

[0052] For the obtained measurement data of tunnel cross-sectional area, length and roughness coefficient, the system will calculate the error range and correct the accuracy of the data. By analyzing the measurement error of the sensor and the influence of environmental changes, the system can determine the interval boundary of each measurement value. The data can effectively cover all possible error ranges.

[0053] After obtaining the interval of all measurement data, the system will calculate the resistance interval of the tunnel through interval operation. This process combines the geometric characteristics of the tunnel and the air flow conditions, and through a complex calculation model, the upper and lower limits of the tunnel resistance are obtained. The interval data of the tunnel resistance provides an accurate basis for subsequent air volume prediction, ensuring that the air volume calculation can be more in line with the actual ventilation situation.

[0054] Taking the ventilation system of an underground parking lot as an example, in order to achieve more accurate air volume prediction, a method of obtaining parking lot geometric parameters through a multi-sensor network is implemented. The sensor installed in the parking lot passageway measures the cross-sectional area, the sensor collects the cross-sectional area data of the parking lot passageway in real time, and according to the measurement results, it is converted into interval data. Due to the actual shape of the parking lot passageway may be affected by factors such as vehicles, environmental temperature, etc., the sensor uses interval data, which can accurately reflect the actual cross-sectional area and eliminate the influence of single measurement error.

[0055] After obtaining the cross-sectional area data, the system will continue to measure the length of the passageway. Through the sensor network, the full length data of the parking lot passageway is obtained in real time, and the specific length of each passage paragraph is calculated according to these data. The data will also be converted into interval data to ensure that the length of each passage is accurately recorded. In the calculation of air volume distribution, high-precision length data is crucial to ensure the accuracy of air volume prediction. The use of interval data improves the fault tolerance of measurement, ensuring that all potential errors and changes are considered in air volume calculation.

[0056] The sensor will measure the roughness coefficient of the parking lot passageway. The roughness coefficient affects the wind pressure loss. The sensor will record the roughness data of the surface through high-precision measurement tools and convert it into interval data. In this way, during the air volume calculation process, the system can effectively consider the influence of roughness on air volume, avoiding the error caused by regarding roughness as a fixed value in traditional methods.

[0057] After these data are collected and converted into interval data, the system will calculate and correct the error range of all data. Considering the possible measurement errors of the sensor, the system will correct the data of the cross-sectional area, length and roughness coefficient of the passageway according to the known sensor error range and the actual working environment. The corrected data can effectively cover all possible error ranges, ensuring that the measurement results are more accurate in the actual environment, and avoiding the influence of measurement error on the air volume prediction results.

[0058] The system will combine all the measured interval data to calculate the resistance interval of the passageway through interval operation. During this process, the system considers the geometric characteristics of the passageway, roughness, air flow speed and other factors, and finally obtains the upper and lower bounds of the passageway resistance. This interval data serves as the input for air volume prediction, ensuring the accuracy of subsequent air volume calculation. The implementation of these processing steps enables the parking lot ventilation system to more accurately predict air volume, ensuring air circulation and environmental quality in the parking lot.

[0059] In a specific embodiment, the process of performing step S102 can specifically include the following steps:

[0060] (1) input the roadway resistance interval data into the air volume continuity calculation for node balance processing to obtain a roadway node air volume balance constraint condition;

[0061] (2) perform interval calculation processing on the roadway wind pressure loss value based on the roadway node air volume balance constraint condition to obtain a roadway wind pressure loss interval value;

[0062] (3) perform balance analysis processing on the roadway loop wind pressure according to the roadway wind pressure loss interval value to obtain roadway loop balance data;

[0063] (4) perform iterative solution processing on the roadway loop balance data to obtain a lower limit value of roadway air volume and an upper limit value of roadway air volume;

[0064] (5) perform interval range construction processing based on the lower limit value of roadway air volume and the upper limit value of roadway air volume to obtain the roadway air volume interval range.

[0065] Specifically, the system inputs the roadway resistance interval data into the air volume continuity calculation model for node balance processing. Through this process, the system calculates the air volume distribution between each air volume node according to the roadway resistance data. This step is to accurately simulate the airflow flow in the roadway and avoid uneven air volume distribution, forming a preliminary air volume prediction model.

[0066] After the node balance processing, the system calculates the roadway wind pressure loss interval based on the balance constraint condition. By analyzing factors such as the geometric characteristics of the roadway, air flow rate, and resistance, the system calculates the interval data of the wind pressure loss. Wind pressure loss directly affects the ventilation effect, and accurate wind pressure loss interval provides key information for subsequent calculations, enabling the system to more accurately adjust the ventilation scheme.

[0067] The system performs balance analysis on the wind pressure of the roadway loop. This process ensures that the wind pressure between different air ducts and loops reaches balance by analyzing the wind pressure data of each roadway loop. This analysis not only considers the resistance of the roadway, but also considers the changes in air flow. Through balance analysis, the system can optimize the wind pressure distribution, so that the air volume is reasonably distributed among the loops, improving the overall efficiency of the ventilation system.

[0068] After completing the balance analysis of the loop wind pressure, the system performs iterative solution processing on the air volume. In this process, the system continuously adjusts the results of air volume distribution until the air volume reaches a stable balance state between nodes. Through multiple iterations, the system can minimize errors caused by initial assumptions, making the air volume prediction result more accurate and ensuring that the air volume distribution of all loops meets the actual ventilation demand.

[0069] The system constructs a wind volume interval range based on the obtained lower and upper bounds of wind volume. The construction of the interval range can reflect the uncertainty of wind volume and provide more accurate data support for subsequent wind volume calculation, helping the system dynamically adjust the prediction results to meet the ventilation demand under different environmental conditions.

[0070] Taking the air conditioning system of a large office building as an example, sensors measure real-time indoor temperature, humidity, air flow speed and other parameters, and combine with the resistance data of the air conditioning system to convert them into interval data, reducing the influence of single measurement error and ensuring the accurate reflection of air flow. The system uses these resistance data to calculate the wind pressure loss interval, accurately quantifies the wind pressure loss of each area according to the air flow speed and resistance coefficient, and provides a basis for air flow adjustment. The system also balances the wind pressure difference between the return air outlet and the supply air outlet, adjusts the amount of return air and supply air, ensures uniform indoor air flow, and avoids local overheating or overcooling. By iterative solution, the air flow is adjusted to ensure accurate distribution of wind volume in each area, thereby optimizing the efficiency of the air conditioning system, and through wind volume interval adjustment, ensuring efficient operation and energy saving effect of the system under different environmental conditions.

[0071] In a specific embodiment, the process of performing step S103 can specifically include the following steps:

[0072] (1) Extract and process the lower and upper bounds of wind volume based on the interval range of roadway wind volume to obtain roadway wind volume boundary parameters;

[0073] (2) Statistically analyze and process the historical wind volume data of the roadway according to the roadway wind volume boundary parameters to obtain the most likely value of the roadway wind volume;

[0074] (3) Perform triangular fuzzy number construction processing on the roadway wind volume boundary parameters and the most likely value of the roadway wind volume to obtain the triangular fuzzy number of roadway wind volume;

[0075] (4) Perform membership function calculation processing on the triangular fuzzy number of roadway wind volume to obtain the membership degree distribution curve of roadway wind volume;

[0076] (5) Perform fuzzy distribution data assembly processing based on the membership degree distribution curve of roadway wind volume to obtain the fuzzy distribution data of roadway wind volume.

[0077] Specifically, the system extracts the upper and lower bounds of the roadway wind volume interval, calculates the maximum and minimum values of the wind volume in the roadway, and obtains the possible range of the wind volume to provide data support for subsequent fuzzy number construction. Combined with the statistical analysis of historical wind volume data, the most likely value of the roadway wind volume is calculated, the most likely value is determined according to the actual wind volume fluctuation law, and the prediction accuracy is improved. According to the most likely value and the upper and lower bounds of the wind volume interval, the triangular fuzzy number of roadway wind volume is constructed, and the uncertainty of wind volume is represented using fuzzy mathematical method to express the fluctuation range of wind volume.Figure 2 FIG. 16 is a schematic view of a membership function of a triangular fuzzy number provided by an embodiment of the present application, which represents the membership degree of the air volume in different intervals through the membership function to obtain the probability distribution of the air volume change, wherein the membership function formula of the triangular fuzzy number is described as: wherein is a specific value of the air volume, is the minimum value of the air volume interval, is the most likely value of the air volume interval, is the maximum value of the air volume interval. Based on the air volume membership distribution curve, fuzzy distribution data is assembled, the air volume data is organized into a fuzzy distribution form through the membership function to reflect the possibility of the air volume change, and based on the air volume membership distribution curve a fuzzy distribution data set can be constructed. A discrete air volume data set is set each air volume value corresponds to a membership value . The fuzzy distribution data set of the air volume can be expressed as: wherein, represents the fuzzy distribution data set of the air volume, which contains the air volume value and the corresponding membership value .

[0078] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0079] (1) Based on the tunnel air volume fuzzy distribution data, the importance of the tunnel cross-sectional area and the tunnel length is calculated and processed to obtain a tunnel importance index;

[0080] (2) According to the tunnel importance index, the weight distribution processing of the tunnel function coefficient is performed to obtain a tunnel dynamic weight value;

[0081] (3) The tunnel dynamic weight value is input into a sorting algorithm for importance sorting processing to obtain a tunnel weight sorting sequence;

[0082] (4) Based on the tunnel weight sorting sequence, the tunnel air volume fuzzy distribution data is weighted and fused to obtain a tunnel air volume weighted result;

[0083] (5) According to the tunnel air volume weighted result, the average value calculation processing is performed to obtain the tunnel average air volume fusion data.

[0084] Specifically, the system calculates the importance index of each roadway according to the roadway air volume fuzzy distribution data, analyzes the cross-sectional area and length data of the roadway, evaluates the role and importance of each roadway in the overall ventilation system, and obtains an importance value, which reflects the key degree of the roadway in the ventilation effect. The system assigns weights according to the calculated roadway importance index, ensures that the main ventilation roadway obtains a larger weight, and the weight of the secondary ventilation roadway is relatively small, which can more accurately reflect the influence degree of each roadway on air volume distribution and overall ventilation effect, and the key roadway has a greater influence on the prediction result. The system inputs the roadway dynamic weight into the sorting algorithm, and sorts the roadways according to the weight value, so as to ensure that the air volume data of important roadways is processed first in the air volume prediction, thereby improving the ventilation efficiency and accuracy. The system performs weighted fusion on the air volume fuzzy distribution data according to the roadway weight sorting sequence, ensures that the data of each roadway is weighted according to its importance, and finally makes the air volume prediction result more accurate and in line with the actual ventilation demand. Through the weighted fusion result, the system calculates the average air volume data of the roadway, and provides accurate reference for subsequent ventilation control and system adjustment, ensuring the stable operation and effective adjustment of the ventilation system.

[0085] Taking the air conditioning system of an intelligent office building as an example, the system collects temperature, humidity, air flow speed and other data of each room through sensors, calculates the importance index of each room, determines the weight based on the room area and use frequency, and ensures that high-frequency use rooms and large-area rooms have a larger proportion in air volume distribution. The system assigns weights to each room according to these importance indexes, ensuring that the air volume of the main office area is allocated more preferentially. The system inputs the weight of each room into the sorting algorithm, sorts the rooms according to the weight, so as to prioritize the rooms with larger weights in the air volume prediction process, thereby improving the energy efficiency of the overall air conditioning system. The system performs weighted fusion on the air volume data of each room according to the weight sorting result, ensuring that the air volume prediction of each room meets its actual demand and is accurate. The system calculates the average air volume data of the entire office building through the weighted result, providing accurate reference data for subsequent temperature control and air conditioning, and ensuring that the temperature and air quality of each area in the building are maintained in an ideal state.

[0086] In a specific embodiment, the execution step can specifically include the following steps based on the roadway air volume fuzzy distribution data:

[0087] (1) performing cross-sectional area extraction processing on the roadway air volume fuzzy distribution data to obtain roadway cross-sectional area values;

[0088] (2) performing ratio calculation processing on the maximum cross-sectional area based on the roadway cross-sectional area values to obtain a roadway cross-sectional area weight coefficient;

[0089] (3) The reference length is processed by inverse operation according to the roadway length correction parameter to obtain a roadway length weight coefficient;

[0090] (4) The roadway sectional area weight coefficient and the roadway length weight coefficient are multiplied to obtain a roadway basic importance value;

[0091] (5) The roadway function importance coefficient is weighted and multiplied based on the roadway basic importance value to obtain the roadway importance index.

[0092] Specifically, the system analyzes the roadway air volume fuzzy distribution data, extracts key parameters related to the sectional area. The air volume fuzzy distribution reflects the flow characteristics of the airflow through the section, combined with the physical relationship between the wind speed and the air volume, the sectional area value of the roadway is deduced. The system calculates or fits the functional relationship between the wind speed distribution and the air volume by fuzzy number expectation, extracts representative section information, eliminates the interference of air volume uncertainty on sectional area estimation, and forms a structural description of different roadway sectional areas. After obtaining the sectional area of the roadway, the sectional area weight coefficient is obtained by the ratio between the current roadway sectional area and the maximum sectional area, reflecting the relative proportion of the roadway to the air flow carrying capacity. The larger the ratio, the more significant the contribution of the roadway to the ventilation system, and thus a higher weight is given in the subsequent importance modeling.

[0093] The system introduces a length correction mechanism to normalize the roadway length. The inverse of the reference length is calculated, and shorter roadways are given higher weights to reflect their priority in air flow transport path. Shorter roadways mean smaller ventilation resistance, higher air flow efficiency, and stronger control over the overall ventilation path of the system, so they are more important. Multiply the roadway sectional area weight coefficient and the length weight coefficient to obtain the basic importance value of the roadway physical structure to the air volume transmission capacity. This value considers both sectional size and length resistance, reflecting the physical value of the roadway in air flow transmission.

[0094] The system further introduces a roadway function importance coefficient to consider the regulatory effect of the function role of the roadway on the overall ventilation system. For example, whether a certain roadway is a main air flow channel, whether it connects multiple branches, whether it undertakes special safety tasks, etc. The basic importance value and the function importance coefficient are weighted and multiplied to obtain the importance index of the roadway. This index can reflect the influence of the physical parameters and functional attributes of the roadway on the running state of the ventilation system, making the importance evaluation have both physical meaning and practical application value, providing support for subsequent air volume allocation, prediction optimization, etc.

[0095] Taking the ventilation system of an intelligent warehouse as an example, the system analyzes the air volume fuzzy distribution data of each area in the warehouse, and extracts the sectional area parameters of different areas in the warehouse. By combining the air flow and the wind speed, the sectional area values of each section of the warehouse are back calculated, and the ratio of the sectional area of each area to the maximum sectional area is calculated to obtain the sectional area weight coefficient of each area, which is used to evaluate the carrying capacity of the area to air flow. Then, the system normalizes the length information of each area in the warehouse, and by calculating the reciprocal of the reference area length, a higher weight is given to the shorter area to reflect its priority in air circulation. Then, by multiplying the sectional area weight coefficient and the length weight coefficient, the basic importance value of each area is obtained, which represents the physical ventilation capacity of each area. On this basis, combined with the functional settings of the warehouse, such as the functional differences between the main cargo storage area and the passage area, the system introduces a functional importance coefficient to weight the basic importance value to obtain the final area importance index. Through this index, the system can dynamically adjust the ventilation effect of different areas in the warehouse, optimize the air volume distribution, and improve the overall operation efficiency of the ventilation system.

[0096] In an embodiment, the process of performing step S105 can specifically include the following steps:

[0097] (1) Time series analysis is performed on the roadway average air volume fusion data to obtain a roadway air volume periodic change mode;

[0098] (2) Based on the roadway air volume periodic change mode, a time weight decay function of the roadway is calculated to obtain a roadway time weight decay function;

[0099] (3) According to the roadway time weight decay function, the roadway historical air volume sequence is weighted and analyzed to obtain a roadway historical error sequence;

[0100] (4) The roadway historical error sequence is trend forecasted to obtain a roadway error prediction value;

[0101] (5) Based on the roadway average air volume fusion data and the roadway error prediction value, error compensation is performed to obtain the roadway average air volume prediction data.

[0102] Specifically, the system performs time series analysis on the roadway average air volume fusion data to identify periodic change patterns in the air volume data. Through long-term analysis of historical air volume data, regular fluctuations in air volume can be revealed, which may be due to seasonal factors, work scheduling, or other external environmental changes. The system identifies the periodic trends implied in the air volume data through time series processing and establishes a corresponding periodic change model, which provides a theoretical basis and data support for subsequent air volume prediction. Using this periodic change pattern, the system calculates a time decay coefficient to measure the influence of historical data on current air volume prediction over time. The calculation of the decay coefficient takes into account the time factor, with the influence of distant historical data on current prediction gradually weakening, and newer data contributing more to the prediction results. This mechanism can effectively improve the timeliness and accuracy of the prediction results.

[0103] After obtaining the time decay coefficient, the system performs weighted analysis on the historical air volume sequence of the roadway to obtain a historical error sequence. The weighted analysis process assigns different weights to historical data based on the decay coefficient, and combines the error information of historical data to identify deviations and fluctuation trends in air volume prediction. This process helps identify possible sources of error in air volume prediction and provides a basis for subsequent error correction. By analyzing the error sequence, the system can more clearly understand which data has a greater impact on prediction and further provide a reference for optimizing the prediction model. Based on the historical error sequence, the system uses trend prediction methods to compensate for errors. Through trend analysis of errors, the system can identify future error trends and correct future air volume prediction results, reducing prediction bias.

[0104] The system combines the compensated error with the roadway average air volume fusion data to perform error compensation processing and obtain the final air volume prediction data. Through this error compensation processing, the system can optimize the original air volume prediction data and eliminate the uncertainty caused by historical errors. This process not only improves the accuracy of air volume prediction, but also enables the ventilation system to more flexibly respond to changing environments and working conditions. Error compensation processing combined with air volume prediction data provides a more reliable basis for ventilation system control, ensuring system stability and efficiency. Through this method, the system can achieve accurate air volume control in various complex environments and improve the efficiency of the entire ventilation system.

[0105] Taking the intelligent temperature control system as an example, the system performs time series analysis on indoor temperature data, identifying periodic change patterns such as temperature fluctuations during the day and night or seasonal changes. Based on these periodic changes, the system calculates a time decay coefficient that measures the influence of historical data on current temperature predictions. This decay coefficient allows the system to place more emphasis on recent temperature data, while the influence of distant historical data on predictions gradually decreases. The system applies a time decay function to the historical temperature data for weighted analysis, resulting in error sequences in temperature predictions. By analyzing these error sequences, the system can identify potential biases in the temperature control model and compensate for these errors using trend prediction methods. The system combines the compensated temperature prediction data with the actual temperature to obtain more accurate temperature control prediction data. This process ensures that the intelligent temperature control system can accurately adjust indoor temperature under various environmental changes, optimizing energy use and improving comfort and energy efficiency.

[0106] The roadway average air volume prediction method in the embodiments of the present application is described above, and the roadway average air volume prediction system in the embodiments of the present application is described below. Please refer to Figure 3 An embodiment of the roadway average air volume prediction system in the embodiments of the present application includes:

[0107] The acquisition module 201 is configured to collect and process the roadway cross-sectional area, the roadway length, and the roadway roughness coefficient through the multi-sensor network to obtain roadway resistance interval data.

[0108] The quantification module 202 is configured to quantitatively process the roadway air volume uncertainty according to the roadway resistance interval data to obtain a roadway air volume interval range.

[0109] The conversion module 203 is configured to perform triangular fuzzy number conversion processing on the roadway air volume interval range to obtain roadway air volume fuzzy distribution data.

[0110] The fusion module 204 is configured to perform fusion processing on the roadway air volume fuzzy distribution data through a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data.

[0111] The correction module 205 is configured to perform error correction processing on a roadway historical air volume sequence according to the roadway average air volume fusion data to obtain roadway average air volume prediction data.

[0112] Through the synergistic cooperation of the above-mentioned various components, the system can accurately obtain the key parameters of the roadway and accurately predict the air volume. The acquisition module collects the data such as the sectional area, length and roughness coefficient of the roadway in real time through a multi-sensor network, generates roadway resistance interval data, and provides a reliable basis for subsequent air volume prediction. The quantization module quantizes the uncertainty of the air volume according to the resistance interval data, obtains the interval range of the air volume, and comprehensively reflects the range of the air volume change and its uncertainty. The conversion module converts the air volume interval range into a triangular fuzzy number, accurately describes the probability distribution of the air volume through a fuzzy mathematical method, and avoids the limitation of air volume prediction to a single numerical value. The fusion module fuses the fuzzy distribution data of the air volume through a weighted average operator, combines the weight of the roadway, realizes the reasonable fusion of the air volume prediction results of different roadways, and finally obtains accurate roadway average air volume data. The correction module corrects the error by using historical air volume data, further optimizes the prediction result, and ensures the accuracy of the final air volume prediction data. Through the synergistic work of the above series of steps, the system realizes high-precision roadway air volume prediction and provides reliable data support for actual ventilation control.

[0113] The above Figure 3 The roadway average air volume prediction system in the embodiment of the application is described in detail from the perspective of modular functional entities, and the roadway average air volume prediction device in the embodiment of the application is described in detail from the perspective of hardware processing.

[0114] Referring Figure 4 , the embodiment of the application also provides a roadway average air volume prediction device, which can be a server, and the internal structure thereof can be as shown in Figure 4 . The roadway average air volume prediction device comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is used to provide computing and control capabilities. The memory of the roadway average air volume prediction device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the roadway average air volume prediction device is used to store the corresponding data in the embodiment. The network interface of the roadway average air volume prediction device is used to communicate with the external terminal through network connection. The computer program is executed by the processor to realize the above-mentioned method.

[0115] Those skilled in the art can understand Figure 4 that the structure shown in the embodiment is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the roadway average air volume prediction device to which the scheme of the application is applied.

[0116] The application further provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and the instructions make a computer execute the steps of the roadway average air volume prediction method when the instructions are run on the computer.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0118] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the application or the whole or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for making a roadway average air volume prediction device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0119] The above embodiments are only used to illustrate the technical solutions of the application, rather than limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method of predicting an average air quantity in a roadway, characterized by, The method includes: Data on roadway resistance range is obtained by collecting and processing data on roadway cross-sectional area, roadway length, and roadway roughness coefficient through a multi-sensor network. The uncertainty of the roadway air volume is quantified based on the roadway resistance range data to obtain the roadway air volume range. The tunnel air volume range is processed by triangular fuzzy number transformation to obtain fuzzy distribution data of tunnel air volume. The fuzzy distribution data of the roadway air volume is fused by the roadway weight-induced ordered weighted average operator to obtain the fused average air volume data of the roadway. The average airflow data of the roadways is fused to perform error correction processing on the historical airflow sequence of the roadways to obtain predicted average airflow data. This includes: performing time series analysis on the fused average airflow data of the roadways to obtain the periodic variation pattern of the roadway airflow; calculating the time decay coefficient based on the periodic variation pattern of the roadway airflow to obtain the roadway time weight decay function; performing weighted analysis on the historical airflow sequence of the roadways based on the roadway time weight decay function to obtain the historical error sequence of the roadways; performing trend prediction processing on the historical error sequence of the roadways to obtain the predicted error value of the roadways; and performing error compensation processing based on the fused average airflow data of the roadways and the predicted error value of the roadways to obtain the predicted average airflow data of the roadways.

2. The roadway average air volume prediction method according to claim 1, characterized by, The process of collecting and processing cross-sectional area, length, and roughness coefficient of the roadway through a multi-sensor network to obtain roadway resistance range data includes: The error range of the measured cross-sectional area of ​​the roadway is calculated to obtain the boundary values ​​of the roadway cross-sectional area interval. Based on the boundary values ​​of the tunnel cross-sectional area interval, the tunnel length measurement data is corrected to obtain the tunnel length correction parameters. By analyzing the variation range of the roadway roughness coefficient, the roadway roughness interval coefficient is obtained. The tunnel resistance values ​​are processed by interval calculation based on the tunnel cross-sectional area interval boundary value and the tunnel length correction parameter to obtain the lower limit value and upper limit value of the tunnel resistance. Based on the lower limit value and the upper limit value of the roadway resistance, interval number construction processing is performed to obtain the roadway resistance interval data.

3. The roadway average air volume prediction method according to claim 1, characterized by, The process of quantifying the uncertainty of roadway airflow based on the roadway resistance range data to obtain the roadway airflow range includes: The roadway resistance range data is input into the air volume continuity calculation for node balancing processing to obtain the roadway node air volume balance constraint conditions. Based on the air volume balance constraint of the roadway node, the roadway air pressure loss value is calculated in intervals to obtain the roadway air pressure loss interval value. Based on the aforementioned roadway wind pressure loss range, the roadway loop wind pressure is balanced and processed to obtain roadway loop balance data; The roadway loop balance data is iteratively solved to obtain the lower limit value and upper limit value of the roadway air volume. Based on the lower limit value and the upper limit value of the roadway air volume, an interval range is constructed to obtain the roadway air volume interval range.

4. The roadway average air volume prediction method according to claim 1, characterized by, The process of performing triangular fuzzy number transformation on the air volume range of the tunnel to obtain fuzzy distribution data of the air volume in the tunnel includes: Extracting and processing the lower limit value and the upper limit value of the air volume based on the air volume interval range of the roadway to obtain a roadway air volume boundary parameter; According to the roadway air volume boundary parameter, the historical air volume data of the roadway is statistically analyzed and processed to obtain a most possible value of the air volume of the roadway; The roadway air volume boundary parameter and the most possible value of the air volume of the roadway are processed by triangular fuzzy number construction to obtain a triangular fuzzy number of the air volume of the roadway; The triangular fuzzy number of the air volume of the roadway is calculated and processed by a membership function to obtain a membership degree distribution curve of the air volume of the roadway; Based on the membership degree distribution curve of the air volume of the roadway, fuzzy distribution data is assembled to obtain the fuzzy distribution data of the air volume of the roadway.

5. The roadway average air volume prediction method according to claim 2, characterized by, The roadway air volume fuzzy distribution data is fused by a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data, including: Based on the roadway air volume fuzzy distribution data, the importance of the cross-sectional area and the length of the roadway is calculated to obtain a roadway importance index; According to the roadway importance index, the weight of the roadway function coefficient is allocated to obtain a dynamic weight value of the roadway; The dynamic weight value of the roadway is input into a sorting algorithm for importance sorting to obtain a weight ordering sequence of the roadway; Based on the weight ordering sequence of the roadway, the roadway air volume fuzzy distribution data is weighted and fused to obtain a weighted result of the air volume of the roadway; According to the weighted result of the air volume of the roadway, the average value is calculated to obtain the average air volume fusion data of the roadway.

6. The roadway average air volume prediction method according to claim 5, characterized by, The importance of the cross-sectional area and the length of the roadway is calculated based on the roadway air volume fuzzy distribution data to obtain a roadway importance index, including: The cross-sectional area of the roadway air volume fuzzy distribution data is extracted to obtain a cross-sectional area value of the roadway; Based on the cross-sectional area value of the roadway, the ratio of the maximum cross-sectional area is calculated to obtain a cross-sectional area weight coefficient of the roadway; According to the length correction parameter of the roadway, the reciprocal of the reference length is calculated to obtain a length weight coefficient of the roadway; The cross-sectional area weight coefficient of the roadway and the length weight coefficient of the roadway are multiplied to obtain a basic importance value of the roadway; Based on the basic importance value of the roadway, the importance coefficient of the function of the roadway is weighted and multiplied to obtain the importance index of the roadway.

7. A roadway average air volume prediction system characterized by, The roadway average air volume prediction system for realizing the roadway average air volume prediction method in any one of claims 1-6, comprising: A collection module for collecting and processing the cross-sectional area, the length and the roughness coefficient of the roadway by a multi-sensor network to obtain roadway resistance interval data; A quantization module for quantizing the air volume uncertainty of the roadway based on the roadway resistance interval data to obtain an air volume interval range of the roadway; A conversion module for converting the air volume interval range of the roadway into a triangular fuzzy number to obtain a fuzzy distribution data of the air volume of the roadway; A fusion module for fusing the fuzzy distribution data of the air volume of the roadway by a roadway weight-induced ordered weighted averaging operator to obtain roadway average air volume fusion data. The correction module is configured to perform error correction processing on the historical air volume sequence of the roadway according to the roadway average air volume fusion data, and obtain roadway average air volume prediction data, including: performing time sequence analysis processing on the roadway average air volume fusion data to obtain a roadway air volume periodic change mode; performing calculation processing on a time attenuation coefficient based on the roadway air volume periodic change mode to obtain a roadway time weight attenuation function; performing weighted analysis processing on the historical air volume sequence of the roadway according to the roadway time weight attenuation function to obtain a roadway historical error sequence; performing trend prediction processing on the roadway historical error sequence to obtain a roadway error prediction value; and performing error compensation processing based on the roadway average air volume fusion data and the roadway error prediction value to obtain the roadway average air volume prediction data.

8. A device for predicting average air volume in roadways, characterized in that, The computer program is stored in the memory and executable by the processor, and the processor performs the computer program to realize the roadway average air volume prediction method in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is stored in the memory and executable by the processor, and the processor performs the computer program to realize the roadway average air volume prediction method in any one of claims 1 to 6.

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