Smart city building fire monitoring and early warning system based on multi-modal data

Abstract

The invention relates to the technical field of data processing, in particular to a smart city building fire monitoring and early warning system based on multi-modal data, and the system comprises a data collection module which is used for obtaining a temperature data sequence and a smoke data sequence in any building; the data analysis module is used for constructing a self-adaptive window according to data fluctuation in the smoke data sequence, and obtaining a trend value according to a data change trend in the self-adaptive window; the threshold acquisition module is used for obtaining a predicted value of a future moment according to the data in the adaptive window by using an exponential smoothing algorithm, and adjusting a preset initial early warning value according to a difference between an actual value and the predicted value of the future moment and a trend value to obtain an adaptive early warning value of the future moment; and the abnormity early warning module is used for carrying out fire early warning on the building according to the temperature data corresponding to each piece of smoke data in the adaptive window if the actual value at the future moment is greater than the adaptive early warning value, so that the accuracy of fire early warning on the building is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a smart city building fire monitoring and early warning system based on multimodal data. Background Art

[0002] With the acceleration of global urbanization, the density of buildings has gradually increased, resulting in a rising risk of fire. Fire has the characteristics of rapid spread and strong destructive power. If the fire cannot be detected in time and effectively controlled and handled, it is very easy to cause heavy casualties and property losses. Therefore, in the construction of smart cities, how to efficiently and accurately monitor and warn of building fires has become an important part of urban safety management.

[0003] Since the exponential smoothing algorithm has strong accuracy under stable data, in the traditional way, in the process of monitoring building fires, a fixed warning value is set. By installing smoke density and temperature sensors indoors, the exponential smoothing algorithm is used to predict the future value of the relevant data collected by the sensors to determine whether the future value exceeds the warning value. If it exceeds the warning threshold, an alarm is issued and personnel are notified to flee the scene in time. However, due to non-fire factors such as smoking or cooking, activities will produce characteristic data similar to the initial stage of a fire, such as a rapid increase in smoke density in a short period of time, or smoke density reaching the warning threshold, etc., and the fixed warning value cannot effectively identify these anomalies caused by non-fire factors. At the same time, there may be large errors in the prediction of future values, resulting in the inability to accurately warn of fires.

[0004] Therefore, how to improve the accuracy of building fire warning has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a smart city building fire monitoring and early warning system based on multimodal data to solve the problem of how to improve the accuracy of building fire early warning.

[0006] An embodiment of the present invention provides a smart city building fire monitoring and early warning system based on multimodal data, the system comprising the following steps: A data collection module is used to collect temperature data and smoke data at any location in a historical period in any building, obtain corresponding temperature data sequences and smoke data sequences, and construct an initial window of a preset size by taking the last smoke data in the smoke data sequence as the last data point in the window; A data analysis module, configured to adjust the length of the initial window according to data fluctuations in the smoke data sequence, obtain an adaptive window containing the last smoke data in the smoke data sequence, and obtain a trend value of data change in the adaptive window according to a data change trend in the adaptive window; A threshold acquisition module, used to obtain a predicted value at a future time according to the smoke data in the adaptive window by using an exponential smoothing algorithm, obtain actual smoke data at a future time, and adjust a preset initial warning value according to the difference between the actual smoke data and the predicted value and the trend value to obtain an adaptive warning value at a future time; The abnormal warning module is used to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence if the actual smoke data at the future moment is greater than the adaptive warning value, and obtain a temperature subsequence; according to the first-order temperature difference sequence of the temperature subsequence, a fire warning is issued for any building.

[0007] Furthermore, the data analysis module adjusts the length of the initial window according to the data fluctuation in the smoke data sequence to obtain an adaptive window containing the last smoke data in the smoke data sequence, including: Performing a first-order difference operation on all smoke data in the initial window to obtain a first-order difference sequence, calculating a standard deviation of the first-order difference sequence, and obtaining a noise coefficient of the initial window; If the noise coefficient is greater than or equal to a preset noise coefficient threshold, then in the smoke data sequence, the length of the initial window is expanded by a first preset multiple to obtain a target length, if the target length is an even number, the target length is rounded up to obtain the length of the adaptive window, if the target length is an odd number, the target length is used as the length of the adaptive window; If the noise coefficient is less than the preset noise coefficient threshold, the length of the initial window is reduced according to the data fluctuation trend in the initial window to obtain an adaptive window containing the last smoke data in the smoke data sequence.

[0008] Furthermore, the data analysis module reduces the length of the initial window according to the data fluctuation trend in the initial window to obtain an adaptive window containing the last smoke data in the smoke data sequence, including: According to all the data in the initial window, the average absolute deviation of the initial window is calculated, and the reduction coefficient of the initial window is obtained by subtracting the average absolute deviation of the second preset multiple from the constant 1. The product of the length of the initial window and the reduction coefficient is used as the target length, and the target length is rounded up and rounded down respectively, and the odd number item in the rounding result is selected as the length of the adaptive window.

[0009] Furthermore, the data analysis module obtains a trend value of data change in the adaptive window according to the data change trend in the adaptive window, including: In the smoke data sequence, a preset number of smoke data before the last smoke data in the adaptive window and the last smoke data in the adaptive window are combined into a smoke subsequence, a first-order difference sequence of the smoke subsequence is obtained, and the absolute value of the average value of all difference values ​​in the first-order difference sequence of the smoke subsequence is calculated to obtain a first change trend indicator of the data change in the adaptive window, wherein the number of all data in the smoke subsequence is the product of the number of all data in the initial window and a preset time span coefficient; According to the difference between every two adjacent smoke data in the adaptive window, a second change trend indicator of the data change in the adaptive window is obtained; Obtaining an average smoke value of all smoke data in the adaptive window, and obtaining a third change trend indicator of data change in the adaptive window according to a difference between each smoke data in the adaptive window and the average smoke value; The first change trend indicator, the second change trend indicator and the third change trend indicator of the data change in the adaptive window are weightedly summed to obtain the trend value of the data change in the adaptive window.

[0010] Furthermore, the data analysis module obtains a second change trend indicator of data change in the adaptive window according to the difference between every two adjacent smoke data in the adaptive window, including: In the adaptive window, for any two adjacent smoke data, the absolute value of the difference between the two adjacent smoke data is calculated to obtain a smoke difference index between the two adjacent smoke data, and the product of the smoke difference index and a preset exponential decay coefficient is used as a local fluctuation index between the two adjacent smoke data; A local fluctuation index between every two adjacent smoke data in the adaptive window is obtained, and an average value of all local fluctuation indexes is calculated to obtain a second change trend index of the data change in the adaptive window.

[0011] Furthermore, the data analysis module obtains a third change trend indicator of the data change in the adaptive window according to the difference between each smoke data in the adaptive window and the average smoke value, including: Taking each smoke data in the adaptive window as target data, obtaining the middle position of the adaptive window, calculating the difference between the position of each target data in the adaptive window and the middle position, obtaining the position coefficient of each target data, accumulating the squares of all position coefficients to obtain the position coefficient accumulation value, calculating the ratio between the position coefficient of each target data and the position coefficient accumulation value, and obtaining the position weight of each target data; For any target data, the difference between the target data and the average smoke value is calculated to obtain the deviation of the target data; in the adaptive window, according to the position of any target data, symmetric data of the target data about the middle position is obtained, and the product of the position weight of the symmetric data and the deviation of the target data is calculated to obtain the weighted deviation of the target data; The weighted deviations of all target data are accumulated to obtain a third change trend indicator of the data change in the adaptive window.

[0012] Furthermore, the threshold acquisition module adjusts the preset initial warning value according to the difference between the actual smoke data and the predicted value, and the trend value, to obtain an adaptive warning value at a future moment, including: Calculate the absolute value of the difference between the actual smoke data and the predicted value. If the absolute value of the difference is greater than the trend value, calculate the product of the first preset warning adjustment coefficient and the absolute value of the difference, and use the sum of the product and the preset initial warning value as the adaptive warning value at the future moment. If the absolute value of the difference is less than or equal to the trend value, the product of the second preset warning adjustment coefficient and the absolute value of the difference is calculated, and the sum of the product and the preset initial warning value is used as the adaptive warning value at the future moment.

[0013] Furthermore, the abnormal warning module performs a fire warning for any of the buildings according to the first-order temperature difference sequence of the temperature subsequence, including: Calculating the absolute value of the average value of all difference values ​​in the first-order difference sequence of the temperature subsequence to obtain the temperature change index of the temperature subsequence; If the temperature change index is greater than a preset temperature change index threshold, a fire warning is issued for any of the buildings.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention provides a smart city building fire monitoring and early warning system based on multimodal data, including a data acquisition module, which is used to respectively collect temperature data and smoke data at any location in a historical period in any building, obtain corresponding temperature data sequences and smoke data sequences, and construct an initial window of a preset size with the last smoke data in the smoke data sequence as the last data point in the window; a data analysis module, which is used to adjust the length of the initial window according to data fluctuations in the smoke data sequence, obtain an adaptive window containing the last smoke data in the smoke data sequence, and obtain the adaptive window according to the data change trend in the adaptive window. The present invention relates to a trend value of data change; a threshold acquisition module, which is used to obtain a predicted value at a future moment according to the smoke data in the adaptive window by using an exponential smoothing algorithm, obtain the actual smoke data at the future moment, and adjust the preset initial warning value according to the difference between the actual smoke data and the predicted value, as well as the trend value, to obtain an adaptive warning value at a future moment; an abnormal warning module, which is used to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence if the actual smoke data at the future moment is greater than the adaptive warning value, obtain a temperature subsequence, and issue a fire warning to any of the buildings according to the first-order temperature difference sequence of the temperature subsequence. Among them, an adaptive window is obtained according to the data change trend in the smoke data sequence to accurately capture data fluctuations in a short period of time, and at the same time, fire-like features caused by smoking can be smoothed out; further, an exponential smoothing algorithm is used to obtain the predicted value of the future moment according to the data in the adaptive window, and the initial warning value is adjusted according to the difference between the smoothed value and the actual value to obtain an adaptive warning value. Considering that some non-fire factors may also cause the smoke data to be too high, when the actual value exceeds the adaptive warning value, the change trend of the temperature data corresponding to each smoke data in the adaptive window is further analyzed, and a fire warning is issued for any building according to the change trend of the temperature data, so as to reduce the interference of non-fire factors such as smoking and cooking on the fire warning, and achieve a more comprehensive and accurate fire warning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 It is a structural block diagram of a smart city building fire monitoring and early warning system based on multimodal data provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0018] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is a structural block diagram of a smart city building fire monitoring and early warning system based on multimodal data provided by the first embodiment of the present invention, such as Figure 1 As shown, the system may include: The data acquisition module 11 is used to collect temperature data and smoke data at any location in a historical period in any building, obtain corresponding temperature data sequences and smoke data sequences, and use the last smoke data in the smoke data sequence as the last data point in the window to construct an initial window of a preset size.

[0021] In any building, through smoke sensors and temperature sensors at any location, temperature data and smoke data within 60 seconds are collected, and the sampling frequency is once per second. There is no restriction here. The implementer can obtain the corresponding temperature data sequence and smoke data sequence according to the specific scene settings, where the last data in the data sequence is the data at the current moment. In order to ensure the accuracy and consistency of each data in the data sequence, after all data sequences are obtained, each data in all data sequences is preprocessed to achieve the purpose of data cleaning for all data sequences, where data preprocessing includes but is not limited to: removing noise and outliers to ensure data accuracy. For missing data points, the mean can be used to fill them; the data is normalized so that variables in different ranges have consistent dimensions to facilitate subsequent analysis and processing. Among them, data preprocessing technology belongs to the prior art and will not be repeated here.

[0022] The data analysis module 12 is used to adjust the length of the initial window according to the data fluctuation in the smoke data sequence, obtain an adaptive window containing the last smoke data in the smoke data sequence, and obtain a trend value of the data change in the adaptive window according to the data change trend in the adaptive window.

[0023] When a fire occurs, since smoke sensors have a strong sense of smoke emitted when combustibles burn, smoke concentration is used as the main feature of fire monitoring, and the exponential smoothing algorithm is used to predict smoke data at future times. However, in the process of using the exponential smoothing algorithm to predict smoke data at future times, since the smoke concentration tends to rise sharply at the beginning of a fire, it is first necessary to dynamically adjust the initial window size according to the changing trend of the smoke data to obtain an adaptive window, so as to accurately capture the smoke fluctuations in a short period of time and improve the accuracy of using the exponential smoothing algorithm to predict smoke data at future times. The steps to obtain the adaptive window are: Performing a first-order difference operation on all smoke data in the initial window to obtain a first-order difference sequence, calculating a standard deviation of the first-order difference sequence, and obtaining a noise coefficient of the initial window; If the noise coefficient is greater than or equal to a preset noise coefficient threshold, then in the smoke data sequence, the length of the initial window is expanded by a first preset multiple to obtain a target length, if the target length is an even number, the target length is rounded up to obtain the length of the adaptive window, if the target length is an odd number, the target length is used as the length of the adaptive window; If the noise coefficient is less than the preset noise coefficient threshold, the length of the initial window is reduced according to the data fluctuation trend in the initial window to obtain an adaptive window containing the last smoke data in the smoke data sequence.

[0024] In one embodiment, the size of the initial window is set to 10, which is not limited here and can be set by the implementer according to the implementation scenario. An initial window of size 10 is constructed in the smoke data sequence, that is, the initial window contains 10 smoke data, wherein the last data point in the initial window is the last smoke data in the smoke data sequence, that is, the smoke data at the current moment. Considering that there may be a lot of noise in the smoke data in the initial window, firstly, a first-order difference operation is performed on all the data in the initial window to obtain a first-order difference sequence, and the noise coefficient of the initial window is obtained according to the first-order difference sequence, which is used to reflect the amount of noise contained in the initial window. The calculation formula of the noise coefficient of the initial window is: in, represents the noise coefficient of the initial window, represents the number of all data in the initial window, represents the i-th smoke data in the initial window, represents the i-1th smoke data in the initial window, Represents the mean of all difference values ​​in the first-order difference sequence of all smoke data within the initial window.

[0025] It should be noted that The larger the value of is, the greater the difference between the difference value and the average value in the first-order difference sequence of all smoke data in the initial window is, and the greater the fluctuation of the smoke data in the initial window is. The larger the value of , the greater the possibility that the smoke data in the initial window is abnormal.

[0026] Set the preset noise coefficient threshold to 0.3. There is no limit here. The implementer can set it according to the specific implementation scenario. If the noise coefficient of the initial window is greater than or equal to 0.3, it is considered that the noise value in the initial window is large. At this time, it is necessary to expand the length of the initial window in the smoke data sequence to increase the amount of data. Set the first preset multiple to 2. There is no limit here. The implementer can set it according to the specific implementation scenario. If the noise coefficient of the initial window is greater than or equal to 0.3, the length of the initial window is expanded to , that is, the target length is ,because is an even number, so Round up to an odd number, ,Will As the length of the adaptive window.

[0027] If the noise coefficient of the initial window is less than 0.3, it is considered that the noise value in the initial window is small. At this time, it is necessary to reduce the length of the initial window in the smoke data sequence according to the change amplitude of the data in the initial window to obtain an adaptive window, so as to reduce the amount of data and capture the details of data changes. The larger the change amplitude in the initial window, the greater the reduction of the initial window, so that it can capture short-term data changes more frequently. The steps to reduce the length of the initial window to obtain an adaptive window are: According to all the data in the initial window, the average absolute deviation of the initial window is calculated, and the reduction coefficient of the initial window is obtained by subtracting the average absolute deviation of the second preset multiple from the constant 1. The product of the length of the initial window and the reduction coefficient is used as the target length, and the target length is rounded up and rounded down respectively, and the odd number item in the rounding result is selected as the length of the adaptive window.

[0028] In one embodiment, the calculation formula for obtaining the target length after reducing the length of the initial window is: in, Indicates the target length, that is, the number of all smoke data in the adaptive window. represents the length of the initial window, Indicates the second preset multiple, represents the i-th smoke data in the initial window, represents the average value of all smoke data in the initial window, 1 represents a constant, Represents the absolute value symbol.

[0029] It should be noted that the second preset multiple is set to 0.1, which is not limited here and can be set by the implementer according to the specific implementation scenario. is the mean absolute deviation of the initial window, This is the reduction factor of the initial window, and the result range is The larger the mean absolute deviation, the greater the change in the data in the initial window. At this time, the greater the reduction of the initial window, and the smaller the reduction coefficient. The smaller the value of is, the smaller the length of the initial window after it is reduced, that is, the greater the reduction degree of the initial window.

[0030] Since the calculated value of W is likely to be a decimal, the value of W is rounded up and rounded down respectively, and an odd number item in the rounding result is selected as the length of the adaptive window.

[0031] So far, an adaptive window including the last smoke data in the smoke data sequence is obtained. The number of all smoke data in the adaptive window is an odd number. The data in the adaptive window can reflect the changing trend of the smoke data in a short time.

[0032] In the process of predicting the data at future moments using the exponential smoothing algorithm based on the data in the adaptive window, it is considered that a fire may occur if the smoke is not completely generated. For example, if someone smokes near the smoke sensor or someone cooks near the smoke sensor, the smoke concentration will rise rapidly in a short period of time, producing smoke similar to that of a fire, and then reaching the warning threshold, resulting in a misjudgment of the fire. Therefore, the predicted value at future moments obtained by the exponential smoothing algorithm cannot fully reflect the trend of the fire. It is necessary to use the last smoke data in the adaptive window (the smoke data at the current moment) as a benchmark, analyze the long-term change trend of the smoke data at the current moment in the smoke data sequence, and obtain the trend value of the data change in the adaptive window by comprehensively analyzing the long-term change trend and short-term change trend of the smoke data. This is used to characterize the degree of change of the smoke data at the current moment, so as to reduce the possibility of misjudgment due to local extreme values ​​or abnormal values, and adaptively adjust the warning value of the fire at future moments according to the trend value. The steps for obtaining the trend value of the data change in the adaptive window are as follows: (1) In the smoke data sequence, a first change trend indicator of the data change in the adaptive window is obtained according to the data change trend of a preset number of smoke data before the last smoke data in the adaptive window.

[0033] In the smoke data sequence, a preset number of smoke data before the last smoke data in the adaptive window and the last smoke data in the adaptive window are combined into a smoke subsequence, a first-order difference sequence of the smoke subsequence is obtained, and the absolute value of the average value of all differential values ​​in the first-order difference sequence of the smoke subsequence is calculated to obtain a first change trend indicator of the data change in the adaptive window, wherein the number of all data in the smoke subsequence is the product of the number of all data in the initial window and a preset time span coefficient.

[0034] In one embodiment, assuming that the last smoke data in the adaptive window is the kth smoke data in the smoke data sequence, the preset time span coefficient is set to 3, which is not limited here and can be set by the implementer according to the specific scenario. Then the number of all data in the smoke subsequence is , that is, in the smoke data sequence, the 29 smoke data before the kth smoke data and the kth smoke data form a smoke subsequence, and the first change trend index is obtained according to the first-order difference sequence of the smoke subsequence, which is used to characterize the change trend of the smoke data at the current moment in a long period of time. The calculation formula of the first change trend index is: in, Represents the first change trend indicator, Represents the nth smoke data in the smoke subsequence, Represents the n-1th smoke data in the smoke subsequence, Represents the number of all data in the smoke subsequence, Represents the absolute value symbol.

[0035] It should be noted that The larger the value, the greater the fluctuation of smoke data in the long term. The larger the value, the greater the trend value of data changes in the adaptive window.

[0036] (2) According to the difference between every two adjacent smoke data in the adaptive window, a second change trend indicator of the data change in the adaptive window is obtained.

[0037] In the adaptive window, for any two adjacent smoke data, the absolute value of the difference between the two adjacent smoke data is calculated to obtain a smoke difference index between the two adjacent smoke data, and the product of the smoke difference index and a preset exponential decay coefficient is used as a local fluctuation index between the two adjacent smoke data; A local fluctuation index between every two adjacent smoke data in the adaptive window is obtained, and an average value of all local fluctuation indexes is calculated to obtain a second change trend index of the data change in the adaptive window.

[0038] In one embodiment, assuming that the last smoke data in the adaptive window is the t-th smoke data in the adaptive window, a second change trend index is obtained according to the difference between every two adjacent smoke data in the adaptive window, which is used to characterize the change trend of the smoke data at the current moment in a short time. The calculation formula of the second change trend index is: in, Represents the second change trend indicator, represents the number of all smoke data in the adaptive window, j represents the position of each smoke data in the adaptive window, Indicates the first Smoke data, Indicates the first Smoke data, Represents the preset exponential decay coefficient, Represents the absolute value symbol.

[0039] It should be noted that It is the smoke difference index between two adjacent smoke data in the adaptive window. It is the local fluctuation index between two adjacent smoke data in the adaptive window. The value of is 0.9, which is not limited here. The implementer can implement it according to the specific scenario. The smaller the value of j is, the smaller the value of Smoke data and The closer the distance between the tth smoke data and the tth smoke data (the smoke data at the current moment), the The value of is given a larger weight, The larger the value of , the more sensitive it is to capture the changing trend of smoke data. The larger the smoke difference index, the greater the fluctuation of smoke data in the adaptive window, and thus the larger the local fluctuation index. The larger the value is, the greater the short-term data change trend of the smoke data at the current moment is, and the greater the trend value of the data change in the adaptive window is.

[0040] (3) According to the difference between each smoke data in the adaptive window and the average smoke value, a third change trend indicator of the data change in the adaptive window is obtained.

[0041] Taking each smoke data in the adaptive window as target data, obtaining the middle position of the adaptive window, calculating the difference between the position of each target data in the adaptive window and the middle position, obtaining the position coefficient of each target data, accumulating the squares of all position coefficients to obtain the position coefficient accumulation value, calculating the ratio between the position coefficient of each target data and the position coefficient accumulation value, and obtaining the position weight of each target data; The average smoke value of all smoke data in the adaptive window is obtained, and for any target data, the difference between the target data and the average smoke value is calculated to obtain the deviation of the target data; in the adaptive window, according to the position of any target data, symmetric data of the target data about the middle position is obtained, and the product of the position weight of the symmetric data and the deviation of the target data is calculated to obtain the weighted deviation of the target data; The weighted deviations of all target data are accumulated to obtain a third change trend indicator of the data change in the adaptive window.

[0042] In one embodiment, taking into account the presence of noise data in the adaptive window, in order to prevent the noise data from being amplified during the calculation process and avoid unnecessary interference, the average value of all smoke data in the adaptive window is calculated and recorded as the average smoke value. The difference between each smoke data in the adaptive window and the average smoke value is used to reduce unnecessary interference caused by noise points.

[0043] At the same time, in order to avoid excessive deviation of the results caused by the data at one end of the adaptive window, the middle position of the adaptive window is obtained, and the position weight of each smoke data (target data) is obtained according to the difference between the position of the smoke data (target data) in the adaptive window and the middle position. The closer the noise data (target data) is to the middle position, the larger its corresponding position weight is, so as to amplify the influence of the center position.

[0044] Therefore, according to the difference between each smoke data and the average smoke value in the adaptive window, and the position of each smoke data in the adaptive window, the third change trend index is obtained to characterize the change trend of the smoke data at the current moment in a short time. The calculation formula of the third change trend index is: in, Indicates the third trend indicator, Represents the number of all smoke data in the adaptive window. Indicates the first Smoke data, represents the average smoke value, j represents the position of each smoke data in the adaptive window, Indicates the round-up symbol.

[0045] It should be noted that That is the middle position of the adaptive window. That is, the position coefficient of the jth smoke data (target data) in the adaptive window, That is the accumulated value of the position coefficient, is the position weight of the jth smoke data (target data) in the adaptive window, The symmetric data of the smoke data about the middle position is the jth smoke data, The closer the jth smoke data is to the middle position, the greater its position weight is; The larger the value of The greater the difference between individual smoke data and the average smoke value, the greater the fluctuation of smoke data within the adaptive window. The larger the value is, the greater the short-term data change trend of the smoke data at the current moment is, and the greater the trend value of the data change in the adaptive window is.

[0046] (4) Combining the first change trend indicator, the second change trend indicator, and the third change trend indicator of the data change in the adaptive window, a trend value of the data change in the adaptive window is obtained.

[0047] Specifically, a first change trend indicator, a second change trend indicator and a third change trend indicator of the data change in the adaptive window are weightedly summed to obtain a trend value of the data change in the adaptive window.

[0048] In one embodiment, the first change trend index obtained in the above steps is used to characterize the long-term change trend of the data, and the second change trend index and the third change trend index are used to characterize the short-term change trend of the data. The long-term trend of the data is combined with the short-term trend, so that the result can reflect both short-term changes and long-term trends. Then the calculation formula for the trend value of the data change in the adaptive window is: in, Indicates the trend value of data changes in the adaptive window. Represents the first change trend indicator, Represents the second change trend indicator, Indicates the third trend indicator, represents the first weight coefficient, represents the second weight coefficient, Represents the third weight coefficient.

[0049] It should be noted that 6. , ,There is no restriction here, and the implementer can set it according to the specific implementation scenario. The larger the value, the greater the fluctuation of smoke data in the long term. The larger the value is, the greater the trend value of data change in the adaptive window is; The larger the value, the greater the fluctuation of smoke data in the short term. The larger the value is, the greater the trend value of data change in the adaptive window is; The larger the value, the greater the short-term data change trend of the current smoke data. The larger the value is, the greater the trend value of data change in the adaptive window is.

[0050] At this point, the trend value of the data change in the adaptive window is obtained.

[0051] The threshold acquisition module 13 is used to use an exponential smoothing algorithm to obtain a predicted value at a future moment based on the smoke data in the adaptive window, obtain the actual smoke data at the future moment, and adjust the preset initial warning value based on the difference between the actual smoke data and the predicted value, as well as the trend value, to obtain an adaptive warning value at the future moment.

[0052] Using the exponential smoothing algorithm, the predicted value for the future moment is obtained based on the smoke data in the adaptive window. The exponential smoothing algorithm is a prior art and will not be described here. Due to the influence of non-fire factors, the predicted value cannot accurately reflect the smoke data at the future moment, that is, there is a difference between the predicted value and the actual value at the current moment, so it is necessary to compare the difference between the predicted value and the actual value at the current moment with the trend value, and adjust the fixed warning threshold according to the comparison result to obtain the adaptive warning value at the future moment, making it more accurate and reasonable. The specific method of obtaining the adaptive warning value at the future moment is: Calculate the absolute value of the difference between the actual smoke data and the predicted value. If the absolute value of the difference is greater than the trend value, calculate the product of the first preset warning adjustment coefficient and the absolute value of the difference, and use the sum of the product and the preset initial warning value as the adaptive warning value at the future moment. If the absolute value of the difference is less than or equal to the trend value, the product of the second preset warning adjustment coefficient and the absolute value of the difference is calculated, and the sum of the product and the preset initial warning value is used as the adaptive warning value at the future moment.

[0053] In one implementation, the first preset warning adjustment coefficient is set to 0.1, the second preset warning adjustment coefficient is -0.1, and the preset initial warning value is 0.3. There is no limitation here, and the implementer can set it according to the specific scenario. Then the calculation formula of the adaptive warning value is: in, represents the adaptive warning value at the future moment, Indicates the preset initial warning value. If ,but ,like ,but , represents the actual smoke data at the future moment, represents the predicted value of smoke data at a future time. Represents the absolute value symbol.

[0054] It should be noted that if , indicating that the actual smoke data at the future moment fluctuates violently within its local range, rather than showing a stable upward trend. This may be affected by short-term noise or accidental events (such as smoking or cooking). In this case, the initial warning value needs to be increased, i.e. , to avoid premature alarms; if , it means that the actual smoke data at the future moment changes steadily within its local range and is on an upward trend overall. This situation usually indicates that there may be a potential fire. At this time, the initial warning value needs to be reduced, that is, , to enhance sensitivity and facilitate early detection of fire trends.

[0055] The abnormal warning module 14 is used to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence if the actual smoke data at the future moment is greater than the adaptive warning value, and obtain a temperature subsequence, and issue a fire warning for any of the buildings according to the first-order temperature difference sequence of the temperature subsequence.

[0056] If the actual smoke data at a future moment is greater than the adaptive warning value, it means that a fire may have occurred at this time. However, considering special situations such as someone smoking next to the smoke sensor or someone cooking near the smoke sensor, these special situations will cause the smoke data to fluctuate in the short term and show an upward trend. At the same time, the temperature data will only rise in the short term, and the increase will be small, and then return to normal, or the temperature data will not change. However, if a fire occurs, its temperature data will grow slowly in the early stage of the fire, and may rise sharply in the middle or late stage of the fire. Therefore, it is necessary to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence, obtain the temperature subsequence, and characterize the change trend of the temperature data in the temperature subsequence through the first-order difference sequence of the temperature subsequence to obtain the temperature change index. According to the temperature change index, the fire condition of the building is warned. The specific method of obtaining the temperature change index is as follows: The absolute value of the average value of all difference values ​​in the first-order difference sequence of the temperature subsequence is calculated to obtain the temperature change index of the temperature subsequence.

[0057] In one embodiment, assuming that the last temperature data in the temperature subsequence is the u-th temperature data in the temperature subsequence, the calculation formula of the temperature change index is: Among them, Z represents the temperature change index, M represents the number of all data in the temperature subsequence, Indicates the temperature subsequence Temperature data, Indicates the temperature subsequence Temperature data, Represents the absolute value symbol.

[0058] It should be noted that The larger the value of , the greater the fluctuation of temperature data within a period of time, and the larger the value of Z, the greater the possibility of fire.

[0059] Set the preset temperature change index threshold to 0.1. There is no limit here. The implementer can set it according to the specific scenario. , it is considered that the possibility of fire is high, and a fire warning is needed for the building.

[0060] In summary, the present invention provides a smart city building fire monitoring and early warning system based on multimodal data, including a data acquisition module, which is used to collect temperature data and smoke data at any location in a historical period in any building, respectively, to obtain corresponding temperature data sequences and smoke data sequences, and to construct an initial window of a preset size with the last smoke data in the smoke data sequence as the last data point in the window; a data analysis module, which is used to adjust the length of the initial window according to the data fluctuation in the smoke data sequence, to obtain an adaptive window containing the last smoke data in the smoke data sequence, and to obtain the adaptive window according to the data change trend in the adaptive window. The present invention relates to a trend value of data changes in the window; a threshold acquisition module, which is used to obtain a predicted value at a future moment according to the smoke data in the adaptive window by using an exponential smoothing algorithm, obtain the actual smoke data at the future moment, and adjust the preset initial warning value according to the difference between the actual smoke data and the predicted value, as well as the trend value, to obtain an adaptive warning value at a future moment; an abnormal warning module, which is used to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence if the actual smoke data at the future moment is greater than the adaptive warning value, obtain a temperature subsequence, and issue a fire warning to any of the buildings according to the first-order temperature difference sequence of the temperature subsequence. Among them, an adaptive window is obtained according to the data change trend in the smoke data sequence to accurately capture data fluctuations in a short period of time, and at the same time, fire-like features caused by smoking can be smoothed out; further, an exponential smoothing algorithm is used to obtain the predicted value of the future moment according to the data in the adaptive window, and the initial warning value is adjusted according to the difference between the smoothed value and the actual value to obtain an adaptive warning value. Considering that some non-fire factors may also cause the smoke data to be too high, when the actual value exceeds the adaptive warning value, the change trend of the temperature data corresponding to each smoke data in the adaptive window is further analyzed, and a fire warning is issued for any building according to the change trend of the temperature data, so as to reduce the interference of non-fire factors such as smoking and cooking on the fire warning, and achieve a more comprehensive and accurate fire warning effect.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A smart city building fire monitoring and early warning system based on multimodal data, characterized in that: The smart city building fire monitoring and early warning system based on multimodal data includes: A data collection module is used to collect temperature data and smoke data at any location in a historical period in any building, obtain corresponding temperature data sequences and smoke data sequences, and construct an initial window of a preset size by taking the last smoke data in the smoke data sequence as the last data point in the window; A data analysis module, configured to adjust the length of the initial window according to data fluctuations in the smoke data sequence, obtain an adaptive window containing the last smoke data in the smoke data sequence, and obtain a trend value of data change in the adaptive window according to a data change trend in the adaptive window; A threshold acquisition module, used to obtain a predicted value at a future time according to the smoke data in the adaptive window by using an exponential smoothing algorithm, obtain actual smoke data at a future time, and adjust a preset initial warning value according to the difference between the actual smoke data and the predicted value and the trend value to obtain an adaptive warning value at a future time; The abnormal warning module is used to obtain the temperature data corresponding to each smoke data in the adaptive window in the temperature data sequence if the actual smoke data at the future moment is greater than the adaptive warning value, and obtain a temperature subsequence; according to the first-order temperature difference sequence of the temperature subsequence, a fire warning is issued for any building.

2. The smart city building fire monitoring and early warning system based on multimodal data according to claim 1 is characterized in that: The data analysis module adjusts the length of the initial window according to the data fluctuation in the smoke data sequence to obtain an adaptive window containing the last smoke data in the smoke data sequence, including: Performing a first-order difference operation on all smoke data in the initial window to obtain a first-order difference sequence, calculating a standard deviation of the first-order difference sequence, and obtaining a noise coefficient of the initial window; If the noise coefficient is greater than or equal to a preset noise coefficient threshold, then in the smoke data sequence, the length of the initial window is expanded by a first preset multiple to obtain a target length, if the target length is an even number, the target length is rounded up to obtain the length of the adaptive window, if the target length is an odd number, the target length is used as the length of the adaptive window; If the noise coefficient is less than the preset noise coefficient threshold, the length of the initial window is reduced according to the data fluctuation trend in the initial window to obtain an adaptive window containing the last smoke data in the smoke data sequence.

3. The smart city building fire monitoring and early warning system based on multimodal data according to claim 2 is characterized in that: The data analysis module reduces the length of the initial window according to the data fluctuation trend in the initial window to obtain an adaptive window containing the last smoke data in the smoke data sequence, including: According to all the data in the initial window, the average absolute deviation of the initial window is calculated, and the reduction coefficient of the initial window is obtained by subtracting the average absolute deviation of the second preset multiple from the constant 1. The product of the length of the initial window and the reduction coefficient is used as the target length, and the target length is rounded up and rounded down respectively, and the odd number item in the rounding result is selected as the length of the adaptive window.

4. The smart city building fire monitoring and early warning system based on multimodal data according to claim 2 is characterized in that: The data analysis module obtains a trend value of data change in the adaptive window according to the data change trend in the adaptive window, including: In the smoke data sequence, a preset number of smoke data before the last smoke data in the adaptive window and the last smoke data in the adaptive window are combined into a smoke subsequence, a first-order difference sequence of the smoke subsequence is obtained, and the absolute value of the average value of all difference values ​​in the first-order difference sequence of the smoke subsequence is calculated to obtain a first change trend indicator of the data change in the adaptive window, wherein the number of all data in the smoke subsequence is the product of the number of all data in the initial window and a preset time span coefficient; According to the difference between every two adjacent smoke data in the adaptive window, a second change trend indicator of the data change in the adaptive window is obtained; Obtaining an average smoke value of all smoke data in the adaptive window, and obtaining a third change trend indicator of data change in the adaptive window according to a difference between each smoke data in the adaptive window and the average smoke value; The first change trend indicator, the second change trend indicator and the third change trend indicator of the data change in the adaptive window are weightedly summed to obtain the trend value of the data change in the adaptive window.

5. The smart city building fire monitoring and early warning system based on multimodal data according to claim 4 is characterized in that: The data analysis module obtains a second change trend indicator of data change in the adaptive window according to the difference between every two adjacent smoke data in the adaptive window, including: In the adaptive window, for any two adjacent smoke data, the absolute value of the difference between the two adjacent smoke data is calculated to obtain a smoke difference index between the two adjacent smoke data, and the product of the smoke difference index and a preset exponential decay coefficient is used as a local fluctuation index between the two adjacent smoke data; A local fluctuation index between every two adjacent smoke data in the adaptive window is obtained, and an average value of all local fluctuation indexes is calculated to obtain a second change trend index of the data change in the adaptive window.

6. The smart city building fire monitoring and early warning system based on multimodal data according to claim 4 is characterized in that: The data analysis module obtains a third change trend indicator of data change in the adaptive window according to the difference between each smoke data in the adaptive window and the average smoke value, including: Taking each smoke data in the adaptive window as target data, obtaining the middle position of the adaptive window, calculating the difference between the position of each target data in the adaptive window and the middle position, obtaining the position coefficient of each target data, accumulating the squares of all position coefficients to obtain the position coefficient accumulation value, calculating the ratio between the position coefficient of each target data and the position coefficient accumulation value, and obtaining the position weight of each target data; For any target data, the difference between the target data and the average smoke value is calculated to obtain the deviation of the target data; in the adaptive window, according to the position of any target data, symmetric data of the target data about the middle position is obtained, and the product of the position weight of the symmetric data and the deviation of the target data is calculated to obtain the weighted deviation of the target data; The weighted deviations of all target data are accumulated to obtain a third change trend indicator of the data change in the adaptive window.

7. The smart city building fire monitoring and early warning system based on multimodal data according to claim 1 is characterized in that: The threshold acquisition module adjusts the preset initial warning value according to the difference between the actual smoke data and the predicted value, and the trend value, to obtain an adaptive warning value at a future moment, including: Calculate the absolute value of the difference between the actual smoke data and the predicted value. If the absolute value of the difference is greater than the trend value, calculate the product of the first preset warning adjustment coefficient and the absolute value of the difference, and use the sum of the product and the preset initial warning value as the adaptive warning value at the future moment. If the absolute value of the difference is less than or equal to the trend value, the product of the second preset warning adjustment coefficient and the absolute value of the difference is calculated, and the sum of the product and the preset initial warning value is used as the adaptive warning value at the future moment.

8. The smart city building fire monitoring and early warning system based on multimodal data according to claim 1 is characterized in that: The abnormal warning module performs a fire warning on any building according to the first-order temperature difference sequence of the temperature subsequence, including: Calculating the absolute value of the average value of all difference values ​​in the first-order difference sequence of the temperature subsequence to obtain the temperature change index of the temperature subsequence; If the temperature change index is greater than a preset temperature change index threshold, a fire warning is issued for any of the buildings.

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