CN118314673A - Intelligent fire disaster early warning method and system based on internet data - Google Patents
Intelligent fire disaster early warning method and system based on internet data Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to an intelligent fire early warning method and system based on internet data, comprising the following steps: collecting a plurality of whole environment data sequences and a plurality of environment data sequences; obtaining an environmental characteristic factor of the whole environmental data sequence according to the distribution difference conditions of the change directions of the environmental reference data in different time ranges in the whole environmental data sequence; obtaining the environment filtering weight of the environment reference data according to the matching difference condition of the environment reference data and the environment characteristic factors at the same recording time between different environment data sequences and the association condition of the environment reference data and the external environment factors; and carrying out self-adaptive window adjustment according to the environmental filtering weight, and denoising the environmental reference data. The invention enables the filter window to realize self-adaptive adjustment, improves the denoising effect and improves the accuracy of intelligent fire early warning results.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent fire early warning method and system based on internet data.
Background
In order to ensure the property safety of the place, the intelligent robot is generally used for collecting the environmental information in the place and detecting the transmitted environmental data through the connection with the Internet so as to perform fire early warning in real time. However, the environmental data collected by the intelligent robot has random noise interference, so that the environmental data needs to be denoised before intelligent fire early warning.
The existing method generally utilizes an SG (Savitzky-Golay) filtering algorithm to denoise the environmental data, and the traditional SG filtering algorithm generally adopts a fixed window to denoise and filter the environmental data; however, the influence of different kinds of environmental data on the fire prediction result in the place is not the same, but the traditional SG filtering algorithm cannot be well combined with the influence condition of different kinds of environmental data on the fire prediction result, so that the denoising effect on the environmental data is not ideal, and the accuracy of the fire early-warning result is reduced.
Disclosure of Invention
The invention provides an intelligent fire early warning method and system based on internet data, which aims to solve the existing problems: the existing SG (Savitzky-Golay) filtering algorithm adopts a fixed window to carry out denoising filtering on environmental data, and does not combine the influence conditions of different types of environmental data on fire prediction results, so that the accuracy of fire early warning results is reduced.
The intelligent fire disaster early warning method and system based on the internet data adopt the following technical scheme:
The embodiment of the invention provides an intelligent fire early warning method based on internet data, which comprises the following steps:
collecting a plurality of whole environment data sequences and a plurality of environment data sequences, wherein the whole environment data sequences comprise a plurality of environment data sequences, the environment data sequences comprise a plurality of environment reference data, and each environment reference data corresponds to one recording moment;
According to the distribution difference conditions of the changing directions of the environmental reference data in different time ranges in the whole environmental data sequences, obtaining the environmental characteristic factors of each whole environmental data sequence;
Obtaining the environmental filtering weight of each environmental reference data according to the matching difference condition of the environmental reference data and the environmental characteristic factors at the same recording moment between different environmental data sequences and the association condition of the environmental reference data and the external environmental factors;
And carrying out self-adaptive window adjustment according to the environmental filtering weight, and denoising the environmental reference data.
Preferably, the obtaining the environmental characteristic factor of each overall environmental data sequence according to the distribution difference condition of the variation directions of the environmental reference data in different time ranges in the overall environmental data sequence comprises the following specific methods:
Presetting an environment reference data quantity T1; for any one whole environment data sequence, marking any one environment reference data in the whole environment data sequence as target environment reference data, marking a data segment formed by T1 environment reference data before the target environment reference data and T1 environment reference data after the target environment reference data as a neighborhood environment data segment of the target environment reference data;
acquiring a trend direction difference value of each extreme value in a neighborhood environment data segment of the target environment reference data;
Carrying out linear normalization on all trend direction difference values, and marking the normalized trend direction difference values as trend direction difference factors; marking any extreme value in a neighborhood environment data segment of the target environment reference data as a target extreme value, and marking a difference value of trend direction difference factors of subtracting the target extreme value from 1 as a first difference value of the target extreme value; the difference value of subtracting the target extremum from the average value of all extremums is recorded as a second difference value of the target extremum; the square of the product of the first difference value and the second difference value of the target extremum is recorded as the first product of the target extremum;
acquiring a first product of all extreme values in a neighborhood environmental data segment of the target environmental reference data, and recording an open square value of an accumulated sum of the first products of all the extreme values as a first accumulated value of the target environmental reference data; acquiring first accumulated values of all environment reference data in the whole environment data sequence, and recording an inverse proportion normalized value of an accumulated sum of the first accumulated values of all environment reference data as a first inverse proportion value; the difference of 1 minus the first inverse proportion value is noted as the environmental characteristic factor of the overall environmental data sequence.
Preferably, the method for obtaining the trend direction difference value of each extremum in the neighborhood environmental data segment of the target environmental reference data includes the following specific steps:
And acquiring principal component directions in the neighborhood environment data segment by using a principal component analysis algorithm (PCA), marking a value corresponding to each extreme value in the principal component directions in the neighborhood environment data segment as a principal component value of each extreme value, and marking a difference value obtained by subtracting the corresponding principal component value from each extreme value in the neighborhood environment data segment as a trend direction difference value of each extreme value.
Preferably, the method for obtaining the environmental filtering weight of each environmental reference data according to the matching difference condition of the environmental reference data and the environmental characteristic factors at the same recording time between different environmental data sequences and the association condition of the environmental reference data and the external environmental factors includes the following specific steps:
For any one whole environment data sequence, in the whole environment data sequence, marking the last environment data sequence as a core environment data sequence, and marking each environment data sequence except the core environment data sequence as a reference environment data sequence; for any one reference environment data sequence, acquiring all matching point pairs of the reference environment data sequence and the core environment data sequence by using a DTW dynamic time warping algorithm; acquiring trend time overall difference values, neighborhood distance overall difference values and similar weights of all matching point pairs;
Obtaining the environmental similarity of the core environmental data sequence and the reference environmental data sequence according to the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs between the core environmental data sequence and the reference environmental data sequence; acquiring the environmental similarity of the core environmental data sequence and all the reference environmental data sequences;
Obtaining the noise influence degree of the core environment data sequence according to the environment similarity of the core environment data sequence and all the reference environment data sequences;
For any one environmental reference data in the core environmental data sequence, marking standard deviations of all environmental reference data in a neighborhood environmental data segment of the environmental reference data as first standard deviations, acquiring the first standard deviations of all environmental reference data in the core environmental data sequence, carrying out linear normalization on all the first standard deviations, and marking the normalized first standard deviations as local environmental standard deviations; presetting an environment reference data threshold T2, and recording the difference value obtained by subtracting the T2 from the average value of all the environment reference data in the neighborhood environment data segment of the environment reference data as an abnormal index of the environment reference data; acquiring abnormal indexes of all environment reference data;
For the z-th environmental reference data in the v-th overall environmental data sequence, obtaining the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence according to the local environmental standard deviation of the z-th environmental reference data, the environmental characteristic factor of the v-th overall environmental data sequence, the noise influence degree of the core environmental data sequence of the v-th overall environmental data sequence and the difference of the anomaly indexes between the v-th overall environmental data sequence and other overall environmental data sequences;
obtaining noise anomaly degree of all environment reference data in the v-th overall environment data sequence, carrying out linear normalization on all noise anomaly degree, and recording the normalized noise anomaly degree as an environment filtering weight.
Preferably, the method for obtaining the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs includes the following specific steps:
For any matching point pair, recording the environment reference data of the matching point pair in the reference environment data sequence as first target environment reference data; in the reference environment data sequence, the environment reference data corresponding to the extreme value with the smallest recording time from the first target environment reference data is recorded as second target environment reference data, and the absolute value of the difference value of the recording time between the first target environment reference data and the second target environment reference data is recorded as the trend time difference value of the first target environment reference data; the environment reference data of the matching point pair in the core environment data sequence is recorded as third target environment reference data, and the trend time difference value of the third target environment reference data is acquired by referring to the acquisition method of the trend time difference value of the first target environment reference data; the average value of the trend time difference values of the first target environment reference data and the third target environment reference data is recorded as the trend time overall difference value of the matching point pair;
The method comprises the steps that on the left side of first target environment reference data, environment reference data corresponding to an extreme value with the smallest recording moment distance from the first target environment reference data are recorded as first target environment data; on the right side of the first target environment reference data, recording environment reference data corresponding to the extreme value with the smallest recording moment distance from the first target environment reference data as second target environment data; the Euclidean distance between the first target environment data and the second target environment data is recorded as a neighborhood distance value of the first target environment reference data; obtaining a neighborhood distance value of third target environment reference data by referring to the neighborhood distance value obtaining method of the first target environment reference data; the average value of the neighborhood distance values of the first target environment reference data and the third target environment reference data is recorded as the neighborhood distance integral difference value of the matching point pair;
In the matching point pair, if the first target environment reference data is an extremum, marking the neighborhood distance value of the first target environment reference data as the similarity weight of the matching point pair; if the third target environment reference data is an extremum, marking the neighborhood distance value of the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are extreme values, marking the accumulated sum of the neighborhood distance values of the first target environment reference data and the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are not extreme values, marking 1 as the similar weight of the matching point pair.
Preferably, the method for obtaining the environmental similarity between the core environmental data sequence and the reference environmental data sequence according to the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs between the core environmental data sequence and the reference environmental data sequence includes the following specific steps:
For the c-th matching point pair in the core environment data sequence and the reference environment data sequence, marking the product of the Euclidean distance between two environment reference data in the c-th matching point pair, the similarity weight of the c-th matching point pair and the whole difference value of the neighborhood distance as a second product; the ratio of the trend time overall difference value of the second product and the c-th matching point pair is recorded as the first ratio of the c-th matching point pair, and the first ratios of all the matching point pairs are obtained; the accumulated sum of the first ratios of all the matching point pairs is recorded as an environment similarity value of the core environment data sequence and the reference environment data sequence; and obtaining all the environment similarity values, carrying out linear normalization on all the environment similarity values, and recording the normalized environment similarity values as the environment similarity.
Preferably, the method for obtaining the noise influence degree of the core environment data sequence according to the environmental similarity between the core environment data sequence and all the reference environment data sequences includes the following specific steps:
The method comprises the steps of marking the average value of all environment reference data in a core environment data sequence as a first average value, marking the average value of all environment reference data in a reference environment data sequence as a second average value, marking the absolute value of the difference value between the first average value and the second average value as the environment difference factor of the core environment data sequence and the reference environment data sequence, obtaining the environment difference factor of the core environment data sequence and all the reference environment data sequence, carrying out linear normalization on all the environment difference factors, and marking the normalized environment difference factor as the environment difference degree;
For any one reference environment data sequence, marking a difference value of subtracting the degree of the environmental difference between the core environment data sequence and the reference environment data sequence from 1 as a third difference value; the product of the environmental similarity of the core environmental data sequence and the reference environmental data sequence and the third difference value is recorded as a third product of the reference environmental data sequence; and obtaining third products of all the reference environment data sequences, and recording the accumulated sum of the third products of all the reference environment data sequences as the noise influence degree of the core environment data sequences.
Preferably, the method for obtaining the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence according to the local environmental standard deviation of the z-th environmental reference data, the environmental characteristic factor of the v-th overall environmental data sequence, the noise influence degree of the core environmental data sequence of the v-th overall environmental data sequence, and the difference of the anomaly indexes between the v-th overall environmental data sequence and other overall environmental data sequences includes the following specific steps:
Recording the difference value of the environmental characteristic factors of the v whole environmental data sequence subtracted from 1 as a fourth difference value; the product of the noise influence degree of the core environment data sequence of the v-th overall environment data sequence, the local environment standard deviation of the z-th environment reference data in the v-th overall environment data sequence and the fourth difference value is recorded as a fourth product; recording the ratio of the fourth product to the number of all the whole environment data sequences except the v whole environment data sequence as a second ratio of the z-th environment reference data in the v whole environment data sequence;
For the (q) th overall environment data sequence except the (v) th overall environment data sequence, subtracting the difference value of the abnormal indexes of the (z) th environment reference data in the (q) th overall environment data sequence from the abnormal indexes of the (z) th environment reference data in the (v) th overall environment data sequence, and recording the difference value as a fifth difference value; the ratio of the environmental characteristic factor of the q-th overall environmental data sequence to the local environmental standard deviation of the z-th environmental reference data in the v-th overall environmental data sequence is recorded as a third ratio; the square of the product of the fifth difference value and the third ratio is recorded as the fifth product of the q-th whole environment data sequence; obtaining a fifth product of all whole environment data sequences except for a v-th whole environment data sequence, and recording the accumulated sum of all the fifth products as a first accumulated sum of the z-th environment reference data in the v-th whole environment data sequence;
the product of the second ratio and the first accumulated sum is recorded as the noise anomaly of the z-th environmental reference data in the v-th overall environmental data sequence.
Preferably, the adaptive window adjustment is performed according to the environmental filtering weight, and the denoising is performed on the environmental reference data, including the following specific methods:
Presetting a filtering window length T3 and a super parameter mu, and recording the sum of mu and the environmental filtering weight of the z1 st environmental reference data in the v1 st overall environmental data sequence as a first sum value for the z1 st environmental reference data in the v1 st overall environmental data sequence; the product of T3 and the first sum is recorded as a sixth product; the upward rounding result of the sixth product is recorded as the final filter window length of the z1 st environmental reference data in the v1 st overall environmental data sequence;
And constructing a window with the window length of L v1,z1 as a final filter window of the z1 st environmental reference data in the v1 st integral environmental data sequence, acquiring a final filter window of each environmental reference data in each integral environmental data sequence, taking the final filter window of each environmental reference data as a filter window, denoising each environmental reference data according to the filter window through an SG filter algorithm, and obtaining all denoised environmental reference data.
The invention also provides an intelligent fire early-warning system based on the internet data, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory so as to realize the steps of the intelligent fire early-warning method based on the internet data.
The technical scheme of the invention has the beneficial effects that: according to the invention, the window is adaptively adjusted by combining the association condition of the whole environment data sequence and external environment factors and the distribution difference condition of the change directions of the environment reference data in different time ranges, so that the denoising effect of each environment reference data is improved; according to the distribution difference conditions of the change directions of the environmental reference data in different time ranges in the whole environmental data sequence, environmental characteristic factors of the whole environmental data sequence are obtained and are used for reflecting the obvious conditions of the characteristics represented by the environmental data types of the whole environmental data sequence, so that the expression degree of the environmental reference data influenced by the environmental factors is improved; then according to the matching difference condition of the environmental reference data and the environmental characteristic factors at the same recording moment between different environmental data sequences and the association condition of the environmental reference data and the external environmental factors, obtaining the environmental filtering weight of each environmental reference data, and reflecting the intensity of denoising after analysis in the aspects of the external environmental factors and the internal noise, thereby improving the denoising effect of each environmental reference data; finally, performing self-adaptive window adjustment according to the environmental filtering weight, and denoising the environmental reference data; according to the invention, through analyzing the influence condition of the external environment factors and the internal noise factors on the environment reference data, the filter window is adaptively adjusted, so that the filter window can be adaptively adjusted according to different environment reference data, the denoising effect is improved, and the accuracy of the intelligent fire early warning result is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent fire early warning method based on Internet data;
fig. 2 is a characteristic relation flow chart of the intelligent fire early warning method based on internet data.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the intelligent fire early warning method and system based on internet data according to the invention, and the specific implementation, structure, characteristics and effects thereof are as follows. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the intelligent fire early warning method and system based on internet data provided by the invention is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of an intelligent fire early warning method based on internet data according to an embodiment of the present invention is shown, and the method includes the following steps:
step S001: a number of overall environmental data sequences and a number of environmental data sequences are acquired.
It should be noted that, in the existing method, the environmental data is usually denoised by using an SG (Savitzky-Golay) filtering algorithm, and the data is usually denoised and filtered by using a fixed window in the traditional SG filtering algorithm; however, the influence of different kinds of environmental data on the fire prediction result in the place is not the same, but the traditional SG filtering algorithm cannot be well combined with the influence condition of different kinds of environmental data on the fire prediction result, so that the denoising effect on the environmental data is not ideal, and the accuracy of the fire early-warning result is reduced. Referring to fig. 2, a characteristic relation flowchart of an intelligent fire early warning method based on internet data is shown.
Specifically, the environmental data sequence needs to be collected at first, and the specific process is as follows: acquiring temperature data, smoke concentration data and brightness data of the near week in an internet database communicated with the intelligent robot; taking all acquired temperature data as an example, carrying out linear normalization on all the temperature data, marking each normalized temperature data as environment reference data, and marking a sequence formed by all the environment reference data in all days as an overall environment data sequence; recording a sequence formed by all the environmental reference data in each day as an environmental data sequence; and acquiring all the whole environment data sequences and all the corresponding environment data sequences. Wherein each environmental reference data corresponds to a recording time, each overall environmental data sequence comprises a plurality of environmental data sequences, and each environmental data sequence comprises a plurality of environmental reference data. In addition, the data type and the acquisition time length of the present embodiment are described by taking temperature data, smoke concentration data, brightness data, and one week as examples, and the present embodiment is not particularly limited, and the data type and the acquisition time length may be determined according to specific implementation conditions.
So far, the whole environment data sequences of all kinds and all environment data sequences are obtained through the method.
Step S002: and obtaining the environmental characteristic factors of each whole environmental data sequence according to the distribution difference conditions of the change directions of the environmental reference data in different time ranges in the whole environmental data sequence.
It should be noted that, the same kind of environmental reference data has different influences on the fire early warning result in different places, for example: as the temperature data increases, the warehouse storing a large amount of inflammables is more likely to ignite than the warehouse storing a large amount of non-inflammables, thereby causing a fire. The same kind of environmental reference data is in the same place, and the environmental condition overall can show trend change in a certain direction along with the time, so that the corresponding environmental reference data overall can also change along a certain direction; if part of the environmental reference data is interfered by noise, the original change trend direction is destroyed. In order to improve accuracy of intelligent fire early warning results, the embodiment obtains environmental characteristic factors of the whole environment data sequence by analyzing change direction trend of reference environment data in the whole environment data sequence so as to facilitate subsequent analysis and processing.
Specifically, an environmental reference data amount T1 is preset, where the embodiment is described by taking t1=7 as an example, and the embodiment is not specifically limited, where T1 may be determined according to specific implementation situations; taking any one whole environment data sequence as an example, marking any one environment reference data in the whole environment data sequence as target environment reference data, marking a data segment formed by T1 environment reference data before the target environment reference data and T1 environment reference data after the target environment reference data as a neighborhood environment data segment of the target environment reference data; and acquiring principal component directions in the neighborhood environment data segment by using a PCA principal component analysis algorithm, marking a value corresponding to each extremum in the neighborhood environment data segment in the principal component directions as a principal component value of each extremum, and marking a difference value between each extremum in the neighborhood environment data segment and the corresponding principal component value as a trend direction difference value of each extremum. The direction of the principal component of the acquired data sequence is the known content of PCA (Principal Component Analysis) principal component analysis algorithm, and this embodiment is not repeated here; in addition, each environmental reference data in the neighborhood environmental data segment is acquired to have a value corresponding to the principal component direction. In addition, it should be noted that if the number of the environmental reference data actually existing before the target environmental reference data does not meet the preset T1, the neighborhood environmental data segment of the target environmental reference data is obtained based on the environmental reference data actually existing before the target environmental reference data; if the number of the environmental reference data actually existing after the target environmental reference data does not meet the preset T1, acquiring a neighborhood environmental data segment of the target environmental reference data based on the environmental reference data actually existing after the target environmental reference data; and each context reference data corresponds to a neighborhood context data segment.
Further, according to the environmental change difference condition in the local range of each environmental reference data in the whole environmental data sequence, the environmental characteristic factor of the whole environmental data sequence is obtained. As one example, the environmental characteristic factor of the overall environmental data sequence may be calculated by the following formula:
Wherein h represents an environmental characteristic factor of the overall environmental data sequence; i represents the number of all environmental reference data in the overall environmental data sequence; m i represents the number of all extremum values in the neighborhood environmental data segment of the ith environmental reference data; a i,m represents the mth extremum in the neighborhood environmental data segment of the ith environmental reference data; Representing the average value of all extreme values in the neighborhood environmental data segment of the ith environmental reference data; Δb i,m represents the trend direction difference value of the mth extremum in the neighborhood environmental data segment of the ith environmental reference data; exp () represents an exponential function based on a natural constant, the embodiment adopts an exp (-x) model to present an inverse proportion relation and normalization processing, x is the input of the model, and an implementer can select the inverse proportion function and the normalization function according to actual conditions; softmax () represents a normalization function for normalizing all Δb i,m. And if the environmental characteristic factor of the whole environmental data sequence is larger, the degree of influence of the environment on the data type corresponding to the whole environmental data sequence is larger, and the degree of influence of noise is smaller. And acquiring the environmental characteristic factors of all the whole environmental data sequences.
So far, the environmental characteristic factors of all the whole environmental data sequences are obtained through the method.
Step S003: and obtaining the environmental filtering weight of each environmental reference data according to the matching difference condition of the environmental reference data and the environmental characteristic factors at the same recording time between different environmental data sequences and the association condition of the environmental reference data and the external environmental factors.
In the actual environment, the fire early warning result is obtained by comprehensively analyzing environmental factors such as the external temperature, the smoke concentration, the light brightness and the like; thus for the same kind of environmental reference data in the same place, this kind of environmental reference data may be affected by other kinds of environmental reference data, such as: when the temperature is higher, air circulation can be accelerated so as to change the diffusion state of the smoke, and meanwhile, the judgment of the ambient brightness can be influenced to a certain extent. So that the environment reference data of the category has association conditions with different degrees in different time ranges, wherein if the association degree is larger, the environment reference data of the category is related to external environment factors, and the degree of noise interference is smaller; in order to improve the accuracy of fire early warning results, the embodiment obtains the noise index of the whole environment data sequence by analyzing the association condition among different environment data sequences so as to facilitate subsequent analysis and processing.
Specifically, taking any one whole environment data sequence as an example, in the whole environment data sequence, marking the last environment data sequence as a core environment data sequence, marking each environment data sequence except the core environment data sequence as a reference environment data sequence, taking any one reference environment data sequence as an example, and acquiring all matching point pairs of the reference environment data sequence and the core environment data sequence; taking any matching point pair as an example, recording the environment reference data of the matching point pair in the reference environment data sequence as first target environment reference data; in the reference environment data sequence, the environment reference data corresponding to the extreme value with the smallest recording time from the first target environment reference data is recorded as second target environment reference data, and the absolute value of the difference value of the recording time between the first target environment reference data and the second target environment reference data is recorded as the trend time difference value of the first target environment reference data. The environment reference data of the matching point pair in the core environment data sequence is recorded as third target environment reference data, and the trend time difference value of the third target environment reference data is acquired by referring to the acquisition method of the trend time difference value of the first target environment reference data; and recording the mean value of the trend time difference values of the first target environment reference data and the third target environment reference data as the trend time integral difference value of the matching point pair. The process of obtaining the matching point pairs of the two data sequences is a well-known content of DTW (Dynamic Time Warping) dynamic time warping algorithm, and this embodiment is not repeated. Wherein each matching point pair contains a first target environment reference data and a third target environment reference data.
Further, on the left side of the first target environment reference data, the environment reference data corresponding to the extreme value with the smallest recording moment distance from the first target environment reference data is recorded as the first target environment data; on the right side of the first target environment reference data, recording environment reference data corresponding to the extreme value with the smallest recording moment distance from the first target environment reference data as second target environment data; the Euclidean distance between the first target environment data and the second target environment data is recorded as a neighborhood distance value of the first target environment reference data; obtaining a neighborhood distance value of third target environment reference data by referring to the neighborhood distance value obtaining method of the first target environment reference data; and recording the average value of the neighborhood distance values of the first target environment reference data and the third target environment reference data as the neighborhood distance integral difference value of the matching point pair. The obtaining of the euclidean distance is a well-known technique, and this embodiment is not described in detail.
Further, in the matching point pair, if only the first target environment reference data is an extremum, the neighborhood distance value of the first target environment reference data is recorded as the similarity weight of the matching point pair; if only the third target environment reference data is an extremum, marking the neighborhood distance value of the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are extreme values, recording the accumulated sum of the neighborhood distance values of the first target environment reference data and the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are not extreme values, marking 1 as the similar weight of the matching point pair. And acquiring the trend time overall difference value, the neighborhood distance overall difference value and the similarity weight of all the matching point pairs.
Further, according to the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs between the core environment data sequence and the reference environment data sequence, the environment similar value of the core environment data sequence and the reference environment data sequence is obtained. As an example, the environmental similarity value of the core environmental data sequence and the reference environmental data sequence may be calculated by the following formula:
Wherein s represents an environmental similarity value between the core environmental data sequence and the reference environmental data sequence; c represents the number of all matching point pairs of the core environment data sequence and the reference environment data sequence; d c denotes the Euclidean distance between the two environmental reference data in the c-th matching point pair; k c denotes the similarity weight of the c-th matching point pair; deltau c represents the neighborhood distance overall difference value of the c-th matching point pair; Δt c represents the trend time overall difference value of the c-th matching point pair. And if the environmental similarity value of the core environmental data sequence and the reference environmental data sequence is larger, the influence of external environmental factors on the core environmental data sequence and the reference environmental data sequence is larger, and the degree of noise interference is smaller. And obtaining the environment similarity values of the core environment data sequence and all the reference environment data sequences, carrying out linear normalization on all the environment similarity values, and recording each normalized environment similarity value as the environment similarity.
Further, the average value of all the environmental reference data in the core environmental data sequence is marked as a first average value, the average value of all the environmental reference data in the reference environmental data sequence is marked as a second average value, the absolute value of the difference value between the first average value and the second average value is marked as the environmental difference factor of the core environmental data sequence and the reference environmental data sequence, the environmental difference factors of the core environmental data sequence and all the reference environmental data sequence are obtained, all the environmental difference factors are subjected to linear normalization, and each environmental difference factor after normalization is marked as the environmental difference degree. And obtaining the noise influence degree of the core environment data sequence according to the environment similarity and the environment difference degree of the core environment data sequence and all the reference environment data sequences. As an example, the noise impact level of the core environment data sequence may be calculated by the following formula:
Wherein f represents the noise influence degree of the core environment data sequence; w represents the number of all reference environment data sequences; s w represents the environmental similarity of the core environmental data sequence and the w-th reference environmental data sequence; gamma w denotes the degree of environmental difference of the core environmental data sequence from the w-th reference environmental data sequence. If the noise influence degree of the core environment data sequence is larger, the environment data type corresponding to the core environment data sequence is more relevant, and the trend of the overall environment data change is more influenced by environment factors and is weaker in the degree of noise interference.
Further, taking any one environmental reference data in the core environmental data sequence as an example, marking the standard deviation of all environmental reference data in a neighborhood environmental data segment of the environmental reference data as a first standard deviation, obtaining the first standard deviation of all environmental reference data in the core environmental data sequence, carrying out linear normalization on all the first standard deviations, and marking each normalized first standard deviation as a local environmental standard deviation. Presetting an environmental reference data threshold T2, wherein the present embodiment is described by taking t2=0.4 as an example, and the present embodiment is not particularly limited, wherein T2 may be determined according to the specific implementation situation; recording the difference value between the average value of all the environmental reference data in the neighborhood environmental data segment of the environmental reference data and T2 as an abnormal index of the environmental reference data; and acquiring the abnormal indexes of all the environmental reference data in the whole environmental data sequence, and acquiring the abnormal indexes of all the environmental reference data in all the whole environmental data sequence.
Further, taking the z-th environmental reference data in the v-th overall environmental data sequence as an example, obtaining the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence according to the local environmental standard deviation of the z-th environmental reference data, the environmental characteristic factor of the v-th overall environmental data sequence, the noise influence degree of the core environmental data sequence of the v-th overall environmental data sequence, and the difference of the anomaly indexes between the v-th overall environmental data sequence and other overall environmental data sequences. As an example, the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence may be calculated by the following formula:
Wherein G v,z represents the noise anomaly of the z-th environmental reference data in the v-th overall environmental data sequence; f v denotes the noise influence degree of the core environment data sequence of the v-th overall environment data sequence; h v denotes an environmental feature factor of the v-th overall environmental data sequence; epsilon v.z represents the local environmental standard deviation of the z-th environmental reference data in the v-th global environmental data sequence; τ represents a preset hyper-parameter for preventing the denominator from being 0; q v represents the number of all the whole environment data sequences except the v-th whole environment data sequence; r v,z represents an abnormality index of the z-th environmental reference data in the v-th overall environmental data sequence; r v,q,z denotes an abnormality index of the z-th environmental reference data in the q-th overall environmental data sequence other than the v-th overall environmental data sequence; h v,q denotes an environmental characteristic factor at the q-th overall environmental data sequence other than the v-th overall environmental data sequence. And if the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence is larger, the correlation between the z-th environmental reference data in the v-th overall environmental data sequence and other environmental factors is lower, and the degree of noise interference of the z-th environmental reference data in the v-th overall environmental data sequence is reflected to be larger. Obtaining noise anomaly degree of all environment reference data in the v-th overall environment data sequence, carrying out linear normalization on all noise anomaly degree, and recording each noise anomaly degree after normalization as an environment filtering weight. And acquiring the environmental filtering weights of all environmental reference data in all the whole environmental data sequences.
So far, the environmental filtering weights of all the environmental reference data in all the whole environmental data sequences are obtained through the method.
Step S004: and carrying out self-adaptive window adjustment according to the environmental filtering weight, and denoising the environmental reference data.
Specifically, a filter window length T3 is preset, where the embodiment is described by taking t3=10 as an example, and the embodiment is not specifically limited, where T3 may be determined according to specific implementation situations; taking the z1 st environmental reference data in the v1 st overall environmental data sequence as an example, according to the filtering window side length T3 and the environmental filtering weight of the z1 st environmental reference data, the final filtering window length of the z1 st environmental reference data in the v1 st overall environmental data sequence is obtained. As an example, the final filter window length of the z1 st environmental reference data in the v1 st overall environmental data sequence may be calculated by the following formula:
Where L v1,z1 represents the final filter window length of the z1 st environmental reference data in the v1 st global environmental data sequence; t3 represents a preset filter window length; μ represents a preset hyper-parameter, in this embodiment preset μ=0.5, for reflecting the initial weight ratio of the filter window; GP v1,z1 represents the environmental filtering weight of the z1 st environmental reference data in the v1 st overall environmental data sequence; Representing an upward rounding. If the final filter window length of the z1 th environmental reference data in the v1 th overall environmental data sequence is larger, the strength that the z1 st environmental reference data in the v1 th overall environmental data sequence is interfered by noise is larger, and the strength that the z1 st environmental reference data in the v1 th overall environmental data sequence needs to be de-noised is reflected to be larger.
Further, a window with a window length of L v1,z1 is constructed as a final filter window of the z1 st environmental reference data in the v1 st whole environmental data sequence, a final filter window of each environmental reference data in each whole environmental data sequence is obtained, the final filter window of each environmental reference data is used as a filter window, and denoising is carried out on each environmental reference data according to the filter window, so that all denoised environmental reference data are obtained. And inputting all the denoised environmental reference data into an early warning system to complete intelligent fire early warning. The process of denoising the data according to the filtering window is a well-known content of SG filtering algorithm, and this embodiment is not described in detail.
Through the steps, the intelligent fire early warning method based on the internet data is completed.
Another embodiment of the present invention provides an intelligent fire early-warning system based on internet data, the system including a memory and a processor, the processor executing the computer program stored in the memory, the above-mentioned method steps S001 to S004.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The intelligent fire disaster early warning method based on the internet data is characterized by comprising the following steps of:
collecting a plurality of whole environment data sequences and a plurality of environment data sequences, wherein the whole environment data sequences comprise a plurality of environment data sequences, the environment data sequences comprise a plurality of environment reference data, and each environment reference data corresponds to one recording moment;
According to the distribution difference conditions of the changing directions of the environmental reference data in different time ranges in the whole environmental data sequences, obtaining the environmental characteristic factors of each whole environmental data sequence;
Obtaining the environmental filtering weight of each environmental reference data according to the matching difference condition of the environmental reference data and the environmental characteristic factors at the same recording moment between different environmental data sequences and the association condition of the environmental reference data and the external environmental factors;
And carrying out self-adaptive window adjustment according to the environmental filtering weight, and denoising the environmental reference data.
2. The intelligent fire early warning method based on internet data according to claim 1, wherein the obtaining the environmental characteristic factor of each overall environmental data sequence according to the distribution difference of the changing directions of the environmental reference data in different time ranges in the overall environmental data sequence comprises the following specific methods:
Presetting an environment reference data quantity T1; for any one whole environment data sequence, marking any one environment reference data in the whole environment data sequence as target environment reference data, marking a data segment formed by T1 environment reference data before the target environment reference data and T1 environment reference data after the target environment reference data as a neighborhood environment data segment of the target environment reference data;
acquiring a trend direction difference value of each extreme value in a neighborhood environment data segment of the target environment reference data;
Carrying out linear normalization on all trend direction difference values, and marking the normalized trend direction difference values as trend direction difference factors; marking any extreme value in a neighborhood environment data segment of the target environment reference data as a target extreme value, and marking a difference value of trend direction difference factors of subtracting the target extreme value from 1 as a first difference value of the target extreme value; the difference value of subtracting the target extremum from the average value of all extremums is recorded as a second difference value of the target extremum; the square of the product of the first difference value and the second difference value of the target extremum is recorded as the first product of the target extremum;
acquiring a first product of all extreme values in a neighborhood environmental data segment of the target environmental reference data, and recording an open square value of an accumulated sum of the first products of all the extreme values as a first accumulated value of the target environmental reference data; acquiring first accumulated values of all environment reference data in the whole environment data sequence, and recording an inverse proportion normalized value of an accumulated sum of the first accumulated values of all environment reference data as a first inverse proportion value; the difference of 1 minus the first inverse proportion value is noted as the environmental characteristic factor of the overall environmental data sequence.
3. The intelligent fire early warning method based on internet data according to claim 2, wherein the acquiring the trend direction difference value of each extremum in the neighborhood environmental data segment of the target environmental reference data comprises the following specific steps:
And acquiring principal component directions in the neighborhood environment data segment by using a principal component analysis algorithm (PCA), marking a value corresponding to each extreme value in the principal component directions in the neighborhood environment data segment as a principal component value of each extreme value, and marking a difference value obtained by subtracting the corresponding principal component value from each extreme value in the neighborhood environment data segment as a trend direction difference value of each extreme value.
4. The intelligent fire early warning method based on internet data according to claim 1, wherein the obtaining the environmental filtering weight of each environmental reference data according to the matching difference condition of the environmental reference data and the environmental characteristic factors and the association condition of the environmental reference data and the external environmental factors at the same recording time between different environmental data sequences comprises the following specific steps:
For any one whole environment data sequence, in the whole environment data sequence, marking the last environment data sequence as a core environment data sequence, and marking each environment data sequence except the core environment data sequence as a reference environment data sequence; for any one reference environment data sequence, acquiring all matching point pairs of the reference environment data sequence and the core environment data sequence by using a DTW dynamic time warping algorithm; acquiring trend time overall difference values, neighborhood distance overall difference values and similar weights of all matching point pairs;
Obtaining the environmental similarity of the core environmental data sequence and the reference environmental data sequence according to the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs between the core environmental data sequence and the reference environmental data sequence; acquiring the environmental similarity of the core environmental data sequence and all the reference environmental data sequences;
Obtaining the noise influence degree of the core environment data sequence according to the environment similarity of the core environment data sequence and all the reference environment data sequences;
For any one environmental reference data in the core environmental data sequence, marking standard deviations of all environmental reference data in a neighborhood environmental data segment of the environmental reference data as first standard deviations, acquiring the first standard deviations of all environmental reference data in the core environmental data sequence, carrying out linear normalization on all the first standard deviations, and marking the normalized first standard deviations as local environmental standard deviations; presetting an environment reference data threshold T2, and recording the difference value obtained by subtracting the T2 from the average value of all the environment reference data in the neighborhood environment data segment of the environment reference data as an abnormal index of the environment reference data; acquiring abnormal indexes of all environment reference data;
For the z-th environmental reference data in the v-th overall environmental data sequence, obtaining the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence according to the local environmental standard deviation of the z-th environmental reference data, the environmental characteristic factor of the v-th overall environmental data sequence, the noise influence degree of the core environmental data sequence of the v-th overall environmental data sequence and the difference of the anomaly indexes between the v-th overall environmental data sequence and other overall environmental data sequences;
obtaining noise anomaly degree of all environment reference data in the v-th overall environment data sequence, carrying out linear normalization on all noise anomaly degree, and recording the normalized noise anomaly degree as an environment filtering weight.
5. The intelligent fire early warning method based on internet data according to claim 4, wherein the acquiring trend time overall difference values, neighborhood distance overall difference values and similar weights of all matching point pairs comprises the following specific steps:
For any matching point pair, recording the environment reference data of the matching point pair in the reference environment data sequence as first target environment reference data; in the reference environment data sequence, the environment reference data corresponding to the extreme value with the smallest recording time from the first target environment reference data is recorded as second target environment reference data, and the absolute value of the difference value of the recording time between the first target environment reference data and the second target environment reference data is recorded as the trend time difference value of the first target environment reference data; the environment reference data of the matching point pair in the core environment data sequence is recorded as third target environment reference data, and the trend time difference value of the third target environment reference data is acquired by referring to the acquisition method of the trend time difference value of the first target environment reference data; the average value of the trend time difference values of the first target environment reference data and the third target environment reference data is recorded as the trend time overall difference value of the matching point pair;
The method comprises the steps that on the left side of first target environment reference data, environment reference data corresponding to an extreme value with the smallest recording moment distance from the first target environment reference data are recorded as first target environment data; on the right side of the first target environment reference data, recording environment reference data corresponding to the extreme value with the smallest recording moment distance from the first target environment reference data as second target environment data; the Euclidean distance between the first target environment data and the second target environment data is recorded as a neighborhood distance value of the first target environment reference data; obtaining a neighborhood distance value of third target environment reference data by referring to the neighborhood distance value obtaining method of the first target environment reference data; the average value of the neighborhood distance values of the first target environment reference data and the third target environment reference data is recorded as the neighborhood distance integral difference value of the matching point pair;
In the matching point pair, if the first target environment reference data is an extremum, marking the neighborhood distance value of the first target environment reference data as the similarity weight of the matching point pair; if the third target environment reference data is an extremum, marking the neighborhood distance value of the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are extreme values, marking the accumulated sum of the neighborhood distance values of the first target environment reference data and the third target environment reference data as the similarity weight of the matching point pair; if the first target environment reference data and the third target environment reference data are not extreme values, marking 1 as the similar weight of the matching point pair.
6. The intelligent fire early warning method based on internet data according to claim 4, wherein the obtaining the environmental similarity between the core environmental data sequence and the reference environmental data sequence according to the trend time overall difference value, the neighborhood distance overall difference value and the similar weight of all the matching point pairs between the core environmental data sequence and the reference environmental data sequence comprises the following specific steps:
For the c-th matching point pair in the core environment data sequence and the reference environment data sequence, marking the product of the Euclidean distance between two environment reference data in the c-th matching point pair, the similarity weight of the c-th matching point pair and the whole difference value of the neighborhood distance as a second product; the ratio of the trend time overall difference value of the second product and the c-th matching point pair is recorded as the first ratio of the c-th matching point pair, and the first ratios of all the matching point pairs are obtained; the accumulated sum of the first ratios of all the matching point pairs is recorded as an environment similarity value of the core environment data sequence and the reference environment data sequence; and obtaining all the environment similarity values, carrying out linear normalization on all the environment similarity values, and recording the normalized environment similarity values as the environment similarity.
7. The intelligent fire early-warning method based on internet data according to claim 4, wherein the obtaining the noise influence degree of the core environment data sequence according to the environmental similarity between the core environment data sequence and all the reference environment data sequences comprises the following specific steps:
The method comprises the steps of marking the average value of all environment reference data in a core environment data sequence as a first average value, marking the average value of all environment reference data in a reference environment data sequence as a second average value, marking the absolute value of the difference value between the first average value and the second average value as the environment difference factor of the core environment data sequence and the reference environment data sequence, obtaining the environment difference factor of the core environment data sequence and all the reference environment data sequence, carrying out linear normalization on all the environment difference factors, and marking the normalized environment difference factor as the environment difference degree;
For any one reference environment data sequence, marking a difference value of subtracting the degree of the environmental difference between the core environment data sequence and the reference environment data sequence from 1 as a third difference value; the product of the environmental similarity of the core environmental data sequence and the reference environmental data sequence and the third difference value is recorded as a third product of the reference environmental data sequence; and obtaining third products of all the reference environment data sequences, and recording the accumulated sum of the third products of all the reference environment data sequences as the noise influence degree of the core environment data sequences.
8. The intelligent fire early warning method based on internet data according to claim 4, wherein the obtaining the noise anomaly degree of the z-th environmental reference data in the v-th overall environmental data sequence according to the local environmental standard deviation of the z-th environmental reference data, the environmental characteristic factor of the v-th overall environmental data sequence, the noise influence degree of the core environmental data sequence of the v-th overall environmental data sequence, and the difference of the anomaly indexes between the v-th overall environmental data sequence and other overall environmental data sequences comprises the following specific steps:
Recording the difference value of the environmental characteristic factors of the v whole environmental data sequence subtracted from 1 as a fourth difference value; the product of the noise influence degree of the core environment data sequence of the v-th overall environment data sequence, the local environment standard deviation of the z-th environment reference data in the v-th overall environment data sequence and the fourth difference value is recorded as a fourth product; recording the ratio of the fourth product to the number of all the whole environment data sequences except the v whole environment data sequence as a second ratio of the z-th environment reference data in the v whole environment data sequence;
For the (q) th overall environment data sequence except the (v) th overall environment data sequence, subtracting the difference value of the abnormal indexes of the (z) th environment reference data in the (q) th overall environment data sequence from the abnormal indexes of the (z) th environment reference data in the (v) th overall environment data sequence, and recording the difference value as a fifth difference value; the ratio of the environmental characteristic factor of the q-th overall environmental data sequence to the local environmental standard deviation of the z-th environmental reference data in the v-th overall environmental data sequence is recorded as a third ratio; the square of the product of the fifth difference value and the third ratio is recorded as the fifth product of the q-th whole environment data sequence; obtaining a fifth product of all whole environment data sequences except for a v-th whole environment data sequence, and recording the accumulated sum of all the fifth products as a first accumulated sum of the z-th environment reference data in the v-th whole environment data sequence;
the product of the second ratio and the first accumulated sum is recorded as the noise anomaly of the z-th environmental reference data in the v-th overall environmental data sequence.
9. The intelligent fire early warning method based on internet data according to claim 1, wherein the adaptive window adjustment is performed according to the environmental filtering weight, and the environmental reference data is denoised, comprising the following specific steps:
Presetting a filtering window length T3 and a super parameter mu, and recording the sum of mu and the environmental filtering weight of the z1 st environmental reference data in the v1 st overall environmental data sequence as a first sum value for the z1 st environmental reference data in the v1 st overall environmental data sequence; the product of T3 and the first sum is recorded as a sixth product; the upward rounding result of the sixth product is recorded as the final filter window length of the z1 st environmental reference data in the v1 st overall environmental data sequence;
And constructing a window with the window length of L v1,z1 as a final filter window of the z1 st environmental reference data in the v1 st integral environmental data sequence, acquiring a final filter window of each environmental reference data in each integral environmental data sequence, taking the final filter window of each environmental reference data as a filter window, denoising each environmental reference data according to the filter window through an SG filter algorithm, and obtaining all denoised environmental reference data.
10. An intelligent fire early warning system based on internet data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program when executed by the processor implements the steps of the intelligent fire early warning method based on internet data as claimed in any one of claims 1-9.
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