CN113792763A - Social group behavior recognition method based on electromagnetic spectrum data mining, computer device and storage medium - Google Patents
Social group behavior recognition method based on electromagnetic spectrum data mining, computer device and storage medium Download PDFInfo
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Abstract
The invention discloses a social group behavior identification method based on electromagnetic spectrum data mining, a computer device and a storage medium, wherein the social group behavior identification method comprises the following steps: the method comprises the steps of obtaining the frequency band occupancy rate and the characteristic data of social group behaviors, extracting an optimal characteristic subset from the characteristic data, determining the correlation between the frequency band occupancy rate and the optimal characteristic subset, carrying out time-varying behavior analysis on the optimal characteristic subset to obtain a first time sequence, and constructing a social group behavior prediction model for social group behavior recognition according to the correlation and the first time sequence. The invention can complete the identification only by analyzing the physical layer, does not interfere the communication process and the radio environment in the monitoring process, does not need to pass through a communication operator or a user on the premise of legal execution, does not have the risk of invading the privacy and has small implementation difficulty. The method is widely applied to the technical field of data mining.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a social group behavior identification method based on electromagnetic spectrum data mining, a computer device and a storage medium.
Background
The social group behaviors refer to behaviors presented by social activities of people, for example, behaviors such as spring transportation, watching sports meetings, disaster relief, subway evacuation and pedestrian flow can be classified as the social group behaviors, and the social management and decision-making work such as pedestrian flow management and resource allocation is facilitated by identifying the characteristics of the social group behaviors. In modern society, devices using electromagnetic wireless communication, such as mobile phones, tablet computers, wearable devices, etc., are widely used, and because these devices are portable and emit signals almost all the time, they are applied to the identification of social group behaviors, for example, data generated by mobile phone communication is associated with social group behavior distribution and analyzed. However, the prior art needs to acquire data generated by mobile phone communication from an operator, and may need to parse the data generated by mobile phone communication to confirm the content therein, which involves issues of ownership and privacy of the data, so that the prior art faces a large limitation.
Disclosure of Invention
In view of at least one of the above technical problems, it is an object of the present invention to provide a social group behavior recognition method, a computer device and a storage medium based on electromagnetic spectrum data mining.
In one aspect, an embodiment of the present invention includes a social group behavior identification method based on electromagnetic spectrum data mining, including:
acquiring a frequency band occupancy rate of radio communication performed in a first area;
acquiring characteristic data of social group behaviors in the first area;
extracting an optimal feature subset from the feature data;
determining a correlation between the frequency band occupancy and the optimal feature subset through a geographical weighted regression model;
performing time-varying behavior analysis on the optimal feature subset through a time series model to obtain a first time series;
according to the correlation between the frequency band occupancy and the optimal feature subset and the first time sequence, a deep learning model is used for constructing a social group behavior prediction model;
and performing social group behavior identification by using the social group behavior prediction model.
Further, the acquiring a frequency band occupancy of radio communication performed in the first area includes:
determining the position of the first area where the signal source is located through an AOA/TDOA algorithm;
acquiring electromagnetic spectrum monitoring data by monitoring a mobile wireless communication device in the first area;
and calculating the frequency band occupancy rate according to the electromagnetic spectrum monitoring data.
Further, the acquiring feature data of social group behaviors in the first area comprises:
measuring crowd activity trajectory data in the first region;
and carrying out correlation analysis, factor analysis or clustering analysis on the crowd activity track data, and taking the obtained result as the characteristic data.
Further, the extracting an optimal feature subset from the feature data includes:
and performing dimensionality reduction on the feature data by using a principal component analysis method to obtain the optimal feature subset.
Further, the constructing a social group behavior prediction model by using a deep learning model according to the correlation between the frequency band occupancy and the optimal feature subset and the first time series comprises:
determining a second time sequence corresponding to the first time sequence according to the correlation between the frequency band occupancy and the optimal feature subset;
training the deep learning model by taking the second time series as the input of the deep learning model and taking the first time series as the expected output of the deep learning model;
and taking the trained deep learning model as the social group behavior prediction model.
Further, the deep learning model is a recurrent neural network, a graph neural network, a decision tree model, a random forest model, a Markov model or an ARMA model.
Further, the social group behavior identification method based on electromagnetic spectrum data mining further comprises the following steps:
and visualizing the correlation between the occupancy rate of the frequency band and the optimal feature subset through a geographic information system.
Further, the social group behavior recognition using the social group behavior prediction model includes:
acquiring the frequency band occupancy rate of radio communication performed in a specific area within a period of time;
inputting the frequency band occupancy into the social group behavior prediction model;
and determining the characteristics of the social group behaviors according to the output result of the social group behavior prediction model.
In another aspect, the embodiment of the present invention further includes a computer apparatus, including a memory for storing at least one program and a processor for loading the at least one program to perform a social group behavior recognition method based on electromagnetic spectrum data mining.
In another aspect, the present invention further includes a storage medium having stored therein a processor-executable program, which when executed by a processor, is configured to perform a social group behavior recognition method based on electromagnetic spectrum data mining.
The invention has the beneficial effects that: the social group behavior identification method based on electromagnetic spectrum data mining in the embodiment can identify the characteristics of social group behaviors by analyzing the frequency band occupancy rate of radio communication used by a group, and the information can be used for social management and decision-making work such as people flow management, resource allocation and the like, so that the work efficiency is improved. Compared with the prior art, the social group behavior identification method based on electromagnetic spectrum data mining in the embodiment does not need to rely on identification of data content contained in wireless signals, identification can be completed only by analyzing a physical layer, the monitoring process cannot cause interference to a communication process and a radio environment, communication operators or users are not needed on the premise of legal implementation, and the risk of invading privacy due to data interception is avoided, so that the implementation difficulty is low.
Drawings
FIG. 1 is a flowchart of a social group behavior recognition method based on electromagnetic spectrum data mining according to an embodiment;
FIG. 2 is a schematic diagram of a social group behavior recognition method based on electromagnetic spectrum data mining in an embodiment;
FIG. 3 is a schematic diagram of a social group behavior distribution monitoring platform based on electromagnetic spectrum data mining in an embodiment;
fig. 4 is a schematic diagram of the main operating frequency bands of the first mobile communication operator in the embodiment;
FIG. 5 is a diagram illustrating the main operating bands of a second mobile communication carrier in an embodiment;
fig. 6 is a schematic diagram of the main operating frequency bands of a third mobile communication operator in the embodiment;
fig. 7 is a schematic diagram illustrating the effect of applying the social group behavior recognition method based on electromagnetic spectrum data mining to a plurality of regions in the embodiment.
Detailed Description
In this embodiment, referring to fig. 1, the method for identifying social group behaviors based on electromagnetic spectrum data mining includes the following steps:
s1, acquiring the frequency band occupancy rate of radio communication performed in a first area;
s2, acquiring characteristic data of social group behaviors in a first area;
s3, extracting an optimal feature subset from the feature data;
s4, determining a correlation between the frequency band occupancy rate and the optimal feature subset through a geographical weighted regression model;
s5, performing time-varying behavior analysis on the optimal feature subset through a time sequence model to obtain a first time sequence;
s6, according to the correlation between the frequency band occupancy rate and the optimal feature subset and the first time sequence, constructing a social group behavior prediction model by using a deep learning model;
and S7, using the social group behavior prediction model to identify the social group behaviors.
The principle of steps S1-S7 is shown in FIG. 2.
Step S1, namely the step of acquiring the frequency band occupancy rate of the radio communication performed in the first area, specifically includes the following steps:
s101, determining the position of a first area where a signal source is located through an AOA/TDOA algorithm;
s102, in a first area, acquiring electromagnetic spectrum monitoring data by monitoring mobile wireless communication equipment;
and S103, calculating the frequency band occupancy rate according to the electromagnetic spectrum monitoring data.
Before executing steps S101-S103, data preprocessing may be performed to convert the existing spectrum data into a format easy to calculate, retain necessary information, and remove redundant information, so that the format normalization of the spectrum data can be realized, the storage space is reduced, and the subsequent calculation speed is increased. The flow and principle of steps S101-S103 are: in step S101, performing region classification on the obtained spectrum data, locating a spatial position of a signal source, adjusting parameters related to the signal source through an AOA/TDOA algorithm, and obtaining spectrum data belonging to a specific region in combination with assistance of geographic information, so as to obtain high-precision regional social group behaviors, where the signal source may be a mobile phone, a tablet computer, a bluetooth headset, a smart bracelet, or other device using electromagnetic wireless communication, and the first region refers to a specific region, for example, a region where a certain specific crowd uses the mobile phone, the tablet computer, the bluetooth headset, the smart bracelet; in step S102, the electromagnetic spectrum monitoring data may be acquired by a communications carrier, or the mobile wireless communications device may be monitored by using an instrument set by a third party without the communications carrier, so as to acquire the electromagnetic spectrum monitoring data, and the frequency band occupancy rate of the specific region, that is, the first region, is calculated according to the spectrum data in the region.
Step S2, namely the step of obtaining the characteristic data of the social group behavior in the first area, specifically includes the following steps:
s201, measuring crowd activity track data in a first area;
s202, carrying out relevance analysis, factor analysis or clustering analysis on the crowd activity track data, and taking the obtained result as characteristic data.
The principle of steps S201-S202 is that: the data of social group behaviors in a specific area, such as crowd activity track data, can be measured in advance through a satellite positioning system or a base station positioning system installed on a mobile phone, and then characteristic data which is obviously related to the social group behaviors is extracted through correlation analysis, factor analysis, a cluster analysis method and the like, so that frequency spectrum data characteristics which are obviously related to the social group behaviors can be obtained.
Step S3, namely, the step of extracting the optimal feature subset from the feature data specifically includes the following steps:
and S301, performing dimension reduction processing on the feature data by using a principal component analysis method to obtain an optimal feature subset.
The feature data is further subjected to dimensionality reduction by adopting a principal component analysis method to obtain an optimal feature subset, the optimal feature subset is used in the subsequent steps, the feature data is not used any more, and the calculated amount of a training model can be reduced.
In step S4, correlation analysis is performed on the frequency band occupancy degree and the optimal feature subset through the geographic weighted regression model, so as to determine a correlation relationship between the frequency band occupancy degree and the optimal feature subset. The optimal characteristic subset is extracted from the characteristic data, the optimal characteristic subset comprises the characteristic information of the social group behaviors, and the frequency band occupation degree is measured from the radio communication used by the crowd and comprises the phenomenon information of the social group behaviors, so that the correlation between the frequency band occupation degree and the optimal characteristic subset represents the corresponding relation between the phenomenon information and the characteristic information of the social group behaviors. In step S4, the correlation between the occupancy rates according to the frequency bands and the optimal feature subsets may also be visualized through a geographic information system.
In step S5, a time-varying behavior analysis is performed on the optimal feature subset through a time series model, so as to obtain a first time series. In step S5, a time series model is used to model social group behaviors in a specific area for time-varying behavior analysis, wherein the selection of the specific area may use city POI data, i.e., to select a socially significant geographic location, such as a mall, a school, a sight spot, etc. The obtained first time series includes a plurality of data having the same properties as the optimal feature subset.
Step S6, namely, the step of constructing a social group behavior prediction model using a deep learning model according to the correlation between the frequency band occupancy and the optimal feature subset and the first time sequence, specifically includes the following steps:
s601, determining a second time sequence corresponding to the first time sequence according to the correlation between the frequency band occupancy rate and the optimal feature subset;
s602, taking the second time sequence as the input of the deep learning model, taking the first time sequence as the expected output of the deep learning model, and training the deep learning model;
and S603, taking the trained deep learning model as a social group behavior prediction model.
In step S601, according to the data with the same properties as the optimal feature subset included in the first time sequence and the correlation between the frequency band occupancy rate and the optimal feature subset, data with the same properties as the frequency band occupancy rate may be obtained, and these data form a second time sequence. According to the nature of the data contained in the first time series and the second time series, the second time series can be used as the input of the deep learning model, and the first time series can be used as the expected output of the deep learning model to train the deep learning model. Specifically, a Recurrent Neural Network (RNN), a Graph Neural Network (GNN), a decision tree model, a random forest model, a markov model, an ARMA model, or the like may be used as the deep learning model. When the deep learning model is trained, data in the second time sequence can be input into the deep learning model, the data is processed by the deep learning model and then output, the output result of the deep learning model is compared with the expected output, parameters of the deep learning model are reversely transmitted according to errors, and the deep learning model is stopped from being trained until the error between the output result of the deep learning model and the expected output is smaller than a preset threshold value.
The trained deep learning model is the social group behavior prediction model in the embodiment, and the obtained social group behavior prediction model has the capability of identifying the social group behavior characteristics according to the frequency band occupancy degree.
Step S7, namely, the step of using the social group behavior prediction model to perform social group behavior recognition, specifically includes the following steps:
s701, acquiring the frequency band occupancy rate of radio communication performed in a specific area within a period of time;
s702, inputting the frequency band occupancy rate into a social group behavior prediction model;
and S703, determining the characteristics of the social group behaviors according to the output result of the social group behavior prediction model.
The specific region in step S701 may refer to a city, a country, or even the world, and the period of time may refer to a fixed time such as a specific year, month, and day, or may refer to a duration of a certain event. For example, when step S701 is executed, the time and place where a short-term significant event occurs may be selected for analysis, such as a concert held in a certain city, a traffic jam occurring on a certain road, and the like, and changes of electromagnetic data and social group behaviors under these scenes are observed; or selecting a longer special period, such as a global infectious disease pandemic period, an urban heat wave duration period and the like for analysis.
By executing steps S701 to S703, the characteristics of the social group behaviors of a region may be obtained according to the frequency band occupancy degree generated when the region uses a mobile phone or other wireless communication device within a period of time, wherein the content of the characteristics of the social group behaviors is the same as the characteristic data used in step S2 when the social group behavior prediction model is obtained by performing the training of steps S1 to S6, and based on the recognition result of the characteristics of the social group behaviors, appropriate opinions and suggestions are provided, such as people flow dispersion decision arrangement, and the like. The method can be used for analyzing social group behaviors through electromagnetic spectrum data and has multiple purposes. If the analysis precision is accurate enough, the method can be used for searching and rescuing trapped people in disaster areas after disasters such as earthquake and the like occur. By accumulating electromagnetic spectrum data for a long period of time in a certain area, when a major emergency occurs, the influence caused by the event can be analyzed. Similarly, if the electromagnetic spectrum data of a certain area is detected to be abnormal in a larger range, the area can be investigated to judge whether a special event occurs.
The social group behavior recognition method in this embodiment can be applied to the monitoring platform shown in fig. 3, and the social group behavior distribution monitoring platform based on electromagnetic spectrum data mining analyzes spectrum data by combining multi-dimensional information of time domain, frequency domain and spatial domain, and can provide prior knowledge for radio. Through the construction and distribution of the platform, a radio supervision department can realize effective spectrum management, and the social group behavior distribution of the region is reflected laterally by calculating the occupancy rate of a target frequency band.
Referring to fig. 4, 5, and 6, in this embodiment, the main operating frequency bands of three mobile communication carriers, namely, a first mobile communication carrier, a second mobile communication carrier, and a third mobile communication carrier in china, are selected as target frequency bands to explain the application of the social group behavior identification method. Fig. 4 shows the main operating frequency band of a first mobile communication carrier, fig. 5 shows the main operating frequency band of a second mobile communication carrier, and fig. 6 shows the main operating frequency band of a third mobile communication carrier.
The social group behavior recognition method in the embodiment is applied to regions such as Wenchang city, Suiyuan city, Lingshui city, Yanghui city and eastern city in Hainan province of China, and social group behaviors in the regions at a certain time period are recognized. The frequency band occupancy of the nearby target frequency band is monitored at the monitoring points in the above-mentioned region, so that step S1 of the social group behavior identification method in the present embodiment is executed. Specifically, the monitoring points located in the areas such as the haikou city, the near-high county, the east city and the like are mainly located in residential places such as a residential district, a dormitory and the like, and the monitoring points located in the autonomous counties such as the wenchang city, the san city and the tomb water are mainly located in places such as the centers of the wenchang dragon building, the san libuan road and the tomb water autonomous counties. Different color depths are used for representing the occupancy rate of the target frequency band in the corresponding region, the darker the color is, the higher the utilization rate of the target frequency band in the region is, and the larger social group behaviors in the region are further reflected from the side.
Fig. 7 shows the analysis results obtained by applying the social group behavior recognition method in this embodiment to the above-described region. As can be seen from fig. 7, the frequency band occupancy of the wenchang city, the san city, and the mausoleum county is slightly higher than the frequency band occupancy of the haikou city, the near-high county, and the east city. According to the principle of the social group behavior recognition method in the present embodiment, it is considered that the social group characteristics of the city of Wenchang, the city of Suiyuan and the autonomous region of the Ling water shown in FIG. 7 are more obvious than the social group characteristics of the city of Haikou, the city of Bighua and the city of Orient. Since the monitoring points in the areas such as the haikou city, the lingao county, the eastern city and the like are mainly located in the residence such as the residential quarter, the dormitory and the like, the social group behaviors of the places are relatively inactive, and the monitoring points in the wenchang city, the san franchise city and the mausoleum are mainly located in the places such as the wenchang dragon building, the san libitum way and the mausoleum county center and the like, the social group behaviors of the places are relatively active, so that the conclusion obtained by the social group behavior identification method in the embodiment is consistent with the actual situation, which proves the feasibility of the social group behavior identification method in the embodiment.
The method for identifying the social group behaviors based on the electromagnetic spectrum data mining can write a computer program according to the method for identifying the social group behaviors based on the electromagnetic spectrum data mining, write the computer program into a memory or an independent storage medium of a computer device, and instruct a processor to execute the method for identifying the social group behaviors based on the electromagnetic spectrum data mining after the computer program is read out, so that the technical effect which is the same as that of the embodiment of the method is realized.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. A social group behavior identification method based on electromagnetic spectrum data mining is characterized by comprising the following steps:
acquiring a frequency band occupancy rate of radio communication performed in a first area;
acquiring characteristic data of social group behaviors in the first area;
extracting an optimal feature subset from the feature data;
determining a correlation between the frequency band occupancy and the optimal feature subset through a geographical weighted regression model;
performing time-varying behavior analysis on the optimal feature subset through a time series model to obtain a first time series;
according to the correlation between the frequency band occupancy and the optimal feature subset and the first time sequence, a deep learning model is used for constructing a social group behavior prediction model;
and performing social group behavior identification by using the social group behavior prediction model.
2. The method for social group behavior recognition based on electromagnetic spectrum data mining as claimed in claim 1, wherein the obtaining of the frequency band occupancy of the radio communication performed in the first area comprises:
determining the position of the first area where the signal source is located through an AOA/TDOA algorithm;
acquiring electromagnetic spectrum monitoring data by monitoring a mobile wireless communication device in the first area;
and calculating the frequency band occupancy rate according to the electromagnetic spectrum monitoring data.
3. The method for identifying social group behaviors based on electromagnetic spectrum data mining as claimed in claim 1, wherein said obtaining characteristic data of social group behaviors in the first area comprises:
measuring crowd activity trajectory data in the first region;
and carrying out correlation analysis, factor analysis or clustering analysis on the crowd activity track data, and taking the obtained result as the characteristic data.
4. The method for social group behavior recognition based on electromagnetic spectrum data mining as claimed in claim 3, wherein the extracting an optimal feature subset from the feature data comprises:
and performing dimensionality reduction on the feature data by using a principal component analysis method to obtain the optimal feature subset.
5. The method for identifying social group behaviors based on electromagnetic spectrum data mining as claimed in claim 1, wherein the constructing a social group behavior prediction model using a deep learning model according to the correlation between the frequency band occupancy and the optimal feature subset and the first time series comprises:
determining a second time sequence corresponding to the first time sequence according to the correlation between the frequency band occupancy and the optimal feature subset;
training the deep learning model by taking the second time series as the input of the deep learning model and taking the first time series as the expected output of the deep learning model;
and taking the trained deep learning model as the social group behavior prediction model.
6. The method of identifying social group behaviors based on electromagnetic spectrum data mining of claim 5, wherein the deep learning model is a recurrent neural network, a graph neural network, a decision tree model, a random forest model, a Markov model, or an ARMA model.
7. The method for social group behavior recognition based on electromagnetic spectrum data mining as claimed in claim 5 or 6, further comprising:
and visualizing the correlation between the occupancy rate of the frequency band and the optimal feature subset through a geographic information system.
8. The method for social group behavior recognition based on electromagnetic spectrum data mining as claimed in claim 1, wherein the social group behavior recognition using the social group behavior prediction model comprises:
acquiring the frequency band occupancy rate of radio communication performed in a specific area within a period of time;
inputting the frequency band occupancy into the social group behavior prediction model;
and determining the characteristics of the social group behaviors according to the output result of the social group behavior prediction model.
9. A computer apparatus comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method for electromagnetic spectrum data mining based social group behavior recognition of any of claims 1-8.
10. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to perform the method for identifying social group behaviors based on electromagnetic spectrum data mining of any one of claims 1 to 8.
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