CN114745465A - Interactive noise self-prior sensing analysis system for smart phone - Google Patents

Interactive noise self-prior sensing analysis system for smart phone Download PDF

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CN114745465A
CN114745465A CN202210296620.9A CN202210296620A CN114745465A CN 114745465 A CN114745465 A CN 114745465A CN 202210296620 A CN202210296620 A CN 202210296620A CN 114745465 A CN114745465 A CN 114745465A
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马斌斌
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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Abstract

The application provides a data acquisition and reconstruction method based on interactive self-prior sensing of a smart phone, and aims at the problems of high data redundancy, high energy consumption in the processing process and the like in an interactive sensing system, the self-prior sensing is integrated into an interactive sensing noise detection system, the whole environment noise data information is reconstructed by using a small amount of noise data, the data acquisition amount is effectively reduced, and in order to improve the accuracy of data acquisition, a coarse-grained audio mode dynamic evaluation method is provided for conjecturing the application scene of a participant in the sensing task process before the noise data is acquired; an environment noise interactive sensing system is designed and realized, the redundancy of noise data is solved, the credibility of the data is guaranteed, the individual privacy is protected, the acquisition efficiency is improved, participants visually acquire the noise distribution condition of a specific area through a thermal map, and the environment noise interactive sensing system has a great effect on noise monitoring and analysis.

Description

Interactive noise self-prior sensing analysis system for smart phone
Technical Field
The application relates to an interactive noise perception analysis system of a smart phone, in particular to an interactive noise self-prior perception analysis system of a smart phone, and belongs to the technical field of interactive noise perception analysis.
Background
With the increasing coverage of smart phones, interactive perception, which is a perception data acquisition mode for completing perception tasks through different individuals, is produced. The participants acquire and store data meeting the requirement of the sensing task through the smart phone end sensor, the acquired data are processed and pushed to the server, and the server can integrate and analyze all uploaded data to obtain a corresponding result. And finally, feeding back the analyzed result to the participant for use. At present, interactive perception is applied to certain applications, such as driving route planning, environmental pollution monitoring and the like.
The interactive sensing can be regarded as extension and expansion application of a wireless sensing network, the theory and technology development of the wireless sensing network is relatively mature, but a certain distance is still left between the development of the wide urban sensing application, wherein the large-scale application of the sensing network is limited by the networking cost and the cost of the sensing network, and the sensor type arranged in the sensing network has strong application correlation, so that the flexibility and the reusability are lacked for the application of different sensing types.
Compared with a wireless sensing network, the interactive sensing has the following characteristics: first, the deployment of the interactive sensing nodes may be static or dynamic, and the sensing range may be flexibly adjusted according to different sensing tasks. Secondly, interactive perception is centered on people, participants are not only providers of perception data, but also can share results brought by data shared by other participants, and the whole perception scale and the demands are constantly changed along with the change of the demands of people.
In the interactive perception process, the whole processing process of the data is very important, and the processing process of the data is mainly divided into four parts: data acquisition, data analysis, data uploading and data pushing.
The side effect of noise pollution is not to make the chignon small, which not only harms the sense of hearing, but also affects other physiological tissues of human body. With the rapid increase of the urban population and the industrial manufacturing scale, the noise pollution is more and more widely concerned, and the effective and scientific real-time monitoring of the noise pollution is more meaningful.
In order to monitor the noise pollution condition more accurately in real time, the distribution of the noise in time and space needs to be known. The intensity of the noise is varied in real time, and the intensity of the noise may vary greatly at different times at the same location. The noise map is used for showing the noise distribution situation, the noise distribution situation of a geographical area is drawn on the map, the change situation of noise along with time and space is reflected, a real-time noise map is established, the intensity of noise of each position in a certain range is shown, the noise map is updated in real time, data and information are provided for noise pollution treatment in an area, and the noise map is also an important component of a data-based city. The method has great significance for environmental governance of cities and noise analysis of regions.
In the existing environmental data acquisition system, sensor nodes are mostly required to be preset, the sensor nodes are preset near the existing standard noise meter to calibrate the acquired noise data, the system transmits the acquired data to a server through a sensor network, and finally, a noise distribution map is drawn by the server terminal for participants to use. Most of the existing environmental data acquisition systems face the situation that when the monitoring range is enlarged, a large amount of sensor equipment needs to be arranged, so that the overhead is undoubtedly increased, and the realization of fine-grained monitoring is also difficult. In addition, during monitoring, dynamic changes are difficult to exist for the monitoring range and the monitoring data type.
The interactive perception is a new perception mode, similar concepts of the interactive perception include crowdsourcing, crowd sensing and the like, and the crowdsourcing, the crowd sensing and the crowd sensing are all used for actively or passively collecting information of the surrounding environment by using a participant to hold a mobile device so as to achieve the perception purpose. The interactive perception system is divided into three categories, namely a public perception category, a social perception category and a personal perception category according to the difference of perception types and the difference of perception ranges.
In the data acquisition of the existing interactive perception system, the following centralized data acquisition types mainly exist: some systems directly allow all participants to upload information, and have a large amount of redundancy in acquisition, transmission and storage, which is not favorable for encouraging the participants to participate, and is not suitable for large-scale application. Some systems provide the point of maximizing the acquisition efficiency of the system while minimizing the energy consumption of the system from the perspective of analyzing the motion trajectory similarity of participants and the fairness of allocating sensing tasks, so that the energy consumption of the system is reduced, but the consideration of generating data redundancy in the acquisition process is lacked. Data redundancy is considered by a part of interactive sensing systems, the systems mostly adopt a synchronous communication mode, the positions of participants are regarded as known conditions, requirements on the positions and the acquisition capacity of the participants are high, but the consideration on energy consumption of equipment of the participants is lacked, and the systems are difficult to popularize and apply.
Interactive sensing systems and applications are more and more diverse, but are far from practical large-scale applications, and due to the advantages of the interactive sensing systems, the interactive sensing systems are expected to be widely popularized and applied in the future. However, there still exist some technical problems in this field that need to be further solved, such as how to solve redundancy of data, how to guarantee credibility of data, how to protect privacy of individuals, how to improve collection efficiency, and the like.
In summary, the noise collection and analysis system in the prior art still has several problems and defects, and the difficulties and problems to be solved in the present application mainly focus on the following aspects:
first, most of the existing noise environment data acquisition systems face a large number of sensor devices, which increases overhead undoubtedly, and there is a great difficulty in implementing fine-grained monitoring. In addition, in the monitoring process, dynamic changes are difficult to exist in the monitoring range and the monitoring data type, particularly the deployment cost of the noise monitoring wireless sensing network is high, the application and development are inconvenient, the monitoring area cannot be dynamically adjusted and the monitoring task cannot be allocated, and a dynamic system for fully utilizing a widely-used smart phone to collect and analyze noise is lacked in the prior art;
secondly, the application of the interactive sensing system in the prior art to the environmental noise collection and analysis also has problems: firstly, all participants are directly allowed to upload information, and a large amount of redundancy exists in acquisition, transmission and storage, so that participation of the participants is not encouraged, and the method is not suitable for large-scale application; secondly, corresponding measures are lacked in the aspects of motion track similarity of participants and fairness of allocating sensing tasks, the interactive sensing system is high in energy consumption, data redundancy is high in the noise acquisition process, and energy consumption is high in the processing process; thirdly, the requirements on the position and the acquisition capacity of the participant are too high, and the energy consumption and the performance of equipment of the participant are not considered; the interactive sensing system is far away from the actual large-scale application, cannot solve the redundancy of data, guarantee the credibility of the data, cannot protect the personal privacy and improve the acquisition efficiency, and is difficult to popularize and apply in the field of noise environment acquisition and analysis;
thirdly, the prior art lacks a noise analysis system facing environment perception, lacks a technical means for realizing acquisition, processing and compression transmission of noise data based on a smart phone, and participants in the system intuitively obtain the noise distribution condition of a concerned area by themselves in the form of a thermal map, and the smart phone mobile terminal is key to reduce energy consumption and reduce data transmission quantity in the whole system due to limited resources, but the prior art has no effective measures; aiming at the problems of high data redundancy, high energy consumption in the processing process and the like in an interactive perception noise detection system, no effective solution is provided, the whole environment noise data information cannot be reconstructed by using a small amount of noise data, and the data acquisition amount is large; before the noise data is collected, a method for speculating the application scene of a participant in the task sensing process is lacked, the precision of the collected data is low, the collection and reconstruction of interactive noise data are difficult, and the method cannot be popularized to practical application;
fourthly, noise collection and analysis in the prior art lacks a high-quality environment noise interactive sensing system, lacks the overall design of a noise collection and analysis system, lacks the design of a client system, cannot complete sensor data acquisition, map service plate design, noise map generation and lacks the design of a server system; the client lacks a smart phone carrying an intelligent operating system, noise data are collected and compressed, and the data are sent to the server, so that a participant cannot check a noise map at the mobile phone; the server side lacks a Web service mode based on an REST framework, lacks a method for receiving data sent from the client side, processing and reconstructing the data, realizes processing of interactive sensing data and conjecture of participant states, and finally feeds back reconstruction results to the client side, so that visualization of noise data cannot be realized, and noise volume and distribution conditions of sensing positions cannot be observed visually.
Disclosure of Invention
The application provides data acquisition and reconstruction based on interactive self-prior sensing of a smart phone for environment sensing, and aims at the problems of high data redundancy, high energy consumption in the processing process and the like in an interactive sensing system, the self-prior sensing is integrated into an interactive sensing noise detection system, the whole environment noise data information is reconstructed by using a small amount of noise data, the data acquisition amount is effectively reduced, and in order to improve the accuracy of data acquisition, a coarse-grained audio mode dynamic evaluation method for coarse granularity is provided for conjecturing the application scene of a participant in the sensing task process before the noise data is acquired; an environment noise interactive perception system is designed and realized, a client uses a smart phone carrying an intelligent operating system and is responsible for collecting and compressing noise data and sending the data to a server, a participant can check a noise map at the mobile phone, the server adopts a Web service mode based on an REST framework and is responsible for receiving the data sent from the client, processing and reconstructing the data, and finally feeding back a reconstruction result to the client, so that the redundancy of the noise data is solved, the credibility of the data is guaranteed, the personal privacy is protected, the collection efficiency is improved, the participant in the system intuitively obtains the noise distribution condition of a region concerned by the participant in a form of a thermal map, and the system has huge practical significance and wide application prospect for noise monitoring and analysis;
in order to achieve the technical effects, the technical scheme adopted by the application is as follows:
an interactive noise self-prior sensing and analyzing system of a smart phone faces to environment sensing, noise interactive analysis is carried out on the basis of the smart phone, self-prior sensing is applied to an interactive sensing system, noise data acquisition, processing and compression transmission are achieved through design and development of a smart phone end and a server end, participants in the system visually obtain the noise distribution condition of a concerned area through a thermal map, and an interactive self-prior sensing noise detection system is achieved;
first, interactive self-a priori aware data acquisition and reconstruction, comprising: firstly, interactive noise data acquisition, secondly, adaptive noise data compression and thirdly, noise self-prior reconstruction;
before noise data are collected, a coarse-grained audio modal dynamic evaluation method is adopted to speculate the application scene of a participant in the task sensing process, so that the accuracy of the collected data is improved; based on the problems of more noise data redundancy and large energy consumption in the processing process in the interactive sensing system, the method puts forward the method that the self-prior sensing is integrated into the interactive sensing noise detection system, and reconstructs the whole environment noise data information by using a small amount of noise data to reduce the data acquisition amount;
secondly, designing and realizing an environmental noise interactive perception system, wherein the system is designed as a whole, the client system is designed, the system comprises sensor data acquisition, map service plate design and noise map generation, and the server system is designed;
the client side uses a smart phone carrying an intelligent operating system to complete collection and compression of noise data and send the data to the server side, and participants check a noise map at the phone side; the server side receives the data sent from the client side by adopting a REST framework-based Web service mode, processes and reconstructs the data, realizes the processing of the interactive perception data and the conjecture of the participant state, and finally feeds back the reconstruction result to the client side.
The smart phone interactive noise self-prior perception analysis system further comprises an interactive noise data acquisition unit: performing coarse-grained audio modal dynamic evaluation on a use scene and a use mode of the smart phone, dividing participants into a static type and a dynamic type according to the motion states of the participants, dividing the participants into a closed space and an open space according to the space where the mobile phones of the participants are located, placing the mobile phones in indoor drawers, determining that the participants are the closed space in a static state, determining that noise data acquired by the mobile phones are relatively small, and cannot truly reflect real-time noise data, wherein the data reliability is low in the state; when the participants are in a motion state, the mobile phone is exposed outdoors, and if the participants adopt high acquisition frequency, noise data of space-time characteristics can be better reflected, and discrete and uninterrupted acquisition areas are avoided;
when judging the motion state of the mobile phone, an acceleration sensor in the mobile phone is adopted, and the acceleration sensor of the mobile phone acquires the acceleration data change of the mobile phone in three moving directions, wherein the X axis represents the acceleration in the left-right moving direction, the Y axis represents the acceleration in the front-back moving direction, and the Z axis represents the acceleration in the vertical moving direction; when a participant carrying intelligent mobile terminal equipment is in a motion state, the accelerations of three axes all have certain changes, but the change rules are not obvious, and the acceleration change rules of each axis are different when the motion postures are different, if the acceleration change on each axis is analyzed, the algorithm complexity is increased, and a new acceleration intensity resultant vector CNU is introduced as a characteristic quantity:
Figure BDA0003563703470000051
ax,ay,azthe acceleration in three directions is respectively, the CNU characteristic quantity is utilized to avoid the complexity caused by analyzing the acceleration in three axes, the CNU value has larger change no matter what motion state, and the CNU is used as the motion state judgment basis;
when the participant is in a certain motion state, the CNU has a change process from a minimum value to a maximum value, when the participant is in a static state, the CNU area is stable, the CNU area is between a gravity acceleration g, 1.1g is set as a judgment critical value in the method, and the average CNU judgment of the participant 3 seconds after the start of a perception task is calculated: when CNU is less than 1.1g, the participator is in a steady state; when CNU is more than or equal to 1.1g, the participant is in a motion state;
when judging whether the mobile phone is in a closed space, judging the space where the mobile phone is located by using data read by the light pulse proximity sensor, and acquiring parameters by using values [0], wherein when the distance is less than 1.6cm, the value of values [0] is 0, and when the distance is more than or equal to 1.5cm, the value of values [0] is 5;
analyzing data acquired by the acceleration sensor and the light pulse proximity sensor, estimating the use scene of the participant in a coarse-grained manner, and screening noise data provided by the participant according to the use scene;
the evaluation of the noise volume is finished through a mobile phone end, when client software is started, the microphone of the mobile phone end senses the sound around and calculates a decibel value, meanwhile, the software acquires current position information, namely longitude and latitude, through positioning and acquires the current time section, namely a triple (a timestamp, the longitude and latitude and the decibel value) is acquired, the triple is stored in the mobile phone to obtain a noise data record, the mobile phone end only measures the surrounding noise data without recording the sound around a participant, a recording file is not stored, and the privacy of the participant is protected;
the noise information is collected based on the position of the participant, a target area G is selected, the area G is divided into N parts, each part is a noise measurement unit, and the obtained data information x in each noise measurement unit is collectedi(i ∈ {1,2,3, …, N }) into a vector form, representing the noise pollution situation in this target area:
x＝(x1，x2，...xN)T formula 2
x is a data complete set, and initial data x is reconstructed according to incomplete and random measurement data.
The interactive noise self-prior perception analysis system of the smart phone, further, self-adaptive noise data compression: in a certain time period T, a participant collects noise data in an area required by a perception task, and the participant collects a string of data vs,vsOnly partial noise measurement data, i.e. noise decibel value, position data information and time cut information, are included, firstly, v is measuredsEncoding into a vector y of dimension Ms，ys＝BsvsWherein B is observed in the matrixsSetting of (1): b issIn is one M × LsContains only three elements of-1, 0,1, k' ≈ 100:
Figure BDA0003563703470000061
observation matrix BsGenerated by a pseudo-random number generator on each participant's mobile phone, and the generated pseudo-random number generator is used by the participant's mobile phone endThe observation matrix realizes the dimensionality reduction of the acquired data, only the vector subjected to dimensionality reduction is sent to the server side, the transmission flow is reduced, and during reconstruction, the server side only needs a pseudo-random number generator identical to that of the mobile phone side, and the observation matrix can be generated in the server section and is consistent with that of the mobile phone side;
the data obtained by all n participants are superposed, so that there is y*＝B*x*Wherein x is*Is all of vsSuperposition of (2):
Figure BDA0003563703470000062
Figure BDA0003563703470000063
B*is a P x Q sparse sampling matrix,
Figure BDA0003563703470000064
x' has data superposition and deletion, and in order to restore the original x, B is paired according to the sequence of elements in x*The reconstruction is performed such that:
y CE BGx formula 6
Where C is the perceptual matrix and E is a sparse representation of x over the discrete cosine transform matrix G.
An interactive noise self-prior sensing analysis system of a smart phone, further, the noise self-prior reconstruction comprises the following specific steps:
defining: inputting: observation base B, observation noise y, sparsity k,
and (3) outputting: x is a noise signal s reconstructed by k sparsity;
initialization: residual margin r of observed noise0Reconstructing the signal s as y 00, index set Tn＝Tn-1U { k }, the iteration number n is 0;
the first step is as follows: calculating the residual margin of the observation noise and the inner product g of each column of the observation base Bn＝BTr n-1；
The second step: find out gnJ ═ argmax | gn[i]|；
The third step: updating index set Tn＝Tn-1Y[j]And indexing observation base BT,Y[*]Updating a function for the index;
the fourth step: approximate solution x solved by least square methodn＝(BT T,BT)-1BT Ty；
The fifth step: updating residual margin of observation noise, rn＝y-GsnWhere, the iteration number n is n +1, and G is a discrete cosine transform matrix;
and a sixth step: if n > k and a convergence condition is satisfied, stopping the iteration, sr＝sn，r＝rnOutput sr，snOtherwise, turning to the first step;
and (3) giving a discrete K sparse signal X, wherein the length N of the signal X is changed, each curve represents different N values, N is 50,100 and 500, the normalized sparsity s of the noise signal X is K/N is 0.1, the oversampling rate c is the ratio of the noise measurement number M to the noise signal sparsity K, and the observation matrix adopts a random Gaussian matrix.
The interactive noise self-prior perception analysis system of the smart phone, further, the overall design of the system: the intelligent mobile phone end acquires a noise decibel value, position information and time information, the noise decibel value, the position information and the time information are uploaded to the server end through the communication module, meanwhile, the noise information acquired by a participant is displayed in real time, after the participant sends a request, the server end feeds collected noise data back to the participant, the noise data are displayed in a noise map mode at a client end, the server end receives and sends the data through the communication module, and data processing is carried out through the data management module;
based on a smart phone, noise data are obtained through an embedded sensor and visualization of the noise data is completed, a server side is used as a data processing center of the system and mainly used for receiving and storing data and carrying out other necessary processing on the server side, data collected by participants are concentrated on the server side to carry out noise self-prior reconstruction, and the server is communicated with a MySQL cluster through a data management module.
The interactive noise self-prior perception analysis system of the smart phone, further, the client system design: adopt smart mobile phone to accomplish data acquisition, storage, upload, the client mainly includes: firstly, sensing data is acquired, wherein the sensing data comprises noise information, positioning information, acceleration sensor information, proximity sensor information and timestamp information; secondly, the client communicates with the server, the client sends the acquired data information to the server and receives a feedback result of the server; thirdly, data visualization is realized, noise data fed back to the client side by the server side is visualized in a thermal map mode, and participants can observe surrounding noise distribution conditions visually;
the smart phone provides use support for the equipment sensor, provides a driver program to manage sensor hardware, and monitors the change of the external environment sensed by the sensor hardware in a monitor mode, wherein the specific steps of the smart sensor development are as follows:
step 1: acquiring sensor service: the participator clicks a button of 'start collecting' to trigger the collecting process, and a Sensor Manager is initialized by applying;
step 2: acquiring a specified type of sensor: selecting a Sensor to be adopted, acquiring and adding a specified Sensor to be monitored by adopting a Sensor Manager;
and 3, step 3: registering a listener: after the Sensor is obtained, a Sensor Manager is adopted to add a registered monitor to the target Sensor, when the Sensor senses the change of the environment, the monitor returns the value of the Sensor, and meanwhile, the frequency of the Sensor acquisition is set in the monitor;
and 4, step 4: and (3) data calculation and storage: after the sensor data are obtained, the sensor data are processed and stored persistently;
the noise detection of the client terminal utilizes a microphone sensor in the mobile phone, the unit of the sound intensity is dB, and the noise intensity data acquisition calculation formula is as follows:
Figure BDA0003563703470000081
Prmsis the measured sound pressure, PrefA sound pressure that is a reference value;
the method comprises the following steps of obtaining participant position information at a noise detection system client by adopting a third party map API, and obtaining a specific longitude and latitude information flow:
1) downloading the SDK of the third-party map and carrying out XML configuration related preparation work:
2) setting a positioning condition, and judging whether setOpegps opens positioning and returns a coordinate type setCoorType of a value;
3) register listener registrar locationiLister;
4) adopting StringBuffer to store the acquired data, wherein getTime acquires time, getLatitude acquires latitude, and getLongitude acquires longitude;
5) sending a request, enabling a monitor to work, and acquiring time and longitude and latitude data;
the thermal map contained in the third-party map is adopted to represent the noise distribution situation, and the noise information thermal map is displayed and realized as follows:
1) setting a map state by using setMapStatus ();
2) adding thermodynamic diagram acquisition data List < data > and thermodynamic diagram construction method public Heat map Builderdata () in addHeatMap ():
3) acquiring data in JSONArray by adopting a List < data > getLocations () method, wherein the method waits to be called in addHeatMap ();
4) the mobile phone end sends a request to the server end;
5) the mobile phone end displays a noise map;
6) the method for deleting the thermodynamic diagram comprises the following steps: removeheatmap ().
The smart phone interactive noise self-prior perception analysis system further comprises the following steps of: the core Sensor Data acquisition module is a Sensor Data Collection, a proximity Sensor, an accelerometer and a Sensor Event Listener interface, the Sensor Event Listener interface is adopted to realize the acquisition of real-time Data of the Sensor, the Location Listener interface of a third-party map is adopted to realize the acquisition of real-time geographic position information, and d BSensor class recorded sound is adopted to obtain the maximum amplitude;
(1) initializing the SensorManager to obtain through get System Service (context. SENSOR _ SERVICE);
(2) acquiring acceleration Sensor data through get Default Sensor (Sensor, TYPE _ ACCELEROMER);
(3) register Listener real-time acquisition data, register Listener (Listener, Sensor manager. Sensor _ DELAY _ NORMAL);
(4) the method of duplicating on Sensor Changed (Sensor Event) obtains Sensor data, and the returned data structure is an Event.
(5) After the sensor object is used, releasing sensor resources by adopting a stop () method, and simultaneously logging out sensor monitoring service, namely sensor manager.
The code implementation process for acquiring the noise intensity comprises the following steps: the method for recording the noise by adopting startRecord () of mediarecordermo and the method for stopping the noise acquisition by stoprcordo are adopted, and the specific flow is as follows:
1) instantiating a MediaRecorder object;
2) setting the audio: setting an audio source by adopting setAudioSource (), setting an audio output format by adopting setOutputFormat (), setting an output coding mode by adopting setAudioEncoder (), and setting the position of an output file by adopting setOutputFile ();
3) prepare () is ready for recording;
4) starting () formally starting recording;
5) calling updateMicStatus () to calculate specific audio intensity, sending a numerical value through handle, sendmessage () and carrying out discontinuous delayed sending through postDelayed () to obtain specific audio intensity;
the updateMicStatus () calculation code in flow 5) is as follows: db ═ int ((20 × math.log1o (ratio)) × 0.7), where multiplying 0.7 reduces the influence of extreme noise on the data, and a double type is converted into an int type using a type-forced conversion;
message.what and message.obj data are transmitted to a handler by using Message, the former contains transmitted code identification, the latter is a specific numerical value of noise intensity, and the handler Message is used for receiving data in MainActivity, wherein switch (msg.what) judges the msg.what identification, and after judging the msg.what identification is correct, the specific numerical value in msg.obj is converted into string type by using setText (msg.obj.testring ()) and then displayed, a timer function is realized by using a Runable () interface and a handler.postDelayed (), and the whole updateMicStatus () is operated discontinuously, so that the data timing is updated.
The interactive noise self-prior perception analysis system of the smart phone, further, the map service plate design: the specific process of obtaining the map information comprises the following steps:
1) building a third-party map using environment: firstly, registering in a third-party map developer, then acquiring a secret key, downloading a jar package with a corresponding application function, putting the jar package into a lib file of software, executing updating import in the software, and then adding a corresponding authority and the secret key in manifest.
2) Java sets up the third party call correlation method: the method comprises the steps of registering and deregistering a third-party map by using a regiorstListener () and a regiorstListener (), and realizing the entrusting of positioning by using a start () and stop () method;
3) establishing location application.java initialization positioning and using the location application.java initialization positioning as an initial entrance for calling a map;
4) detecting the noise of the main interface, monitoring the main interface by adopting setOnClickListener (), carrying out map positioning when the main interface is pressed down, and stopping positioning when the main interface is pressed down again;
the method comprises the steps of newly building BDLocationListener in ManActivtyjava, receiving positioning information by adopting onReceiveLocation (), receiving StringBuffer of a string type buffer, then obtaining positioning time by adopting location.
The interactive noise self-prior perception analysis system of the smart phone further comprises a noise map generation step: the noise map adopts a heat map API provided by a third-party map, noise is displayed in a heat map mode in a user-defined heat map mode, a custom task is completed by a HeatMap builder, the visualization of noise data is realized, and the following process of generating the user-defined heat map is as follows:
1) obtaining noise data sent by a server by a List < LatLng > getLocations (), obtaining an input data stream by a scanner (inputStream), storing String type data into an array, traversing the array to obtain longitude and latitude and noise values, and then putting the obtained data into List.
2) Adding Thread () into the newly-built addHeatMap () method, calling getLocations () to acquire server data by adopting an interface run (), setting data to be drawn by adopting a thermodynamic map constructor HeatMap. builder, and adopting List < LatLng > data ═ getLocations ();
3) and after the setting is finished, adopting build () to construct a heat map, adopting sendEmptyMessage (0) to send a numerical value to handlemap (messagemsg) in the build, and after confirming that the value is received 0, adopting addHeatMap (heatmap) to add the heat map to finish the addition and display of the heat map.
An interactive noise self-prior sensing analysis system of a smart phone is further provided, wherein a server end system is designed as follows: the server side is used as a data processing center to complete analysis of the use scene of the participant and data communication of the mobile terminal, complete data receiving, storage and necessary processing required to be carried out at the server side, the server side adopts an REST architecture, and the server side interface adopts URL as a resource identifier to communicate with the client side;
MySQL is adopted as a system database, and an information data layer is composed of two parts: the system comprises a database and a database access layer, wherein the database access layer complies with JPA (Java native application platform) specifications when performing data storage, modification and deletion operations;
the service layer of the Web server side consists of a service interface, an implementation layer and a physical layer, the Web layer consists of two parts, the first part is a service portal layer, the second part is a Web service layer, and the former is responsible for the interface and the latter is used for the data request of the mobile client;
the method comprises the steps that a server-side participant uses scene conjecture, the motion state and the physical position of the smart phone in the participant behavior collecting process are considered, a short-distance sensor and a linear acceleration sensor are adopted, the acceleration sensor is adopted to judge the motion state of the participant, and the short-distance sensor is adopted to judge the state of the participant and the state of the smart phone.
Compared with the prior art, the innovation points and advantages of the application are as follows:
first, this application uses widely used smart mobile phone to noise monitoring in, provides one kind and accomplishes data acquisition's interactive perception jointly through different individualities, and the participant passes through smart mobile phone end sensor collection and accords with the data and the storage that the perception task required, and then handles and push the server to the data of gathering, and the server can carry out the integration analysis to all data of uploading and obtain corresponding result. The arrangement of the sensing nodes can be static or dynamic, and the sensing range can be flexibly adjusted according to different sensing tasks; the method comprises the following steps that participants are providers of perception data and can share results brought by data shared by other participants, the whole perception scale and the requirements change continuously along with the change of the requirements of people, the distribution situation of noise in time and space is accurately obtained, a noise map is used for displaying the noise distribution situation, the noise distribution situation of a geographic area is drawn on the map, the change situation of the noise along with the time and the space is reflected, a real-time noise map is established, the intensity of noise at each position in a certain range is displayed, the noise distribution situation is updated in real time, the noise pollution situation is accurately monitored in real time, data and information are provided for the treatment of noise pollution in one area, and the method is also an important component of a data city;
secondly, the interactive noise self-prior perception analysis system has the following advantages: firstly, during the data acquisition process, only a small amount of noise data information needs to be acquired to reconstruct overall data information, a signal acquisition end is compressed, and a complex data recovery part is handed to a server end for processing, so that the energy consumption of an acquisition terminal can be reduced; secondly, the data is directly acquired by the self-prior sensing, and the acquisition frequency is low, so that the energy consumption of sampling equipment can be reduced, and the requirement on transmission bandwidth is reduced; and thirdly, safety is provided, since the self-prior sensing is carried out on the data acquired by the participants in the interactive sensing system, the self-prior process is usually completed through a random observation matrix, and the data after the self-prior is transmitted to the server side. If the data are intercepted in the transmission process, the data collected by the participants cannot be obtained through reverse decompression, and the method has important value when being applied to noise perception of the smart phone;
thirdly, the application provides the data acquisition and reconstruction based on the interactive self-prior sensing of the smart phone for the environmental sensing, aiming at the problems of more data redundancy, large energy consumption in the processing process and the like in the interactive sensing system, the self-prior sensing is integrated into the interactive sensing noise detection system, the whole environmental noise data information is reconstructed by using a small amount of noise data, the data acquisition amount is effectively reduced, in order to improve the accuracy of the acquired data, a coarse-grained audio mode dynamic evaluation method is provided to guess the application scene of a participant in the sensing task process before the noise data is acquired, the simulation experiment proves that the algorithm is feasible and efficient, the acquisition, the processing and the compression transmission of the noise data are realized, and the participant intuitively obtains the noise distribution condition of the concerned area in the system in the form of a thermal map, the method has great practical significance and wide application prospect for noise monitoring and analysis;
fourthly, the interactive sensing system for the environmental noise is designed and realized, the client uses a smart phone carrying an intelligent operating system to be responsible for collecting and compressing noise data and sending the data to the server, participants can check a noise map at the phone, the server adopts a Web service mode based on an REST framework to be responsible for receiving the data sent from the client, processing and reconstructing the data, and finally feeding back a reconstructed result to the client. The method solves the redundancy of noise data, guarantees the credibility of the data, protects the individual privacy and improves the acquisition efficiency, and realizes the visualization of the noise data in the form of a thermal map, so that participants can visually observe the noise volume and the distribution condition of a sensing position, and the method is convenient for further analysis and application.
Drawings
Fig. 1 is a schematic diagram of classification, analysis and classification of usage states of a smart phone.
FIG. 2 is a hierarchical comparison of OSI, TSQ/IP and underwater acoustic communication network architectures.
Fig. 3 is a waveform diagram showing the change of CNU during one motion of the human body.
FIG. 4 is a schematic diagram of raw data from prior sensing simulation and performance experiment.
FIG. 5 is a diagram of the result of the original image being DCT transformed to remove absolute value and processed in descending order.
Fig. 6 is a diagram of an observation matrix generated by a participant during acquisition from location information.
Fig. 7 is a diagram illustrating the data reconstructed when the number of participants is different.
Fig. 8 is a general block diagram of a smartphone interactive noise a priori perception analysis system.
Fig. 9 is a flow chart of client design of the smart phone interactive noise a priori perception analysis system.
Fig. 10 is a flow chart of self-prior perceptual noise information acquisition, processing, and display.
Fig. 11 is a flow chart of self-prior perceptual noise information acquisition and termination.
Fig. 12 is a client-side noise thermodynamic diagram generation flow diagram.
FIG. 13 is a flow diagram of participant usage scenario inference with two parts.
Fig. 14 is a schematic diagram of a fifth part of experimental scenario.
Fig. 15 is an App interface diagram of a mobile phone end of a participant of the noise analysis system.
Fig. 16 is an exemplary graph of partial noise data collected by participant a during the morning hours.
Fig. 17 is an example graph of the distribution of noise in the morning period part plotted in the experiment.
Detailed description of the invention
The following further describes the technical solution of the smart phone interactive noise self-prior sensing analysis system provided in the present application with reference to the accompanying drawings, so that those skilled in the art can better understand the present application and can implement the present application.
Compared with a wireless sensing network, the interactive sensing system is low in deployment cost, convenient to apply and develop, capable of dynamically adjusting sensing areas and distributing sensing tasks, and therefore the interactive sensing system is concerned and high in application value.
The noise interactive perception analysis system based on the smart phone is provided for environment perception, collection, processing and compression transmission of noise data are achieved, and participants in the system visually obtain the noise distribution conditions of the interested areas through a heat map mode.
Aiming at the problems of high data redundancy, high energy consumption in the processing process and the like in an interactive sensing system, the method is provided for integrating the prior sensing into an interactive sensing noise detection system, reconstructing the whole environment noise data information by using a small amount of noise data, effectively reducing the data acquisition amount, and in order to improve the accuracy of the acquired data, the method for dynamically evaluating the coarse-grained audio mode is provided for conjecturing the application scene of a participant in the sensing task process before acquiring the noise data, and finally, the feasibility of the algorithm is demonstrated through a simulation experiment.
Finally, an ambient noise interactive perception system is designed and implemented. The client uses a smart phone carrying an intelligent operating system, is responsible for collecting and compressing noise data, sends the data to the server, and the participants can check a noise map at the mobile phone. The server side adopts a Web service mode based on the REST framework, is responsible for receiving the data sent from the client side, processes and reconstructs the data, and finally feeds back a reconstruction result to the client side.
First, data acquisition and reconstruction of interactive self-prior sensing
The interactive sensing system has many advantages, but also has many disadvantages, for the mobile terminal of the smart phone, because the resources are limited, the energy consumption is reduced, and the reduction of the data transmission quantity in the whole system is key, the initial signal can be accurately recovered by a small amount of linear projection, the data reconstruction is completely given to the server end to play a role, the energy consumption of the mobile phone end of the participant is reduced, and the transmission of the data quantity is also reduced.
Interactive noise data acquisition
Based on that the smart phone can influence data authenticity, reliability and practicality in different use scenes and different use modes, the application provides that coarse-grained audio mode dynamic evaluation is carried out on the use scenes and the use modes of the smart phone, the quality of collected data is improved, the purpose of screening data is achieved, and classification and analysis of the use state of the smart phone are shown in figure 1: the method comprises the following steps of dividing participants into a static type and a dynamic type according to the motion state of the participants, dividing the participants into a closed space and an open space according to the space where the mobile phones of the participants are located, for example, if the mobile phones are placed in indoor drawers, the mobile phones are determined to be the closed space in a static state, noise data collected by the mobile phones are relatively small, real-time noise data cannot be truly reflected, and therefore the data reliability in the state is low; when the participator is in the motion state, the mobile phone is exposed outdoors, and if the participator adopts high acquisition frequency, noise data of space-time characteristics can be better reflected, and discrete and uninterrupted acquisition areas are avoided.
When determining which motion state the mobile phone is in, an acceleration sensor in the mobile phone is used, as shown in fig. 2, the acceleration sensor of the mobile phone obtains acceleration data changes in three moving directions of the mobile phone, wherein an X axis represents acceleration in a left-right moving direction, a Y axis represents acceleration in a front-back moving direction, and a Z axis represents acceleration in a vertical moving direction.
When a participant carrying intelligent mobile terminal equipment is in a motion state, accelerations in three directions obviously change, the accelerations of three axes change to a certain extent in the motion process, but the change rules are not obvious, and the acceleration change rules of each axis are different when the motion postures are different, so that the algorithm complexity is increased if the acceleration change on each axis is analyzed, and therefore a new acceleration intensity resultant vector CNU is introduced as a characteristic quantity.
Figure BDA0003563703470000141
ax,ay,azThe acceleration in three directions is respectively, the CNU characteristic quantity is utilized to avoid the complexity brought by analyzing the acceleration in three axes, and the CNU value has larger change no matter what motion state, and the CNU is used as the motion state judgment basis. Fig. 3 is a waveform diagram showing the change of CNU during one motion of the human body.
When the participant is in a certain motion state, the CNU has a change process from a minimum value to a maximum value, when the participant is in a static state, the CNU area is stable, the CNU area is between a gravity acceleration g, 1.1g is set as a judgment critical value in the method, and the average CNU judgment of the participant 3 seconds after the start of a perception task is calculated: when CNU < 1.1g, participants were in a steady state. When CNU is more than or equal to 1.1g, the participant is in motion.
When judging whether the mobile phone is in a closed space, a smart phone light pulse proximity sensor is used for judging, the light pulse senses the distance between the mobile phone and a human body, calling is carried out in the conversation process, when a screen of the mobile phone is close to the face of a participant, the distance between the mobile phone and the human body is automatically measured, when the distance is smaller than a certain value, the screen is turned off, a touch screen event of the participant is not received, the false touch event in the conversation process is effectively prevented, the space where the mobile phone is located is judged by using data read by the light pulse proximity sensor, values [0] are used for acquiring parameters, when the distance is smaller than 1.6cm, the value [0] is 0, and when the distance is larger than or equal to 1.5cm, the value [0] is 5.
The data collected by the acceleration sensor and the light pulse proximity sensor are analyzed, the use scene of the participants is estimated in a coarse granularity mode, and the noise data provided by the participants are screened according to the use scene.
The evaluation of the noise volume is completed through a mobile phone end, when client software is started, the microphone at the mobile phone end senses the sound around and calculates the decibel value, meanwhile, the software acquires the current position information, namely longitude and latitude, through positioning and acquires the current time section, namely a triple (timestamp, longitude and latitude and decibel value) is acquired, the triple is stored in the mobile phone to obtain a noise data record, the mobile phone end only measures the surrounding noise data without recording the sound around the participant, a recording file is not stored, and the privacy of the participant is protected.
The noise information is collected based on the position of the participant, a target area G is selected, the area G is divided into N parts, each part is a noise measurement unit, and the obtained data information x in each noise measurement unit is collectedi(i ∈ {1,2,3, …, N }) into a vector form, representing the noise pollution situation in this target area:
x＝(x1，x2，...xN)T formula 2
x is a data complete set, and initial data x is reconstructed according to incomplete and random measurement data.
(II) adaptive noise data compression
In a certain time period T, a participant collects noise data in an area required by a perception task, and the participant collects a string of data vs,vsOnly partial noise measurement data, i.e. noise decibel value, position data information and time cut information, are included, firstly, v is measuredsEncoding into a vector y of dimension Ms，ys＝BsvsWherein B is observed in the matrixsSetting (2): bsIn is one M × LsContains only three elements of-1, 0,1, k' ≈ 100:
Figure BDA0003563703470000151
observation matrix BsThe pseudo random number generator is used for generating the pseudo random number on the mobile phone of each participant, the mobile phone end of each participant reduces the dimension of the acquired data through the generated observation matrix, only the vector after dimension reduction is sent to the service end, and therefore the transmission flow can be reducedAnd in the device, the observation matrix can be generated in the server section and is consistent with the mobile phone end.
The data obtained by all n participants are superposed, so that there is y*＝B*x*Wherein x is*Is all of vsSuperposition of (2):
Figure BDA0003563703470000152
Figure BDA0003563703470000153
B*is a P x Q sparse sampling matrix,
Figure BDA0003563703470000154
x' has data superposition and deletion, and in order to restore the original x, B is paired according to the sequence of elements in x*The reconstruction is performed such that:
y CE BGx formula 6
Where C is the perceptual matrix and E is a sparse representation of x over the discrete cosine transform matrix G.
(III) noise self-prior reconstruction
The noise self-prior reconstruction comprises the following specific steps:
defining: inputting: observation base B, observation noise y, sparsity k,
and (3) outputting: x k sparsity reconstructed noise signal s;
initialization: residual margin r of observation noise0Reconstructing the signal s as y 00, index set Tn＝Tn-1U { k }, the iteration number n is 0;
the first step is as follows: calculating the residual margin of the observation noise and the inner product g of each column of the observation base Bn＝BTr n-1；
The second step is that: find out gnJ is argmax gn[i]|；
The third step: updating index set Tn＝Tn-1Y[j]Andindex Observation base BT,Y[*]Updating a function for the index;
the fourth step: approximate solution x solved by least square methodn＝(BT T,BT)-1BT Ty；
The fifth step: updating residual margin of observation noise, rn＝y-GsnWhere, the iteration number n is n +1, G is a discrete cosine transform matrix;
and a sixth step: if n > k and a convergence condition is satisfied, stopping the iteration, sr＝sn，r＝rnOutput sr，snOtherwise, turning to the first step;
and (3) giving a discrete K sparse signal X, wherein the length N of the signal X is changed, each curve represents different N values, N is 50,100 and 500, the normalized sparsity s of the noise signal X is K/N is 0.1, the oversampling rate c is the ratio of the noise measurement quantity M to the noise signal sparsity K, and the observation matrix adopts a random Gaussian matrix.
(IV) self-prior sensing simulation and performance analysis
Simulation scene and parameter setting: in one area, 600 measurement units are selected, as shown in fig. 4: it is arranged that within a 3km x 2km rectangular area there are 600 areas of 100 x 100m, each with a height value for a total of 600 height values.
Setting the number of participants from 50 to 500, randomly collecting data in a target area to be collected by adopting a random walk model, setting the walking speed at 0.2 to 2m/s, setting the walking time of each time at 10 to 60min, pausing each participant for 0 to 15min, continuously collecting data for 24h, and starting to collect data when the participant pauses.
Before a simulation experiment is carried out, firstly, sparse transformation is carried out on initial data, the sparsity of the initial data is obtained, whether the data can be sparsely represented or not is determined, and the initial data is sparsely represented in a DCT (discrete cosine transformation) mode.
The displayed result with the first value removed is then processed by descending absolute value to obtain the result shown in fig. 5. It can be seen from fig. 5 that the numerical sparsity of the initial data is mostly 0, which proves that the initial data is sparse, in order to further obtain the sparsity of the initial data, the data with the numerical value smaller than 500 after sparse transformation is directly set to 0, then inverse transformation is performed, after inverse transformation, the numerical value of the data is maximum when the serial number is 50, and the sparsity k of the initial signal is estimated to be approximately equal to 50.
Performance analysis: the method comprises the steps that a participant collects data in the process of moving around, data collection is carried out at a pause point, the motion track of the participant is recorded while the participant moves in a region to be detected, a projection is generated on an observed value in the region in the collection process, namely in the collection process, when the participant collects one piece of data, the position information of the collected point is recorded, the data collected at the position is multiplied by a pseudo-random number generated by a pseudo-random number generator, when the next piece of position data is collected, the operation is repeated and is superposed with the data collected last time, the process is carried out until the data collection is finished, the data self-prior of the data at the collection end is realized, the data sent to a service end by the participant only comprises the position information and the data information after the self-prior, and the data redundancy is reduced. The observation matrix generated by the position information of the participants in the acquisition process is shown in fig. 6, the position of each pseudo-random number generator generating the pseudo-random number is the position of the data acquired by the participants, and the observation matrix performs self-prior on the data acquired by each participant, and then the data is projected from high dimensionality to low dimensionality after the observation matrix performs self-prior, so that the redundancy in the data transmission process is reduced.
The noise prior reconstruction result is shown in fig. 7, which respectively gives reconstruction results when 50 to 500 participants participate in the noise prior reconstruction result, and the reconstructed results are drawn into a topographic map for visually observing the comparison condition between the reconstructed results and the initial data. Fig. 7 shows the data results reconstructed when the number of participants is different, and when the number of participants is about 250, the reconstructed results are substantially similar to the original data set.
FIG. 7 is only a rough visual comparison of the reconstructed data and the original data, and the following error analysis is introduced, and the difference between the restored data corpus x and the used original data corpus x is defined by the relative error: the larger the relative error is, the worse the quality of the reconstructed signal is proved, and if the relative error is positive, the good quality of the reconstructed signal is proved, wherein the expression of the relative error is as follows:
Figure BDA0003563703470000171
when 250 participants are needed, the relative error epsilon is less than 0.025, namely within the allowable error range, only 250 participants are needed to provide one data per participant to reconstruct the whole data in the whole noise measurement area.
The simulation results verify that the whole required noise data can be reconstructed through a reconstruction algorithm under the condition that the information of each measuring unit is not obtained through a small number of participants when the interactive self-prior sensing data acquisition and reconstruction system is applied, the error can be controlled within an allowable range, and the accuracy is high.
Second, overall system design
The intelligent mobile phone end acquires the noise decibel value, the position information and the time information, and the noise decibel value, the position information and the time information are uploaded to the server end through the communication module. Meanwhile, noise information acquired by the participants is displayed in real time, after the participants send requests, the server side feeds back acquired noise data to the participants, the noise data are displayed in a noise map mode at the client side, the server side receives and sends the data through the communication module, and data processing is carried out through the data management module.
Based on the smart phone, noise data are obtained through the embedded sensor and visualization of the noise data is completed. The server side is used as a data processing center of the system and mainly performs data receiving, data storage and other necessary processing at the server side, data collected by participants are concentrated at the server side for noise self-prior reconstruction, the server adopts a data management module to communicate with the MySQL cluster, and the whole system is shown in figure 8.
Third, design of client system
Adopt smart mobile phone to accomplish data acquisition, storage, upload, the client mainly includes: firstly, sensing data is acquired, wherein the sensing data comprises noise information, positioning information, acceleration sensor information, proximity sensor information and timestamp information; secondly, the client communicates with the server, sends the acquired data information to the server and receives a feedback result of the server; and thirdly, data visualization, namely, the noise data fed back to the client side by the server side is visualized in a thermal map mode, so that the participants can visually observe the surrounding noise distribution condition. The flow chart of the whole client design is shown in fig. 9.
The smart phone provides use support for the equipment sensor, provides a driver program to manage the sensor hardware, and monitors the change of the external environment sensed by the sensor hardware in a monitor mode. The intelligent sensor development comprises the following specific steps:
step 1: acquiring sensor service: the participant clicks the "start acquisition" button to trigger the acquisition process, and the Sensor Manager (Sensor Manager) is initialized by the application;
step 2: acquiring a specified type of sensor: selecting a Sensor to be adopted, adopting a Sensor Manager to obtain and add a specified Sensor to be monitored;
and 3, step 3: registering a listener: after the Sensor is obtained, a Sensor Manager is adopted to add a registered monitor for the target Sensor, when the Sensor senses the change of the environment, the monitor returns the value of the Sensor, and meanwhile, the frequency of the Sensor acquisition is set in the monitor;
and 4, step 4: and (3) data calculation and storage: after the sensor data are obtained, the sensor data are processed and stored persistently;
the noise detection of the client terminal utilizes a microphone sensor in the mobile phone, the unit of the sound intensity is dB, and the noise intensity data acquisition calculation formula is as follows:
Figure BDA0003563703470000181
Prmsis the measured sound pressure, PrefSound pressure (20 micro pascal) as a reference value, noiseThe steps of starting data acquisition and terminating data are shown in fig. 10 and 11, respectively.
The method comprises the following steps of obtaining participant position information at a noise detection system client by adopting a third party map API, and obtaining a specific longitude and latitude information flow:
1) downloading the SDK of the third-party map and carrying out XML configuration related preparation work:
2) setting a positioning condition, and judging whether setOpegps opens positioning and returns a coordinate type setCoorType of a value;
3) register listener registrar locationiLister;
4) adopting StringBuffer to store the acquired data, wherein getTime acquires time, getLatitude acquires latitude, and getLongitude acquires longitude;
5) sending a request, enabling a monitor to work, and acquiring time and longitude and latitude data;
in order to facilitate participants to visually check the noise distribution situation, a thermodynamic map contained in a third-party map is adopted to represent the noise distribution situation, and the noise information thermodynamic map is displayed in the following way:
1) setting a map state by using setMapStatus ();
2) adding thermodynamic diagram acquisition data List < data > and a thermodynamic diagram construction method public Heat map Builderdata () to addHeatMap ():
3) acquiring data in JSONARray by using a List < data > getLocation () method, wherein the method is waiting to be called in addHeatMap ();
4) the mobile phone end sends a request to the server end;
5) the mobile phone end displays a noise map;
6) the method for deleting the thermodynamic diagram comprises the following steps: removeheatmap ();
the noise thermodynamic diagram generation process is shown in fig. 12.
Sensor data acquisition
The Sensor Data Collection is a core Sensor Data Collection module, a proximity Sensor, an accelerometer and a Sensor Event Listener interface are adopted to realize the acquisition of real-time Data of the Sensor, a Location Listener interface of a third-party map is adopted to realize the acquisition of information of a real-time geographic position, and d BSensor class recorded sound is adopted to obtain the maximum amplitude;
(1) initializing the SensorManager to obtain through get System Service (context. SENSOR _ SERVICE);
(2) acquiring acceleration Sensor data through get Default Sensor (Sensor. TYPE _ ACCELEROMETER);
(3) the register Listener acquires data, register Lister (Listener, Sensor manager. SENSOR _ DELAY _ NORMAL), in real time;
(4) the method of duplicating on Sensor Changed (Sensor Event) obtains Sensor data, and the returned data structure is an Event.
(5) After the sensor object is used, releasing sensor resources by using a stop () method, and simultaneously logging out a sensor monitoring service, sensor manager.
The code implementation process for acquiring the noise intensity comprises the following steps: the method for recording the noise by adopting startRecord () of mediarecordermo and the method for stopping the noise acquisition by stoprcordo are adopted, and the specific flow is as follows:
1) instantiating a MediaRecorder object;
2) setting the audio: setting an audio source by adopting setAudioSource (), setting an audio output format by adopting setOutputFormat (), setting an output coding mode by adopting setAudioEncoder (), and setting the position of an output file by adopting setOutputFile ();
3) prepare () is ready for recording;
4) starting () formally starting recording;
5) calling updateMicStatus () to calculate specific audio intensity, sending a numerical value through handler.
The updateMicStatus () calculation code in flow 5) is as follows: db (int) ((20 × math. log1o (ratio)) × 0.7), where multiplying 0.7 reduces the influence of extreme noise on the data, while, here, not so high accuracy is required, the double type is converted into the int type using the type-forced conversion.
Message.what and message.obj data are transmitted to a handler by using Message, the former contains transmitted code identification, the latter is a specific numerical value of noise intensity, and the handler Message is used for receiving data in MainActivity, wherein switch (msg.what) judges the msg.what identification, and after judging the msg.what identification is correct, the specific numerical value in msg.obj is converted into string type by using setText (msg.obj.testring ()) and then displayed, a timer function is realized by using a Runable () interface and a handler.postDelayed (), and the whole updateMicStatus () is operated discontinuously, so that the data timing is updated.
Map service plate design
The specific process of obtaining the map information comprises the following steps:
1) and (3) building a third-party map using environment: firstly, registering in a third-party map developer, then acquiring a secret key, downloading a jar package with a corresponding application function, putting the jar package into a lib file of software, executing updating import in the software, and then adding a corresponding authority and the secret key in manifest.
2) Java sets up the third party call correlation method: the method comprises the steps of registering and deregistering a third-party map by using a regiorstListener () and a regiorstListener (), and realizing the entrusting of positioning by using a start () and stop () method;
3) establishing location application.java initialization positioning and using the location application.java initialization positioning as an initial entrance of a calling map;
4) and detecting the noise of the main interface, monitoring the main interface by adopting setOnClickListener (), carrying out map positioning when the main interface is pressed down, and stopping positioning when the main interface is pressed down again.
The method comprises the steps of establishing BDLocation Listener in ManActivtyjava, receiving positioning information by adopting onReceiveLocation (), receiving StringBuffer of a string type, then obtaining positioning time by adopting location.
(III) noise map Generation
The noise map adopts a heat map API provided by a third-party map, noise is displayed in a heat map mode through a user-defined heat map, a custom task is completed by adopting a HeatMap. builder constructor, the visualization of noise data is realized, and the following process for generating the user-defined heat map is given:
1) noise data sent by a server is obtained through a List < LatLng > getLocations () method, an input data stream is obtained through a scanner (inputStream), String type data is stored in an array, then the array is traversed, longitude and latitude and noise values are obtained, and then the obtained data is put into a List.
2) Adding Thread () into the newly-built addHeatMap () method, calling getlocation () by adopting an interface run () to obtain server data, setting data to be drawn by adopting a heat map constructor HeatMap. builder, and adopting List < LatLng > data ═ getlocation ();
3) after the setting is finished, building a heat map by using build () and sending a numerical value to handleMessage (message msg) in the builder by using sendEmptyMessage (0), and after the handleMessage confirms that the numerical value is received 0, adding the heat map by using addHeatMap (heatmap) to finish the addition and display of the heat map.
Fourth, design of service end system
The server side is used as a data processing center to complete analysis of the use scene of the participant, perform data communication with the mobile terminal and complete data receiving, storage and necessary processing required to be performed at the server side, the server side adopts an REST framework, and the server side interface adopts a URL as a resource identifier to communicate with the client side.
MySQL is adopted as a system database, and an information data layer is composed of two parts: the system comprises a database and a database access layer, wherein the database access layer complies with JPA (Java native application platform) specifications when performing data storage, modification and deletion operations.
The service layer of the Web server side is composed of a service interface, an implementation layer and a physical layer, the Web layer is composed of two parts, the first part is a service portal layer, the second part is a Web service layer, the former is responsible for the interface, and the latter is used for the data request of the mobile client.
The method comprises the steps of server-side participant use scene presumption, wherein a motion state and a physical position of a smart phone in a participant collection behavior process are considered, a short-distance sensor and a linear acceleration sensor are adopted, the acceleration sensor is adopted to judge a participant motion state, the short-distance sensor is adopted to judge a state of the smart phone, and fig. 13 shows a flow chart for carrying out participant use scene presumption by combining the two parts.
Fifth, system experiment and result analysis
(I) Experimental mode
The selected experimental place is a road from the entrance of a college to a longan restaurant in a college, as shown in fig. 14, the length of the road is about 275m, the width of the road is about 9m, and the area of the road is about 1980m2. Selecting 5 participants, wherein mobile phones held by the participants are p40pro, and the mobile phones are divided into 3 time periods for collecting data, namely 08 morning: 15-08: 20, 10 am: 20-10: 25, 17 pm: 30-17: and 35, collecting and recording data every 3 seconds by the participant, carrying out self-prior processing on the collected data, and sending the processed data to the server side.
(II) System exhibition
As shown in fig. 15, when the participant clicks the start detection button, the mobile phone App automatically reminds the participant whether to allow the program to access the microphone and obtain the position information, and after the participant agrees, the mobile phone App starts to obtain the sound data and the positioning information around the participant.
The client compresses the collected data and sends the compressed data to the server, and after the server deploys the web service to the tomcat server, the web service inquires the wadl description file of the project.
(III) analysis of the results of the experiment
After a participant receives noise data information reconstructed by a server at a mobile phone terminal, a thermodynamic map is constructed at a client, and the construction of the thermodynamic map is mainly divided into the following processes:
(1) initializing a third-party thermal map, establishing a basic base map, setting a positioning position where a participant starts to acquire data, and setting the zoom level of the third-party map to be 20;
(2) setting gradient colors and starting values of gradient colors, wherein the adopted parameters are 5 color gradients, the color decibels are [0,255 ], [0,255,255], [0,255,0], [255,0,0], and the starting value of the gradient colors is startpoint ═ 0.45f,0.55f,0.65f,0.95f and 1 };
(3) specific noise data are transmitted according to specific geographic positions, and then data required to be rendered of the thermal map are constructed through a WeightedData method according to the size of the transmitted data;
(4) adding and displaying a thermal map, instantiating a Heatmap layer, and constructing the layer by adopting Heatmap.
As illustrated in fig. 16 and 17, participant a was in the morning 08: 15-08: 20, the data collected by the participants have time information, longitude and latitude and the size of noise data, and as can be seen from the data collected by the participant a, the decibel value of the noise in the detection area in the time period mostly exceeds 50 decibels, so that it can be known that the traffic flow in the detection area in the time period is more, the noise is larger, and as can be seen from the thermal map, the color of the detection area in the time period is darker, which can correspond to the specific detected value, so that the time period of the area is known as an early peak period.

Claims (10)

1. The system is characterized in that the system is based on the noise interactive analysis of the smart phone facing to the environment perception, the self-prior perception is applied to the interactive perception system, the collection, the processing and the compression transmission of noise data are realized through the design and the development of a smart phone end and a server end, and participants in the system visually obtain the noise distribution condition of the concerned area through a form of a thermal map to realize the interactive self-prior perception noise detection system;
first, interactive self-a priori aware data acquisition and reconstruction, comprising: firstly, interactive noise data acquisition, secondly, adaptive noise data compression and thirdly, noise self-prior reconstruction;
before noise data are collected, a coarse-grained audio modal dynamic evaluation method is adopted to speculate the application scene of a participant in the task sensing process, so that the accuracy of the collected data is improved; based on the problems of more noise data redundancy and large energy consumption in the processing process in the interactive sensing system, the method puts forward the method that the self-prior sensing is integrated into the interactive sensing noise detection system, and reconstructs the whole environment noise data information by using a small amount of noise data to reduce the data acquisition amount;
secondly, designing and realizing an environmental noise interactive perception system, wherein the system is designed as a whole, the client system is designed, the system comprises sensor data acquisition, map service plate design and noise map generation, and the server system is designed;
the client side uses a smart phone carrying an intelligent operating system to complete collection and compression of noise data and send the data to the server side, and participants check a noise map at the phone side; the server side receives the data sent from the client side by adopting a REST framework-based Web service mode, processes and reconstructs the data, realizes the processing of the interactive perception data and the conjecture of the participant state, and finally feeds back the reconstruction result to the client side.
2. The smartphone interactive noise a priori perception analysis system of claim 1, wherein interactive noise data acquisition: performing coarse-grained audio modal dynamic evaluation on a use scene and a use mode of the smart phone, dividing participants into a static type and a dynamic type according to the motion states of the participants, dividing the participants into a closed space and an open space according to the space where the mobile phones of the participants are located, placing the mobile phones in indoor drawers, determining that the participants are the closed space in a static state, determining that noise data acquired by the mobile phones are relatively small, and cannot truly reflect real-time noise data, wherein the data reliability is low in the state; when the participants are in a motion state, the mobile phone is exposed outdoors, and if the participants adopt high acquisition frequency, noise data of space-time characteristics can be better reflected, and discrete and uninterrupted acquisition areas are avoided;
when judging the motion state of the mobile phone, an acceleration sensor in the mobile phone is adopted, and the acceleration sensor of the mobile phone acquires the acceleration data change of the mobile phone in three moving directions, wherein the X axis represents the acceleration in the left-right moving direction, the Y axis represents the acceleration in the front-back moving direction, and the Z axis represents the acceleration in the vertical moving direction; when a participant carrying intelligent mobile terminal equipment is in a motion state, the accelerations of three axes all have certain changes, but the change rules are not obvious, and the change rules of the acceleration of each axis are different when the motion postures are different, if the acceleration change on each axis is analyzed, the algorithm complexity is increased, and a new acceleration intensity resultant vector CNU is introduced as a characteristic quantity:
Figure FDA0003563703460000011
ax,ay,azthe acceleration in three directions is respectively, the CNU characteristic quantity is utilized to avoid the complexity caused by analyzing the acceleration in three axes, the CNU value has larger change no matter what motion state, and the CNU is used as the motion state judgment basis;
when the participant is in a certain motion state, the CNU has a change process from a minimum value to a maximum value, when the participant is in a static state, the CNU area is stable, the CNU area is between a gravity acceleration g, 1.1g is set as a judgment critical value in the method, and the average CNU judgment of the participant 3 seconds after the start of a perception task is calculated: when CNU is less than 1.1g, the participator is in a steady state; when the CNU is more than or equal to 1.1g, the participants are in a motion state;
when judging whether the mobile phone is in a closed space, judging the space where the mobile phone is located by using data read by the light pulse proximity sensor, and acquiring parameters by using values [0], wherein when the distance is less than 1.6cm, the value of values [0] is 0, and when the distance is more than or equal to 1.5cm, the value of values [0] is 5;
analyzing data acquired by the acceleration sensor and the light pulse proximity sensor, estimating the use scene of the participant in a coarse-grained manner, and screening noise data provided by the participant according to the use scene;
the evaluation of the noise volume is finished through a mobile phone end, when client software is started, the microphone of the mobile phone end senses the sound around and calculates a decibel value, meanwhile, the software acquires current position information, namely longitude and latitude, through positioning and acquires the current time section, namely a triple (a timestamp, the longitude and latitude and the decibel value) is acquired, the triple is stored in the mobile phone to obtain a noise data record, the mobile phone end only measures the surrounding noise data without recording the sound around a participant, a recording file is not stored, and the privacy of the participant is protected;
the noise information is collected based on the position of the participant, a target area G is selected, the area G is divided into N parts, each part is a noise measurement unit, and the obtained data information x in each noise measurement unit is collectedi(i e {1,2,3, …, N }) to vector form, representing the noise pollution situation in this target area:
x＝(x1，x2，...xN)Tformula 2
x is a data complete set, and initial data x is reconstructed according to incomplete and random measurement data.
3. The smartphone interactive noise a priori perceptual analysis system of claim 1, wherein adaptive noise data compression: in a certain time period T, a participant collects noise data in an area required by a perception task, and the participant collects a string of data vs,vsOnly partial noise measurement data, i.e. noise decibel value, position data information and time cut information, are included, firstly, v is measuredsEncoding into a vector y of dimension Ms，ys＝BsvsWherein B is observed in the matrixsSetting of (1): b issIs one M × LsOf an observation matrix of only-1,0,1 three elements, k' ≈ 100:
Figure FDA0003563703460000021
observation matrix BsThe method comprises the steps that a pseudo-random number generator is used for generating on a mobile phone of each participant, the mobile phone end of each participant reduces the dimension of collected data through a generated observation matrix, only vectors after dimension reduction are sent to a service end, the transmission flow is reduced, and during reconstruction, the service end only needs the pseudo-random number generator the same as that of the mobile phone end, and the observation matrix can be generated in a server section and is consistent with that of the mobile phone end;
the data obtained by all n participants are superposed, so that there is y*＝B*x*Wherein x is*Is all of vsSuperposition of (2):
Figure FDA0003563703460000031
Figure FDA0003563703460000032
B*is a sparse sampling matrix of PxQ, P-M n
Figure FDA0003563703460000033
x' has data superposition and deletion, and in order to restore the original x, B is paired according to the sequence of elements in x*The reconstruction is performed such that:
y CE BGx formula 6
Where C is the perceptual matrix and E is a sparse representation of x over the discrete cosine transform matrix G.
4. The smartphone interactive noise a priori perception analysis system according to claim 1, wherein the noise a priori reconstruction specifically includes the steps of:
defining: inputting: observation base B, observation noise y, sparsity k,
and (3) outputting: x is a noise signal s reconstructed by k sparsity;
initialization: residual margin r of observation noise0Reconstructing the signal s as y00, index set Tn＝Tn-1U { k }, the iteration number n is 0;
the first step is as follows: calculating the residual margin of the observation noise and the inner product g of each column of the observation base Bn＝BTrn-1；
The second step is that: find out gnJ ═ argmax | gn[i]|；
The third step: updating index set Tn＝Tn-1Y[j]And indexing observation base BT,Y[*]Updating a function for the index;
the fourth step: approximate solution x solved by least square methodn＝(BT T,BT)-1BT Ty；
The fifth step: updating residual margin of observation noise, rn＝y-GsnWhere, the iteration number n is n +1, and G is a discrete cosine transform matrix;
and a sixth step: if n > k and a convergence condition is satisfied, stopping the iteration, sr＝sn，r＝rnOutput sr，snOtherwise, turning to the first step;
and (3) giving a discrete K sparse signal X, wherein the length N of the signal X is changed, each curve represents different N values, N is 50,100 and 500, the normalized sparsity s of the noise signal X is K/N is 0.1, the oversampling rate c is the ratio of the noise measurement quantity M to the noise signal sparsity K, and the observation matrix adopts a random Gaussian matrix.
5. The smartphone interactive noise a priori perception analysis system according to claim 1, wherein the overall system design is as follows: the intelligent mobile phone end acquires a noise decibel value, position information and time information, the noise decibel value, the position information and the time information are uploaded to the server end through the communication module, meanwhile, the noise information acquired by a participant is displayed in real time, after the participant sends a request, the server end feeds collected noise data back to the participant, the noise data are displayed in a noise map mode at a client end, the server end receives and sends the data through the communication module, and data processing is carried out through the data management module;
based on a smart phone, noise data are obtained through an embedded sensor and visualization of the noise data is completed, a server side is used as a data processing center of the system and mainly used for receiving and storing data and carrying out other necessary processing on the server side, data collected by participants are concentrated on the server side to carry out noise self-prior reconstruction, and the server is communicated with a MySQL cluster through a data management module.
6. The smartphone interactive noise a priori perception analysis system according to claim 1, wherein the client system is designed to: adopt smart mobile phone to accomplish data acquisition, storage, upload, the client mainly includes: firstly, sensing data is acquired, wherein the sensing data comprises noise information, positioning information, acceleration sensor information, proximity sensor information and timestamp information; secondly, the client communicates with the server, the client sends the acquired data information to the server and receives a feedback result of the server; thirdly, data visualization is realized, noise data fed back to the client side by the server side is visualized in a thermal map mode, and participants can observe surrounding noise distribution conditions visually;
the smart phone provides use support for the equipment sensor, provides a driver program to manage sensor hardware, and monitors the change of the external environment sensed by the sensor hardware in a monitor mode, wherein the specific steps of the smart sensor development are as follows:
step 1: acquiring sensor service: the participator clicks a button of 'start collecting' to trigger the collecting process, and a Sensor Manager is initialized by applying;
step 2: acquiring a specified type of sensor: selecting a Sensor to be adopted, adopting a Sensor Manager to obtain and add a specified Sensor to be monitored;
and 3, step 3: registering a listener: after the Sensor is obtained, a Sensor Manager is adopted to add a registered monitor to the target Sensor, when the Sensor senses the change of the environment, the monitor returns the value of the Sensor, and meanwhile, the frequency of the Sensor acquisition is set in the monitor;
and 4, step 4: and (3) data calculation and storage: after the sensor data are obtained, processing the sensor data and performing persistent storage;
the noise detection of the client terminal utilizes a microphone sensor in the mobile phone, the unit of the sound intensity is dB, and the noise intensity data acquisition calculation formula is as follows:
Figure FDA0003563703460000051
Prmsis the measured sound pressure, PrefA sound pressure that is a reference value;
the method comprises the following steps of obtaining participant position information at a noise detection system client by adopting a third party map API, and obtaining a specific longitude and latitude information flow:
1) downloading the SDK of the third-party map and carrying out XML configuration related preparation work:
2) setting a positioning condition, and judging whether setOpegps opens positioning and returns a coordinate type setCoorType of a value;
3) register listener registrar locationiLister;
4) adopting StringBuffer to store the acquired data, wherein getTime acquires time, getLatitude acquires latitude, and getLongitude acquires longitude;
5) sending a request, enabling a monitor to work, and acquiring time and longitude and latitude data;
the thermal map contained in the third-party map is adopted to represent the noise distribution situation, and the noise information thermal map is displayed and realized as follows:
1) setting a map state by adopting setMapStatus ();
2) adding thermodynamic diagram acquisition data List < data > and a thermodynamic diagram construction method public Heat map Builderdata () to addHeatMap ():
3) acquiring data in JSONArray by adopting a List < data > getLocations () method, wherein the method waits to be called in addHeatMap ();
4) the mobile phone end sends a request to the server end;
5) the mobile phone end displays a noise map;
6) the method for deleting the thermodynamic diagram comprises the following steps: removeheatmap ().
7. The smartphone interactive noise a priori perception analysis system of claim 1, wherein sensor data acquisition: the core Sensor Data acquisition module is a Sensor Data Collection, a proximity Sensor, an accelerometer and a Sensor Event Listener interface, the Sensor Event Listener interface is adopted to realize the acquisition of real-time Data of the Sensor, the Location Listener interface of a third-party map is adopted to realize the acquisition of real-time geographic position information, and d BSensor class recorded sound is adopted to obtain the maximum amplitude;
(1) initializing the SensorManager to obtain through get System Service (context. SENSOR _ SERVICE);
(2) acquiring acceleration Sensor data through get Default Sensor (Sensor, TYPE _ ACCELEROMER);
(3) register Listener real-time acquisition data, register Listener (Listener, Sensor manager. Sensor _ DELAY _ NORMAL);
(4) the method of duplicating on Sensor Changed (Sensor Event) obtains Sensor data, and the returned data structure is an Event.
(5) After the sensor object is used, releasing sensor resources by adopting a stop () method, and simultaneously logging out sensor monitoring service, namely sensor manager.
The code implementation process for acquiring the noise intensity comprises the following steps: the method for recording the noise by adopting startRecord () of mediarecordermo and the method for stopping the noise acquisition by stoprcordo are adopted, and the specific flow is as follows:
1) instantiating a MediaRecorder object;
2) setting the audio: setting an audio source by adopting setAudioSource (), setting an audio output format by adopting setOutputFormat (), setting an output coding mode by adopting setAudioEncoder (), and setting the position of an output file by adopting setOutputFile ();
3) prepare () is ready for recording;
4) starting () formally starting recording;
5) calling updateMicStatus () to calculate specific audio intensity, sending a numerical value through handler, sendmessage (), and carrying out intermittent delay sending through postDelayed ();
the updateMicStatus () calculation code in flow 5) is as follows: db ═ int ((20 × math.log1o (ratio)) × 0.7), where multiplying 0.7 reduces the influence of extreme noise on the data, and a double type is converted into an int type using a type-forced conversion;
message.what and message.obj data are transmitted to a handler by using Message, the former contains transmitted code identification, the latter is a specific numerical value of noise intensity, and the handler Message is used for receiving data in MainActivity, wherein switch (msg.what) judges the msg.what identification, and after judging the msg.what identification is correct, the specific numerical value in msg.obj is converted into string type by using setText (msg.obj.testring ()) and then displayed, a timer function is realized by using a Runable () interface and a handler.postDelayed (), and the whole updateMicStatus () is operated discontinuously, so that the data timing is updated.
8. The smartphone interactive noise a priori perceptual analysis system of claim 1, wherein the map service board is designed to: the specific process of obtaining the map information comprises the following steps:
1) building a third-party map using environment: firstly, registering in a third-party map developer, then acquiring a secret key, downloading a jar package with a corresponding application function, putting the jar package into a lib file of software, executing updating import in the software, and then adding a corresponding authority and the secret key in manifest.
2) Java sets up the third party call correlation method: the method comprises the steps of registering and deregistering a third-party map by using a regiorstListener () and a regiorstListener (), and realizing the entrusting of positioning by using a start () and stop () method;
3) establishing location application.java initialization positioning and using the location application.java initialization positioning as an initial entrance for calling a map;
4) detecting the noise of the main interface, monitoring the main interface by adopting setOnClickListener (), carrying out map positioning when the main interface is pressed down, and stopping positioning when the main interface is pressed down again;
the method comprises the steps of establishing BDLocation Listener in ManActivtyjava, receiving positioning information by adopting onReceiveLocation (), receiving StringBuffer of a string type, then obtaining positioning time by adopting location.
9. The smartphone interactive noise a priori perception analysis system according to claim 1, wherein the generation of the noise map: the noise map adopts a heat map API provided by a third-party map, noise is displayed in a heat map mode in a user-defined heat map mode, a custom task is completed by a HeatMap builder, the visualization of noise data is realized, and the following process of generating the user-defined heat map is as follows:
1) obtaining noise data sent by a server by a List < LatLng > getLocations (), obtaining an input data stream by a scanner (inputStream), storing String type data into an array, traversing the array to obtain longitude and latitude and noise values, and then putting the obtained data into List.
2) Adding Thread () into the newly-built addHeatMap () method, calling getlocation () by adopting an interface run () to obtain server data, setting data to be drawn by adopting a heat map constructor HeatMap. builder, and adopting List < LatLng > data ═ getlocation ();
3) and after the setting is finished, adopting build () to construct a heat map, adopting sendEmptyMessage (0) to send a numerical value to handlemap (messagemsg) in the build, and after confirming that the value is received 0, adopting addHeatMap (heatmap) to add the heat map to finish the addition and display of the heat map.
10. The smartphone interactive noise a priori perception analysis system of claim 1, wherein a server-side system design: the server side is used as a data processing center to complete analysis of the use scene of the participant and data communication of the mobile terminal, complete data receiving, storage and necessary processing required to be carried out at the server side, the server side adopts an REST architecture, and the server side interface adopts URL as a resource identifier to communicate with the client side;
MySQL is adopted as a system database, and an information data layer is composed of two parts: the system comprises a database and a database access layer, wherein the database access layer complies with JPA (Java native platform application) specifications when performing data storage, modification and deletion operations;
the service layer of the Web server side consists of a service interface, an implementation layer and a physical layer, the Web layer consists of two parts, the first part is a service portal layer, the second part is a Web service layer, and the former is responsible for the interface and the latter is used for the data request of the mobile client;
the method comprises the steps that a server-side participant uses scene conjecture, the motion state and the physical position of the smart phone in the participant behavior collecting process are considered, a short-distance sensor and a linear acceleration sensor are adopted, the acceleration sensor is adopted to judge the motion state of the participant, and the short-distance sensor is adopted to judge the state of the participant and the state of the smart phone.
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