CN113535693B - Data true value determination method and device for mobile platform and electronic equipment - Google Patents
Data true value determination method and device for mobile platform and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for determining a data true value of a mobile platform and electronic equipment, and belongs to the technical field of communication. The method for determining the data true value of the mobile platform comprises the steps of initially calculating the weight of a data source according to the probability density distribution of an observation value, constructing an optimization function F taking the weight of the data source as an optimization variable, updating the weight of the data source, the group reliability and the true value of the observation value through an iteration method, and determining the current true value as a target true value after the optimization function is judged to be converged. In the invention, the weights of the data sources and the reliability of each observation value are evaluated and optimized, namely the weights of all the data sources and the reliability of all the observation values are treated differently, so that the method for determining the true data value in the mobile platform is more accurate, namely the true data value stored in the mobile storage platform is closer to the true data value, and the effect of applying the true data value by the mobile platform is better.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for determining a true data value of a mobile platform, and an electronic device.
Background
In the era of mobile big data, a mobile platform accumulates and stores a large amount of data with various types and structures, and in order to reduce data redundancy and storage space cost, the data is generally required to be cleaned as necessary and then imported into the mobile storage platform.
When the data in the mobile platform has multiple sources, the problem of data collision often exists in the process of data cleaning. In order to determine the true data value from multiple sources of data, the true data value is usually determined by taking the average value or median of the conflicting data in the information of different sources. However, the accuracy of determining the data truth value by this method is low, and the data truth value of the mobile storage platform deviates from the actual truth value greatly, which results in the poor effect of applying the data truth value, for example, the poor effect of applying the data truth value to recommend mobile client products.
Disclosure of Invention
In order to solve the problem that the data true value is difficult to accurately determine in the data cleaning process of a mobile platform, so that the effect of the application data true value is poor, the invention provides a method, a device and electronic equipment for determining the data true value of the mobile platform.
In a first aspect, the present invention provides a method for determining a true value of data of a mobile platform, including:
s02, for conflict data describing the same object, aiming at all data sources and all objects, establishing an optimization function F by taking the weight of the data sources as an optimization variable, wherein the weight of the data sources is obtained by calculating the probability density distribution of an observation value, and the observation value is the conflict data provided to the object by the data sources;
s04, calculating the group reliability of the observation values based on the weight of the data source and the reliability of each observation value aiming at each object, wherein the reliability of each observation value is obtained from the probability density distribution of the data source;
s06, updating the weights of all data sources and updating the true values according to the group reliability of the observed values;
s08, calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F is converged according to the obtained F value, and returning to S04 for continuous execution if the optimization function F is not converged; and if the convergence is reached, taking the current true value as the true value of the target object.
In a second aspect, the present invention provides an apparatus for determining a true value of data of a mobile platform, including:
the system comprises a construction and initialization module, a data source optimization module and a data source optimization module, wherein the construction and initialization module is used for constructing an optimization function F by taking the weight of the data source as an optimization variable aiming at all data sources and all objects, wherein the weight of the data source is obtained by calculating the probability density distribution of an observation value, and the observation value is used for providing the conflict data to the objects for the data sources;
an observation reliability calculation module, configured to calculate, for each object, a group reliability of the observations based on the weight of the data source and a reliability of each observation, where the reliability of each observation is obtained from a probability density distribution of the observations;
the truth value updating module is used for updating the weights of all data sources and updating the truth value according to the group reliability of the observation value;
the convergence judging module is used for calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F is converged according to the obtained F value, and returning to S04 for continuous execution if the optimization function F is not converged; and if the convergence is reached, taking the current true value as the true value of the target object. In a third aspect, the present invention provides an electronic device comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method as claimed in any one of the above.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as defined in any one of the above.
The method for determining the data true value of the mobile platform comprises the steps of initially calculating the weight of a data source according to the probability density distribution of an observation value, constructing an optimization function F taking the weight of the data source as an optimization variable, updating the weight of the data source, the group reliability and the true value of the observation value through an iteration method, and determining the current true value as a target true value after the optimization function is judged to be converged. In the invention, the weights of the data sources and the reliability of each observation value are evaluated and optimized, namely the weights of all the data sources and the reliability of all the observation values are treated differently, so that the method for determining the true data value in the mobile platform is more accurate, namely the true data value stored in the mobile storage platform is closer to the true data value, and the effect of applying the true data value by the mobile platform is better.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating a method for determining true values of data on a mobile platform according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart of S04 in FIG. 1;
FIG. 3 is a detailed flowchart of S06 in FIG. 1;
FIG. 4 is another detailed flowchart of S06 in FIG. 1;
FIG. 5 is a block diagram of a data truth determination apparatus for a mobile platform according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In this era of information explosion, observations can be collected from multiple data source sources for the same object. In a mobile platform, for example, the same customer mobile signal information received through different mobile signal base stations, product rating scores collected from different customers. However, these observations are often inconsistent and there is often a data conflict between observations originating from different data sources. Therefore, it becomes a challenge how to integrate and summarize the observations provided by multiple sources to determine the true values of the data. Currently, in the method for determining the true value of data, all data sources are generally considered to be equally reliable, which results in a large deviation between the true value of data and the true value of data, and thus the application effect of data is poor, for example, false sensor error messages are continuously sent out, or spam messages spreading false information on the network, or customer mobile signal information errors, or product rating scores are not accurate, or the effect of mobile customer product recommendation is poor, and the like. Therefore, an embodiment of the present invention provides a method for determining a true value of data of a mobile platform.
Example one
FIG. 1 is a flow chart illustrating a method for determining a data truth value for a mobile platform in accordance with an exemplary embodiment of the present invention. The execution main body in the first embodiment of the present invention may be a server or a terminal, and the flow of the data truth value determining method includes the following steps.
S02, for conflict data describing the same object, aiming at all data sources and all objects, establishing an optimization function F by taking the weight of the data sources as an optimization variable, wherein the weight of the data sources is obtained by calculating the probability density distribution of an observation value, and the observation value is the conflict data provided to the object by the data sources;
s04, calculating the group reliability of the observation values based on the weight of the data source and the reliability of each observation value aiming at each object, wherein the reliability of each observation value is obtained from the probability density distribution of the observation values;
s06, updating the weights of all data sources and updating the true values according to the group reliability of the observed values;
s08, calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F is converged according to the obtained F value, and returning to S04 for continuous execution if the optimization function F is not converged; and if the convergence is reached, taking the current true value as the true value of the target object.
In the method for determining a data true value of a data platform according to the embodiment of the present invention, the weights of the data sources and the reliability of each observation value are evaluated and optimized, that is, the weights of all the data sources and the reliabilities of all the observation values are treated differently, so that the method for determining a data true value in a mobile platform is more accurate, that is, the data true value stored in the mobile storage platform is closer to the true value, and the effect of applying the data true value by the mobile platform is better. Wherein, the truth value refers to the correct truth value in a group of observed values. .
Before S02, S01: conflicting data from different data sources is obtained. Of course, embodiments of the present invention also aim to determine true values from collision data. For example, a set of observations in an object { x } 1 ，x 2 ,……,x i I ∈ n }, where the observations within the set of observations are from n data sources.
In S02, the probability density of the observed value is calculated by a kernel density function, wherein the input value in the kernel density function includes a gaussian kernel function.
Specifically, the kernel density function is:
wherein it is present>
Is the window width of the ith data source as h i (h i >0) The window width may be obtained experimentally, and the MAD (median absolute difference) may be taken as the window width initially. The kernel function is guaranteed to be estimated as a precondition for the probability density function, and the following conditions are satisfied:
K(x)≥0，∫K(x)dx＝1。
kernel function as described above
It can be a gaussian kernel function, and the corresponding gaussian kernel function is:
Thus, in S02, a set { x of observations in one object can be obtained based on the above-described kernel density function 1 ，x 2 ,……,x i I ∈ n } may also be set to a uniform distribution, i.e., all observations are the same in reliability (i.e., all data sources have the same probability value). Wherein the reliability of the observed value phi i (x i ) This can be obtained by a probability density distribution P (x) query.
In S02, the optimization function F is a loss function, which is:
in the above formula, L 1 ,L 2 …L m All objects are referred to; p 1 ,P 2 …P n All data sources; wherein n is j Is a data source S j (jth data source) all observations provided to object j; x is a radical of a fluorine atom ij Providing the observed value, phi, for the jth object to the ith data source j (x ij ) (ii) a reliability of the observation provided to the jth object for the ith data source; p i The probability density of the ith data source can be obtained according to the kernel density function; lj is the group reliability of the observed values for the jth object,
W ij the weight of the ith data source providing an observation for the jth object is specifically given by the following equation:
wherein i =1,2 \ 8230 \8230, n; j =1,2 \8230, m.
As shown in fig. 2, S04 specifically includes S041 and S042.
And S041, multiplying the weight of each data source and the reliability of each corresponding observation value respectively.
And S042, calculating the result of the product by a weighted average method to obtain the group reliability of the observed value.
As analyzed above, for the jth object, the group reliability of its observations is as follows:
wherein, W ij Weight of ith data source providing observation for jth object, Φ j (x ij ) The reliability of the observed values provided to the j (j =1,2, \8230; m) th object for the ith data source.
As shown in fig. 3, in S06, S061, S062, and S063 may be included to update the weight of the data source.
S061, updating the group reliability of the observed values in the optimization function F. Namely update the above
Lj in (1).
And S062, performing Lagrange multiplier method operation on the optimization function, and updating probability density distribution of the observed values in the object. And carrying out Lagrange multiplier operation on the formula to generate a new optimization function:
the probability density distribution of the observations in the updated object is thus calculated as follows:
and S063, updating the weight of all the data sources according to the updated probability density distribution of the observation values.
I.e. by
The weight of the updated data source is calculated. />
As shown in fig. 4, S064, S065, S066, and S067 may also be included in S06 to update the truth.
S064, multiplying the weight of the data source of each observation value, the group reliability of the observation values and the corresponding observation values in sequence to obtain a group of first product values aiming at each object;
and S065, multiplying the weight of the data source of each observation value and the group reliability of the observation values in sequence to obtain a group of second product values.
And S066, adding all the first product values to obtain a first value, and adding all the second product values to obtain a second value.
And S067, the ratio of the first value to the second value is the true value.
That is, the true value of the jth object is given by:
finally, in S08, when it is determined that the value F of the optimization function F is no longer changed, the optimization function F is considered to be converged, and the true value currently obtained in the jth object is taken as the target true value. Otherwise, S04 and S06 are repeated continuously.
By the embodiment of the invention, the truth value of the object can be determined from observation values of multiple sources in a fine and accurate manner.
Specifically, according to the method for determining the true value of the data of the mobile platform provided by the embodiment of the present invention, the object and the data source may be different according to different applications in the mobile platform, and naturally, the types of the conflicting data are different. For example, the method is applied to determining the rating score of a product, if the object is the product, the data source is different customers, the observed value is the score provided by different customers, and the target true value is the rating score of the product; for example, the method is applied to product recommendation of a mobile client, the object is the mobile client, the data source is different products, the observed value can be the stay time or the attention time of the mobile client on different products, and the target true value is a product to be recommended to the user; for example, for mobile signal information confirmation, the target is a mobile client, the data source is a different mobile base station, the observation value may be signal information transmitted by the mobile base station, and the target true value is a true value of mobile signal information finally transmitted to the mobile client, that is, a true value of mobile signal information finally transmitted to the mobile client is determined from mobile signal information transmitted by a plurality of mobile base stations.
Example two
A second embodiment of the present invention provides a specific application, that is, a method for determining a true value of data of a mobile platform is provided, for example, when received conflict information is mobile signal information about a target subscriber, in order to determine a true value of the mobile signal information of the target subscriber. The method specifically comprises the following steps:
and S21, receiving mobile signal information about the target client sent by different mobile base stations.
And S22, aiming at the mobile signal information describing the same target customer, aiming at all the mobile base stations and all the target customers, and taking each mobile base station as an optimization variable, constructing an optimization function F, wherein the weight of each mobile base station is obtained by calculating the probability density distribution of the information sent by the mobile base station.
And S24, aiming at the same mobile signal information, calculating the group reliability of the mobile signal information of the target client based on the weight of the mobile base station and the reliability of the information sent by each mobile base station, wherein the reliability of the information sent by each mobile base station is obtained by the probability density distribution of the information sent by the mobile base station.
And S26, updating the weights of all the mobile base stations and updating the true value of the mobile signal information of the target client according to the group reliability of the mobile signal information of the target client.
S28, calculating the F value of the optimization function F according to the updated weights of all the mobile base stations, judging whether the optimization function F is converged according to the obtained F value, and returning to S04 for continuous execution if the optimization function F is not converged; if the convergence is detected, the current true value is used as the mobile signal information true value of the target user.
Thus, the true value determining method determines the true value of the mobile signal information of the target user from the mobile signal information about the target subscriber transmitted from a plurality of different mobile base stations, thereby enabling the target user to receive more accurate signal information.
In addition, the specific processes in S22, S24, S26 and S28 may refer to embodiment one, and are not described herein again.
EXAMPLE III
Based on the same idea, the above method for determining a data true value of a mobile platform provided in the embodiment of the present invention further provides a data true value determining device, as shown in fig. 5.
This safety monitoring device 500 of thing networking includes: a construction and initialization module 501, an observation reliability calculation module 502, a true value update module 503, and a convergence judgment module 504. Wherein: the constructing and initializing module 501 is configured to construct an optimization function F for conflict data describing the same object, with respect to all data sources and all objects, by using a weight of a data source as an optimization variable, where the weight of the data source is obtained by calculating a probability density distribution of an observation value, and the observation value is conflict data provided by the data source to the object. An observation reliability calculation module 502, configured to calculate, for each object, a group reliability of the observation based on the weight of the data source and the reliability of each observation, where the reliability of each observation is obtained from a probability density distribution of the observation. And a true value updating module 503, configured to update the weights of all the data sources and update the true value according to the group reliability of the observation value. The convergence judging module is used for calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F converges according to the obtained F value, and returning to S04 for continuous execution if the optimization function F does not converge; and if the convergence is reached, taking the current true value as the true value of the target object.
Optionally, as an embodiment, the data true value determining apparatus further includes a conflict data receiving module, configured to acquire conflict data from different data sources.
In this embodiment of the present invention, the constructing and initializing module 501 is further configured to initialize the weight of the data source, specifically, during initialization, the reliability of all the observation values may be set to be the same.
In this embodiment of the present invention, the observed value reliability calculation module 502 is specifically configured to: multiplying the weight of each data source and the reliability of each corresponding observation value respectively; and calculating the result of the product by a weighted average method to obtain the group reliability of the observed value.
In the embodiment of the present invention, the true value updating module 503 may be specifically configured to update the group reliability of the observation value in the optimization function F; performing Lagrange multiplier method operation on the optimization function, and updating probability density distribution of the observed values in the object; and updating the weights of all the data sources according to the updated probability density distribution of the observed values.
Optionally, the true value updating module 503 may be further specifically configured to: for each object, multiplying the weight of the data source of each observation value, the group reliability of the observation value and the corresponding observation value in sequence to obtain a group of first product values; multiplying the weight of the data source of each observation value by the group reliability of the observation value in sequence to obtain a group of second product values; adding all the first product values to obtain a first value, and adding all the second product values to obtain a second value; the ratio of the first value to the second value is the true value.
In the constructing and initializing module 501, the probability density distribution of the observed values is calculated by a kernel density function, where the input values in the kernel density function include a gaussian kernel function.
The embodiment of the present invention provides a data true value determining apparatus 500, and the data true value determining apparatus provided in the embodiment of the present invention may also perform the method performed by the data true value determining apparatus in fig. 1, and implement the function of the data true value determining apparatus in the embodiment shown in fig. 1, which is not described herein again.
Example four
Figure 6 is a schematic diagram of a hardware configuration of an electronic device implementing various embodiments of the invention,
the electronic device 600 includes, but is not limited to: a radio frequency unit 601, a network module 602, an audio output unit 603, an input unit 604, a sensor 605, a display unit 606, a user input unit 607, an interface unit 608, a memory 609, a processor 610, and a power supply 611. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 6 does not constitute a limitation of the electronic device, and that the electronic device may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. In the embodiment of the present invention, the electronic device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer, and the like.
The processor 610 is configured to obtain interaction data of the internet of things device;
the processor 610 is further configured to determine, according to the interaction data, whether the interaction frequency of the internet of things device exceeds a safety upper limit value;
and the processor 610 is also used for outputting alarm information if the answer is positive.
In addition, the processor 610 is further configured to obtain a historical data training set of the internet of things device, where the historical data training set includes interaction frequencies within a historical preset time granularity;
in addition, the processor 610 is further configured to input the historical data training set into a big data algorithm prediction model to obtain a predicted value within the preset time granularity;
in addition, the processor 610 is further configured to calculate the safety upper limit value according to the predicted value and a residual corresponding to the historical data training set.
In addition, the processor 610 is further configured to determine whether address information of the interaction data is included in a security service library when the interaction frequency of the server device does not exceed a security upper limit value;
the processor 610 is further configured to output an alarm if no.
In addition, the processor 610 is further configured to obtain historical security service data of the server device, where the historical security service data includes address information corresponding to a secure connection;
in addition, the processor 610 is further configured to store the address information to generate the security service library.
In addition, the processor 610 is further configured to count an interaction frequency of the internet of things device within a preset time granularity based on the interaction data;
in addition, the processor 610 is further configured to determine whether the interaction frequency exceeds a safety upper limit value within the preset time granularity.
In addition, the processor 610 is further configured to send the alarm information to a target analysis object, where the alarm information includes: alarm time, alarm type and alarm content.
The embodiment of the invention provides electronic equipment, which can automatically and intelligently monitor the safety of the Internet of things at a terminal side and a service side of the Internet of things by acquiring the interaction data of the Internet of things equipment, judging whether the interaction frequency of the Internet of things equipment exceeds a safety upper limit value according to the interaction data and giving an alarm when the interaction frequency exceeds the safety upper limit value, so that the positioning accuracy of the safety problem is improved, the intelligent automatic analysis and output of the positioning monitoring of the safety problem of the Internet of things are realized, and the manpower requirement is reduced.
It should be understood that, in the embodiment of the present invention, the radio frequency unit 601 may be used to receive and transmit signals during a message transmission or call process, and specifically, receive downlink data from a base station and then process the received downlink data to the processor 610; in addition, the uplink data is transmitted to the base station. Generally, radio frequency unit 601 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 601 may also communicate with a network and other electronic devices through a wireless communication system.
The electronic device provides wireless broadband internet access to the user via the network module 602, such as assisting the user in sending and receiving e-mails, browsing web pages, and accessing streaming media.
The audio output unit 603 may convert audio data received by the radio frequency unit 601 or the network module 602 or stored in the memory 609 into an audio signal and output as sound. Also, the audio output unit 603 can provide audio output related to a specific function performed by the electronic apparatus 600 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 603 includes a speaker, a buzzer, a receiver, and the like.
The input unit 604 is used to receive audio or video signals. The input Unit 604 may include a Graphics Processing Unit (GPU) 6051 and a microphone 6042, and the Graphics processor 6051 processes image data of a still picture or video obtained by an image capturing apparatus (such as a camera) in a video capture mode or an image capture mode. The processed image frames may be displayed on the display unit 606. The image frames processed by the graphic processor 6051 may be stored in the memory 609 (or other storage medium) or transmitted via the radio frequency unit 601 or the network module 602. The microphone 6042 can receive sound, and can process such sound into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 601 in case of the phone call mode.
The electronic device 600 also includes at least one sensor 605, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 6061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 6061 and/or the backlight when the electronic apparatus 600 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration identification related functions (such as pedometer, tapping), and the like; the sensors 605 may also include fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which are not described in detail herein.
The display unit 606 is used to display information input by the user or information provided to the user. The Display unit 606 may include a Display panel 6061, and the Display panel 6061 may be configured by a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 607 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device. Specifically, the user input unit 607 includes a touch panel 6071 and other input devices 6072. Touch panel 6071, also referred to as a touch screen, may collect touch operations by a user on or near it (e.g., operations by a user on or near touch panel 6071 using a finger, stylus, or any other suitable object or attachment). The touch panel 6071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 610, receives a command from the processor 610, and executes the command. In addition, the touch panel 6071 can be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The user input unit 607 may include other input devices 6072 in addition to the touch panel 6071. Specifically, the other input devices 6072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a track ball, a mouse, and a joystick, which are not described herein again.
Further, the touch panel 6071 can be overlaid on the display panel 6061, and when the touch panel 6071 detects a touch operation on or near the touch panel 6071, the touch operation is transmitted to the processor 610 to determine the type of the touch event, and then the processor 610 provides a corresponding visual output on the display panel 6061 according to the type of the touch event. Although in fig. 6, the touch panel 6071 and the display panel 6061 are two independent components to implement the input and output functions of the electronic device, in some embodiments, the touch panel 6071 and the display panel 6061 may be integrated to implement the input and output functions of the electronic device, which is not limited herein.
The interface unit 608 is an interface for connecting an external device to the electronic apparatus 600. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 608 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the electronic device 600 or may be used to transmit data between the electronic device 600 and external devices.
The memory 609 may be used to store software programs as well as various data. The memory 609 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 609 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 610 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 609, and calling data stored in the memory 609, thereby performing overall monitoring of the electronic device. Processor 610 may include one or more processing units; preferably, the processor 610 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 610.
The electronic device 600 may further include a power supply 611 (e.g., a battery) for supplying power to the various components, and preferably, the power supply 611 may be logically connected to the processor 610 via a power management system, such that the power management system may be used to manage charging, discharging, and power consumption.
Preferably, an embodiment of the present invention further provides an electronic device, which includes a processor 610, a memory 609, and a computer program that is stored in the memory 609 and can be run on the processor 610, and when being executed by the processor 610, the computer program implements each process of the foregoing security monitoring method for the internet of things, and can achieve the same technical effect, and in order to avoid repetition, details are not described here again.
EXAMPLE five
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the foregoing embodiment of the security monitoring method for the internet of things, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
The embodiment of the invention provides a computer-readable storage medium, which can automatically and intelligently monitor the safety of the internet of things at a terminal side and a service side of the internet of things by acquiring the interactive data of the internet of things equipment, judging whether the interactive frequency of the internet of things equipment exceeds a safety upper limit value according to the interactive data and giving an alarm when the interactive frequency exceeds the safety upper limit value, thereby improving the positioning accuracy of safety problems, realizing the intelligent automatic analysis and output of the positioning monitoring of the safety problems of the internet of things and reducing the manpower requirement.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transient media) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present invention and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (7)
1. A method for determining a true value of data of a mobile platform, comprising:
s02, for conflict data describing the same object, aiming at all data sources and all objects, establishing an optimization function F by taking the weight of the data sources as an optimization variable, wherein the weight of the data sources is obtained by calculating the probability density distribution of an observation value, and the observation value is the conflict data provided to the object by the data sources;
s04, calculating group reliability of a group of observation values based on the weight of the data source and the reliability of each observation value aiming at each object, wherein the reliability of each observation value is obtained from the probability density distribution of the data source;
s06, updating the weights of all data sources and updating the true values according to the group reliability of the observed values;
s08, calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F is converged according to the obtained F value, and returning to S04 for continuous execution if the optimization function F is not converged; if the convergence is achieved, taking the current true value as a true value of the target object;
in the S04, including:
multiplying the weight of each data source and the reliability of each corresponding observation value respectively;
calculating the result of the product by a weighted average method to obtain the group reliability of the observed value;
in the S06, including:
updating the group reliability of the observed values in the optimization function F;
performing Lagrange multiplier operation on the optimization function, and updating probability density distribution of the observed values in the object;
updating the weights of all the data sources according to the updated probability density distribution of the observed values;
the step S06 includes:
for each object, multiplying the weight of the data source of each observation value, the group reliability of the observation values and the corresponding observation values in sequence to obtain a group of first product values;
multiplying the weight of the data source of each observation value by the group reliability of the observation value in sequence to obtain a group of second product values;
adding all the first product values to obtain a first value, and adding all the second product values to obtain a second value;
the ratio of the first value to the second value is the true value.
2. The method according to claim 1, characterized in that before S02, the method further comprises S01: conflicting data from different data sources is obtained.
3. The method according to claim 1, wherein the step of calculating the weight of the data source at S02 from the probability density distribution of the observed value comprises: the reliability of all the observations is the same.
4. The method of claim 1, wherein in S02 the probability density distribution of the observed values is calculated by a kernel density function, wherein the input values comprise gaussian kernel functions.
5. An apparatus for determining true data values for a mobile platform, comprising:
the system comprises a construction and initialization module, a data source calculation module and a data source comparison module, wherein the construction and initialization module is used for constructing an optimization function F by taking the weight of a data source as an optimization variable aiming at all data sources and all objects, wherein the weight of the data source is obtained by calculating the probability density distribution of an observation value, and the observation value is used for providing conflict data for the objects from the data source;
an observation reliability calculation module, configured to calculate, for each object, a group reliability of the observation based on the weight of the data source and the reliability of each observation, where the reliability of each observation is obtained from a probability density distribution of the observation;
the truth value updating module is used for updating the weights of all data sources and updating the truth value according to the group reliability of the observation value;
the convergence judging module is used for calculating the F value of the optimization function F according to the updated weights of all the data sources, judging whether the optimization function F converges according to the obtained F value, and returning to S04 for continuous execution if the optimization function F does not converge; if the convergence is achieved, taking the current true value as a true value of the target object;
the observation value reliability calculation module is specifically configured to: multiplying the weight of each data source and the reliability of each corresponding observation value respectively; calculating the result of the product by a weighted average method to obtain the group reliability of the observed value;
the truth value updating module is specifically used for updating the group reliability of the observation value in the optimization function F; performing Lagrange multiplier operation on the optimization function, and updating probability density distribution of the observed values in the object; updating the weights of all the data sources according to the updated probability density distribution of the observed values;
the truth update module is further specifically configured to: for each object, multiplying the weight of the data source of each observation value, the group reliability of the observation value and the corresponding observation value in sequence to obtain a group of first product values; multiplying the weight of the data source of each observation value by the group reliability of the observation value in sequence to obtain a group of second product values; adding all the first product values to obtain a first value, and adding all the second product values to obtain a second value; the ratio of the first value to the second value is the true value.
6. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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