CN115563449A - Personal track detection method and device, electronic equipment and storage medium - Google Patents
Personal track detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a personal track detection method, a personal track detection device, electronic equipment and a storage medium, which relate to the technical field of data processing, and the method comprises the following steps: acquiring at least two different types of data sources associated with the person location information; obtaining a weight coefficient of a preset index according to the data source, wherein the preset index comprises at least one of a data source type, the number of data sources representing that the personnel are positioned at the same position in a continuous time period, and the time difference between the current position and the previous position of the personnel; determining the weighted credibility of the personnel at the target position according to the weight coefficient; and obtaining the personal track of the personnel according to the relation between the weighted credibility and a preset threshold value. The detection error during personnel's track detection is reduced to carry out through single data source, can combine the data source of multiple different grade type to carry out the analysis to the track of personnel under test, promotes individual track data accuracy.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for detecting a trajectory of a person, an electronic device, and a storage medium.
Background
The method includes the steps that user positioning in a garden network is usually analyzed by referring to certain data, a personal track is formed based on the data, for example, the personal track in a period of time is formed based on building entrance guard card swiping data, and if a person in the garden enters a building A through an entrance guard card swiping mode at a first time point, the position of the person in the follow-up period of time is judged to be in the building A; and the person enters a building B through the entrance guard card swiping at a second time point, and personal tracks of the person in the campus network for a period of time are formed according to the time sequence, so that the person is judged to be in the building A from the first time point to the second time point and is positioned in the building B after the second time point.
The above approach relies on only a single data source for analysis and has the following problems: firstly, a single data source cannot realize the full coverage of the garden position, for example, data capable of positioning the position of a user comprises access control building card swiping data and wireless network data, a building access control is only deployed in buildings A and B, a wireless network is only built in buildings B and C, the access control building card swiping data statistics is only relied on, the access control building card swiping data statistics lacks the position data of the buildings C, the access control building card swiping data statistics is only relied on, the access data statistics lacks the position data of the buildings A, and therefore the loss of key data exists when the individual trajectory is statistically analyzed. Secondly, a certain error exists between the statistical analysis and the actual situation according to the single data source, for example, the person only conducts entrance guard card swiping action in the building A but does not actually enter the building A, or the person passes through the building B and automatically connects the wireless network of the building B due to wireless signal roaming mobile phone but does not actually enter the building B, and invalid data can be generated if only the statistical analysis is conducted by the single data source.
Therefore, the accuracy of the conventional method for detecting the personal track through a single data source is not high.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method, an apparatus, an electronic device and a storage medium for detecting a personal trajectory, which can specifically solve the existing problems.
In view of the above, in a first aspect, the present application provides a method for detecting a trajectory of a person, the method including: acquiring at least two different types of data sources associated with the person location information; obtaining a weight coefficient of a preset index according to the data source, wherein the preset index comprises at least one of a data source type, the number of data sources representing that the personnel are positioned at the same position in a continuous time period, and the time difference between the current position and the previous position of the personnel; determining the weighted reliability of the personnel at the target position according to the weight coefficient; and obtaining the personal track of the personnel according to the relation between the weighted credibility and a preset threshold value.
Optionally, after acquiring at least two different types of data sources associated with the person location information, the method comprises: and extracting data information of the data in the data source to obtain the type, the personnel information, the time information and the position information of the data source.
Optionally, the obtaining the weight coefficient of the preset index according to the data source includes: counting the number of the types of the data sources of the personnel at the target position within a preset time period; and obtaining a first weight coefficient according to the category number and the exponential function of the data source of the personnel at the target position.
Optionally, the obtaining a weight coefficient of the preset index according to the data source includes: obtaining the position sequence of the personnel in a continuous time period according to the position information and the time information of the at least two different types of data sources; obtaining the number of continuous data sources of the same position information in continuous time periods according to the position sequence; and obtaining a second weight coefficient according to the continuous occurrence number of the data sources of the same position information.
Optionally, the obtaining a weight coefficient of the preset index according to the data source includes: acquiring a first time required for reaching the current position from the previous position; and obtaining a third weight coefficient according to the ratio of the time difference between the current position and the previous position of the person to the first time.
Optionally, the preset indexes include types of data sources, the number of data sources representing that the person is located at the same position in consecutive time periods, and time differences between the current position and the previous position of the person, the weight coefficients include a first weight coefficient, a second weight coefficient, and a third weight coefficient respectively corresponding to the preset indexes, and the determining the weighted reliability of the person at the target position according to the weight coefficients includes: calculating a first weight coefficient, a second weight coefficient and a third weight coefficient of each data source; and obtaining the weighted reliability of the personnel at the target position according to the product of the first weight coefficient, the second weight coefficient and the third weight coefficient.
Optionally, the obtaining the personal trajectory of the person according to the relationship between the weighted reliability and a preset threshold includes: determining the credibility of the data source under the condition that the weighted credibility is greater than a preset threshold value; and extracting position information in a credible data source, and arranging the position information according to a time sequence to obtain the personal track of the personnel.
In a second aspect, there is provided a personal trajectory detection device, comprising: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring at least two different types of data sources related to personnel position information; the weight calculation module is used for obtaining a weight coefficient of a preset index according to the data source, wherein the preset index comprises at least one of the type of the data source, the number of data sources representing that the personnel are positioned at the same position continuously in continuous time periods, and the time difference between the current position and the previous position of the personnel; the reliability calculation module is used for determining the weighted reliability of the personnel at the target position according to the weight coefficient; and the track acquisition module is used for acquiring the personal track of the personnel according to the relation between the weighted credibility and a preset threshold value.
In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect.
In a fourth aspect, there is also provided a computer readable storage medium having stored thereon a computer program for execution by a processor to perform the method of any of the first aspects.
In general, the advantages of the present application and the experience brought to the user are:
in this embodiment, at least two different types of data sources associated with position information of a person are obtained, and then a weight coefficient of a preset index is obtained according to the data sources, where the preset index includes at least one of a type of the data source, a number of data sources indicating that the person is at the same position continuously appearing in a continuous time period, and a time difference between the current position and a previous position of the person; then determining the weighted credibility of the personnel at the target position according to the weight coefficient; and according to the relation between the weighted reliability and the preset threshold, the personal track of the person is obtained, the detection error in the detection of the personal track through a single data source can be reduced, the track of the person to be detected can be analyzed by combining various different types of data sources, and the accuracy of personal track data is improved.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are not to be considered limiting of its scope.
FIG. 1 illustrates a flow chart of steps of a personal trajectory detection method of the present application;
FIG. 2 is a schematic diagram of a structure of a personal trajectory detection device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 4 is a schematic diagram of a storage medium according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of steps of a personal trajectory detection method according to the present application, where the method is applicable to a campus positioning system, where the campus positioning system may include an access control system, a face recognition system, a wireless network system, and other systems capable of positioning and recognizing people, and the method may be executed by a server or an electronic device of the campus positioning system.
Referring to fig. 1, the method includes the following steps S101 to S104:
s101, acquiring at least two different types of data sources related to the personnel position information.
In this embodiment, the data source includes data of swiping a card through a gate of a person entering or exiting the campus, face data of a face recognition device of the campus, and wireless network data, or data of checking in a card executed by a user through an application program.
This embodiment can reduce the detection error when carrying out personnel's track detection through single data source through the data source of acquireing the at least two kinds of different grade types that are correlated with personnel's positional information, can combine the data source of multiple different grade types to carry out the analysis to the orbit of testee, promotes personal track data degree of accuracy.
In one example, in consideration that the acquired data sources have different types, the data forms of the acquired data sources may differ, and therefore, after acquiring at least two different types of data sources associated with the person position information, the method of the embodiment includes: and extracting data information of the data in the data source to obtain the type, the personnel information, the time information and the position information of the data source.
The data source types comprise gate data, face data, wireless network data, communication data and the like. The personnel information comprises a user name of the personnel or a name of the intelligent terminal corresponding to the user name and the like. The time information comprises the time when the person swipes the card through the gate, the time when the person is connected with the wireless network and the like. The position information includes the position of the gate where the person swiped the card, the position of the wireless network connected by the user, and the like.
And S102, obtaining a weight coefficient of a preset index according to the data source.
In this embodiment, the preset index includes at least one of a type of a data source, a number of data sources representing that the person is at the same position in consecutive time periods, and a time difference between the current position and a previous position of the person.
Wherein, under the condition that the preset index is the data source type, obtaining the weight coefficient of the preset index according to the data source comprises: counting the number of the types of the data sources of the personnel at the target position within a preset time period, and obtaining a first weight coefficient according to the number of the types of the data sources of the personnel at the target position and an exponential function.
In this embodiment, the target location may be set by a system administrator in a customized manner, for example, the target location is a current location of a certain person, and may also be any location of the certain person in a historical time period.
In this embodiment, a first weight coefficient α is obtained according to the number of types of data sources of the person at the target position and an exponential function, where an expression of the first weight coefficient α is:
α＝e n-1
wherein n is the number of types of data sources of the target position (n is more than or equal to 1).
For example, when only building entrance card swiping data exists in a building, and no other data exists, n =1; when building entrance guard card swiping data and wireless network access data exist in the B building at the same time, n =2. When multiple data sources exist in the target position, the more the number of the data sources is, the more credible the data of the target position is.
Under the condition that the preset index is the number of continuous data sources representing the personnel at the same position in a continuous time period, the weight coefficient of the preset index is obtained according to the data sources, and the method comprises the following steps: obtaining the position sequence of the personnel in the continuous time period according to the position information and the time information of at least two different types of data sources; obtaining the number of continuous data sources of the same position information in continuous time periods according to the position sequence; and obtaining a second weight coefficient according to the continuous occurrence number of the data sources of the same position information.
In this embodiment, the expression of the second weight coefficient β is:
β＝1+ln k
and k is the number of continuous occurrences of the data source at the same position in the continuous time period, and the larger k is, the more credible the data at the position is.
For example, a, B, C, and D are identifiers corresponding to four buildings, and positions extracted by data sources in a continuous time period are ranked as AABBBBCD, so that the number k of data sources of the building a continuously appearing in the continuous time period a =2, number k of consecutive occurrences of data sources of B buildings in consecutive time periods b =4, number k of consecutive occurrences of data sources of C buildings in consecutive time periods c =1, number of consecutive occurrences of data source of D building in consecutive time periods k d ＝1。
Under the condition that the preset index is the time difference between the current position and the previous position of the personnel, the weight coefficient of the preset index is obtained according to a data source, and the method comprises the following steps: acquiring a first time required for reaching a current position from a previous position; and obtaining a third weight coefficient according to the ratio of the time difference between the current position and the previous position of the person to the first time.
In this embodiment, assuming that the previous location is a building a and the current location is a building B, the first time when the previous location reaches the current location may be an average time between the arrival of the person from the building a to the building B.
In this embodiment, the expression of the third weight coefficient γ is:
wherein, t 1 Time stamp of person at current position, t 0 Is the time stamp of the person at the previous location, and T is the first time.
It will be understood that when t is 1 -t 0 >When it is indicated that the person is available at t 1 -t 0 The time difference from building A to building B is reasonable, has high reliability and is 0<t 1 -t 0 When the time difference is smaller than or equal to T, the time difference is too small, data errors can exist, and the smaller the time difference is, the more unreliable the data at the position is. Therefore, this embodiment is at 0<t 1 -t 0 And when the weight is less than or equal to T, improving the reliability of the data through a third weight coefficient gamma.
For example, the position information data such as AABBBBCD is sequentially extracted according to the time sequence, and the time stamp collected by the data source of the building C is t c The timestamp acquired by the data source of the previous building B is t b Then, it is
S103, determining the weighted reliability of the person at the target position according to the weight coefficient.
In this embodiment, the preset index includes at least one of a type of a data source, a data source representing that the person is at the same position in a continuous time period, and a time difference between the current position and the previous position of the person. And determining the weighted reliability of the personnel at the target position according to the weight coefficient of the preset index, and when the preset index only comprises the data source type, determining the weighted reliability according to the first weight coefficient, wherein the weighted reliability is the first weight coefficient. When the preset index only includes the number of data sources representing that the person is located at the same position continuously in continuous time periods, the weighted reliability can be determined according to the second weight coefficient, and the weighted reliability is the second weight coefficient. When the preset index only includes the time difference between the current position and the previous position of the person, the weighted reliability can be determined according to the third weight coefficient, and the weighted reliability is the third weight coefficient. And when the preset index comprises the type of the data source and the number of the data sources representing the personnel at the same position in continuous time periods, obtaining the weighted credibility according to the sum of the first weight coefficient and the second weight coefficient. And when the preset indexes comprise the data source type and the time difference between the current position and the previous position of the personnel, obtaining the weighted reliability according to the sum of the first weight coefficient and the third weight coefficient. And when the preset index comprises a data source of the person at the same position and the time difference between the current position and the previous position, obtaining the weighted reliability according to the sum of the second weight coefficient and the second weight coefficient.
It can be understood that the more the preset indexes are, the more accurate the calculation result is, therefore, the preset indexes of this embodiment include the type of the data source, the number of consecutive occurrences of the data source that represents that the person is at the same position in consecutive time periods, and the time difference between the current position and the previous position of the person, the weighting coefficients include a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient that respectively correspond to the preset indexes, at this time, the weighted reliability that the person is at the target position is determined according to the weighting coefficients, including: and calculating a first weight coefficient, a second weight coefficient and a third weight coefficient of each data source, and obtaining the weighted reliability of the personnel at the target position according to the product of the first weight coefficient, the second weight coefficient and the third weight coefficient.
The expression of the weighted credibility P is as follows:
P＝α×β×γ
where α is a first weight coefficient, β is a second weight coefficient, and γ is a third weight coefficient.
For example, if the target position is a building a, and the type of data source of the building a n =2 in the preset time period, the first weight coefficient α = e, and the number k of data sources of the building a continuously appearing in the preset time period a =2, then the second weight coefficient β =1+ ln 2, the position of the person before the a building is B building, then
Assuming γ =1, the person is at a weighted confidence of building a:
P＝e×(1+ln 2)×1≈4.6
by analogy, the weighted confidence level that the person is at the target location can be obtained from all data sources associated with the target location.
And S104, obtaining the personal track of the personnel according to the relation between the weighted credibility and the preset threshold value.
In the embodiment, the credibility of the person at the target position is determined by setting the credible preset threshold and by the size relationship between the preset threshold and the weighted credibility. Determining the credibility of the data source under the condition that the weighted credibility of the personnel at the target position is greater than a preset threshold value; and extracting the position information in the credible data source, and arranging the position information according to the time sequence to obtain the personal track of the personnel.
For example, if the preset threshold is 4, the target location includes four buildings a, B, C, and D, the person trajectory obtained by the single data source calculation method is abbbccdddd, and the weighted confidence levels of the person at the four buildings calculated according to the above steps S101 to S103 in this embodiment are P A ＝1、P B ＝5、P C ＝4.5、P D If the number of people in building a is not equal to or less than 6, it is indicated that the data of the person in building a is not authentic, and if the wireless terminal of the person is connected to the local area network of building a but the person is not in building a, the trajectory obtained when the trajectory of the person is calculated is BCD.
In the embodiment, at least two different types of data sources associated with position information of a person are obtained, and then a weight coefficient of a preset index is obtained according to the data sources, wherein the preset index comprises at least one of the type of the data source, the number of the data sources which indicate that the person is at the same position in continuous time periods, and the time difference between the current position and the previous position of the person; then determining the weighted credibility of the personnel at the target position according to the weight coefficient; and according to the relation between the weighted credibility and the preset threshold value, the personal track of the personnel is obtained, the detection error in the personnel track detection through a single data source can be reduced, the track of the personnel to be detected can be analyzed by combining various different types of data sources, and the accuracy of personal track data is improved.
An application embodiment provides a personal trajectory detection device, which is configured to execute the personal trajectory detection method according to the foregoing embodiment, and as shown in fig. 2, the personal trajectory detection device 200 includes:
a data acquisition module 210 for acquiring at least two different types of data sources associated with the person location information.
The weight calculation module 211 is configured to obtain a weight coefficient of a preset index according to the data source, where the preset index includes at least one of a type of the data source, a number of data sources representing that the person is located at the same position continuously appearing in a continuous time period, and a time difference between the current position and a previous position of the person.
A confidence level calculation module 212, configured to determine a weighted confidence level that the person is at the target location according to the weight coefficient.
And the track obtaining module 213 is configured to obtain the personal track of the person according to the relationship between the weighted reliability and a preset threshold.
In a possible example, the data obtaining module 210 is further configured to perform data information extraction on the data in the data source, so as to obtain a data source type, personnel information, time information, and location information.
In a possible example, the weight calculating module 211 is further configured to count the number of types of data sources of the person at the target location within a preset time period; and obtaining a first weight coefficient according to the type number and the exponential function of the data source of the personnel at the target position.
In a possible example, the weight calculating module 211 is further configured to obtain the position ranking of the person in the continuous time period according to the position information and the time information of the at least two different types of data sources; obtaining the number of continuous data sources of the same position information in continuous time periods according to the position sequence; and obtaining a second weight coefficient according to the continuous occurrence number of the data sources of the same position information.
In a possible example, the weight calculating module 211 is further configured to obtain a first time required for reaching the current location from the previous location; and obtaining a third weight coefficient according to the ratio of the time difference between the current position and the previous position of the person to the first time.
In a possible example, the preset indexes include a data source type, a number of data sources representing that the person is at the same position in a continuous time period, and a time difference between the current position and a previous position of the person, and the reliability calculation module 212 is further configured to calculate a first weight coefficient, a second weight coefficient, and a third weight coefficient for each data source; and obtaining the weighted reliability of the personnel at the target position according to the product of the first weight coefficient, the second weight coefficient and the third weight coefficient.
In a possible example, the trajectory obtaining module 213 is further configured to determine that the data source is trusted if the weighted confidence level is greater than a preset threshold; and extracting position information in a credible data source, and arranging the position information according to a time sequence to obtain the personal track of the personnel.
The personal trajectory detection device provided by the above embodiment of the present application and the personal trajectory detection method provided by the embodiment of the present application have the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored in the personal trajectory detection device.
The embodiment of the present application further provides an electronic device corresponding to the personal trajectory detection method provided in the foregoing embodiment, so as to execute the personal trajectory detection method. The embodiments of the present application are not limited.
Please refer to fig. 3, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 3, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the personal trajectory detection method provided in any of the foregoing embodiments when executing the computer program.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the personal track detection method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Referring to fig. 4, the computer-readable storage medium is an optical disc 30, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program may perform the personal track detection method provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the personal trajectory detection method provided by the embodiment of the present application have the same advantages as the method adopted, run or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Moreover, those of skill in the art will understand that although some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application. The present application may also be embodied as apparatus or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of personal trajectory detection, the method comprising:
acquiring at least two different types of data sources associated with personnel location information;
obtaining a weight coefficient of a preset index according to the data source, wherein the preset index comprises at least one of a data source type, the number of data sources representing that the personnel are positioned at the same position in a continuous time period, and the time difference between the current position and the previous position of the personnel;
determining the weighted credibility of the personnel at the target position according to the weight coefficient;
and obtaining the personal track of the personnel according to the relation between the weighted credibility and a preset threshold value.
2. The method of claim 1, wherein after obtaining at least two different types of data sources associated with people location information, the method comprises:
and extracting data information of the data in the data source to obtain the type of the data source, personnel information, time information and position information.
3. The method according to claim 1, wherein the predetermined index is a data source type, and the obtaining a weight coefficient of the predetermined index according to the data source comprises:
counting the number of types of data sources of the personnel at the target position within a preset time period;
and obtaining a first weight coefficient according to the category number and the exponential function of the data source of the personnel at the target position.
4. The method according to claim 1, wherein the preset index is the number of continuous occurrences of a data source representing that the person is at the same position in a continuous time period, and the obtaining of the weight coefficient of the preset index according to the data source comprises:
obtaining the position sequence of the personnel in a continuous time period according to the position information and the time information of the at least two different types of data sources;
obtaining the number of continuous data sources of the same position information in continuous time periods according to the position sequence;
and obtaining a second weight coefficient according to the continuous occurrence number of the data sources of the same position information.
5. The method of claim 1, wherein the preset index is a time difference between a current position and a previous position of the person, and the obtaining a weighting factor of the preset index according to the data source comprises:
acquiring a first time required for reaching the current position from the previous position;
and obtaining a third weight coefficient according to the ratio of the time difference between the current position and the previous position of the person to the first time.
6. The method of claim 1, wherein the preset indexes comprise data source types, the number of continuous occurrences of data sources representing that the person is at the same position in continuous time periods, and time differences of the person between the current position and the previous position, the weight coefficients comprise a first weight coefficient, a second weight coefficient and a third weight coefficient respectively corresponding to the preset indexes, and the determining the weighted credibility of the person at the target position according to the weight coefficients comprises:
calculating a first weight coefficient, a second weight coefficient and a third weight coefficient of each data source;
and obtaining the weighted reliability of the personnel at the target position according to the product of the first weight coefficient, the second weight coefficient and the third weight coefficient.
7. The method of claim 1, wherein obtaining the personal trajectory of the person according to the relationship between the weighted confidence level and a preset threshold comprises:
determining that the data source is credible under the condition that the weighted credibility is larger than a preset threshold;
and extracting position information in a credible data source, and arranging the position information according to a time sequence to obtain the personal track of the personnel.
8. A personal trajectory detection device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring at least two different types of data sources related to personnel position information;
the weight calculation module is used for obtaining a weight coefficient of a preset index according to the data source, wherein the preset index comprises at least one of the type of the data source, the number of data sources representing that the personnel are positioned at the same position continuously in continuous time periods, and the time difference between the current position and the previous position of the personnel;
the reliability calculation module is used for determining the weighted reliability of the personnel at the target position according to the weight coefficient;
and the track acquisition module is used for acquiring the personal track of the personnel according to the relation between the weighted credibility and a preset threshold value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method according to any of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116776014A (en) * | 2023-07-10 | 2023-09-19 | 和智信(山东)大数据科技有限公司 | Multi-source track data representation method and device |
| CN117368953A (en) * | 2023-12-08 | 2024-01-09 | 深圳咸兑科技有限公司 | Hybrid positioning method, hybrid positioning device, electronic equipment and computer readable storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116776014A (en) * | 2023-07-10 | 2023-09-19 | 和智信(山东)大数据科技有限公司 | Multi-source track data representation method and device |
| CN116776014B (en) * | 2023-07-10 | 2024-01-16 | 和智信(山东)大数据科技有限公司 | Multi-source track data representation method and device |
| CN117368953A (en) * | 2023-12-08 | 2024-01-09 | 深圳咸兑科技有限公司 | Hybrid positioning method, hybrid positioning device, electronic equipment and computer readable storage medium |
| CN117368953B (en) * | 2023-12-08 | 2024-03-22 | 深圳咸兑科技有限公司 | Hybrid positioning method, hybrid positioning device, electronic equipment and computer readable storage medium |
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