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CN111132052A - Intelligent safety campus positioning method, system, equipment and readable storage medium - Google Patents

Intelligent safety campus positioning method, system, equipment and readable storage medium Download PDF

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Publication number
CN111132052A
CN111132052A CN201911414456.1A CN201911414456A CN111132052A CN 111132052 A CN111132052 A CN 111132052A CN 201911414456 A CN201911414456 A CN 201911414456A CN 111132052 A CN111132052 A CN 111132052A
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signal
positioning
time
signals
calculating
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徐荣盛
翁冠南
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Zhejiang Qinghai Internet Of Things Technology Co Ltd
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Zhejiang Qinghai Internet Of Things Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The invention discloses a smart safe campus positioning method, which comprises the following steps: acquiring label signals sent by a sender and received by all Internet of things gateways, wherein the label signals comprise data frame sequence numbers, receiving time and signal intensity; correcting the signal intensity to generate corrected signal intensity; classifying the label signals of the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the strength of the correction signal, and taking the label signal corresponding to the maximum strength of the correction signal as a positioning signal of the data frame sequence number; and calculating the residence time of the sender in the detection range of each Internet of things gateway according to the positioning signals to generate the positioning residence time. Correspondingly, the invention also discloses a system, computer equipment and a computer readable storage medium for realizing the method. By adopting the invention, the track information of the students can be accurately and efficiently collected and analyzed, and the cost is reduced.

Description

Intelligent safety campus positioning method, system, equipment and readable storage medium
Technical Field
The invention relates to a positioning technology, in particular to a smart security campus positioning method, a system, equipment and a readable storage medium.
Background
With the popularization of quality education, the interest and hobbies of students can be known, and further, the education according to the profiles becomes the key point of interest of schools. The activity tracks of students in schools reflect the interests and hobbies of the students to a certain degree, for example, the interests and hobbies of students in libraries are different from those of students in playgrounds. In addition, with the progress of campus security construction, the knowledge of students' whereabouts is becoming an important part of campus security management. Therefore, schools currently focus on the mastery of the whereabouts of students. At present, the way of mastering the whereabouts of students is mainly to install various cameras indoors, such as classrooms and other places, and analyze the behavior habits of the students by collecting the positions of the students.
However, the use of cameras to collect the whereabouts of students requires face recognition to determine the positions of the students and the durations of the students at certain positions, the solution is complicated, and the cost is very high when the cameras are installed indoors, and at least 4 cameras are needed in one classroom. In addition, the camera not only collects the whereabouts information of the students, but also collects the daily images of the students, and the privacy of the students is inevitably involved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an intelligent safe campus positioning method, system, device and readable storage medium, which can accurately and efficiently collect and analyze the whereabouts information of students, reduce the cost and reduce the damage to the privacy of the students.
In order to solve the above technical problem, the present invention provides a smart security campus positioning method, including: acquiring label signals sent by a sender and received by all Internet of things gateways, wherein the label signals comprise data frame sequence numbers, receiving time and signal intensity; correcting the signal intensity to generate corrected signal intensity; classifying the label signals of the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the strength of the correction signal, and taking the label signal corresponding to the maximum strength of the correction signal as a positioning signal of the data frame sequence number; and calculating the residence time of the sender in the detection range of each Internet of things gateway according to the positioning signals to generate the positioning residence time.
As an improvement of the above, the step of performing correction processing on the signal intensity and generating a corrected signal intensity includes: acquiring a preset calibration value; calculating a corrected signal strength Sr：
Sr＝Si+R
Wherein S isrTo correct the signal strength, SiFor signal strength, R is a preset calibration value.
As an improvement of the above solution, the step of classifying the tag signals of the same data frame number according to the receiving time to generate at least one comparison set includes: s1, sequencing the label signals with the same data frame sequence number according to the receiving time to generate a signal sequence; s2, respectively calculating the receiving time difference between the first label signal and other label signals in the signal sequence; s3, judging whether the receiving time difference is less than or equal to the preset error time one by one, if so, classifying the two label signals corresponding to the receiving time difference into a comparison set, and if not, classifying the first label signal into the comparison set; and S4, judging whether the tag signals are not included in the comparison set in the signal sequence, if so, sorting the tag signals which are not included according to the receiving time to generate a new signal sequence, and returning to the step S2.
As an improvement of the above scheme, the step of calculating the stay time of the sender in the detection range of each internet of things gateway according to the positioning signal and generating the positioning stay time includes: calculating the number of positioning signals of all data frame serial numbers received by each Internet of things gateway to generate the number of the positioning signals; acquiring a preset data frame sending period; calculating the positioning stay time Ts：
Ts＝Tc×C
Wherein, TsFor positioning the dwell time, TcC is the number of positioning signals for a preset data frame transmission period.
As an improvement of the above scheme, the tag signal further includes sender identity information, and the smart secure campus positioning method further includes: calculating the positioning stay time of all the senders according to the steps of the scheme; computing systemThe average value of the positioning residence time of all the senders in the detection range of the gateway of the Internet of things is used for generating the average value of the stop time; calculating critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs a critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient; and judging whether the positioning residence time of the sender is greater than a critical alarm value in the detection range of the same Internet of things gateway, and if so, sending the identification information of the Internet of things gateway, the identity information of the sender and the positioning residence time to the client.
The invention also discloses a smart safe campus positioning system, which comprises: the signal acquisition module is used for acquiring all label signals sent by a sender and received by the gateway of the Internet of things, wherein the label signals comprise data frame numbers, receiving time and signal intensity; the signal correction module is used for correcting the signal intensity to generate corrected signal intensity; the classification positioning module is used for classifying the label signals of the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the strength of the correction signal, and taking the label signal corresponding to the maximum strength of the correction signal as the positioning signal of the data frame sequence number; and the residence time calculation module is used for calculating the residence time of the sender in the detection range of each Internet of things gateway according to the positioning signals and generating the positioning residence time.
As an improvement of the above scheme, the signal correction module includes: a calibration value obtaining unit for obtaining a preset calibration value; a calibration signal calculation unit for calculating a correction signal intensity Sr：
Sr＝Si+R
Wherein S isrTo correct the signal strength, SiFor signal strength, R is a preset calibration value; the classification positioning module comprises: the signal sequencing unit is used for sequencing the label signals with the same data frame sequence number according to the receiving time to generate a signal sequence; a time difference calculation unit for calculating the first label signal and other label signals in the signal sequence respectivelyThe difference in the time of receipt of the number; the signal classification unit is used for judging whether the receiving time difference is smaller than or equal to the preset error time one by one, if so, classifying the two label signals corresponding to the receiving time difference into a comparison set, and if not, classifying the first label signal into the comparison set; the residual signal processing unit is used for judging whether label signals are not included in the comparison set in the signal sequence, if so, sequencing the label signals which are not included in the comparison set according to the receiving time to generate a new signal sequence, and returning the new signal sequence to the time difference calculating unit; the residence time calculation module includes: the positioning counting unit is used for calculating the number of the positioning signals of all the data frame serial numbers received by each Internet of things gateway and generating the number of the positioning signals; a period obtaining unit, configured to obtain a preset data frame sending period; a time-of-rest calculation unit for calculating the positioning dwell time Ts：
Ts＝Tc×C
Wherein, TsFor positioning the dwell time, TcC is the number of positioning signals for a preset data frame transmission period.
As an improvement of the above scheme, the tag signal further includes sender identity information, and the smart secure campus positioning system further includes: each party positioning stop time calculation module is used for calculating the positioning stop time of all the senders through the functional module of the scheme; the average value calculating module is used for calculating the average value of the positioning residence time of all the senders in the detection range of the same Internet of things gateway and generating the average value of the stop time; a critical value calculating module for calculating critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs a critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient; and the critical alarm module is used for judging whether the positioning residence time of the sender is greater than a critical alarm value in the detection range of the same Internet of things gateway, and if so, sending the identification information of the Internet of things gateway, the identity information of the sender and the positioning residence time to the client.
Correspondingly, the invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores computer programs, and the processor executes the steps of the intelligent safety campus positioning method.
Accordingly, the present invention also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of the smart safe campus positioning method described above.
The implementation of the invention has the following beneficial effects:
the intelligent safe campus positioning method, the system, the equipment and the readable storage medium can accurately and efficiently collect and analyze the whereabouts information of students, reduce the cost and simultaneously reduce the damage to the privacy of the students.
Specifically, after the tag signal is obtained, the signal strength is corrected, so that errors of signal strength values received by different gateways are corrected, and the signal accuracy is improved. Then, classifying the label signals with the same data frame number to generate at least one comparison set, sequencing the label signals in each comparison set according to the strength of the correction signal, and taking the label signal corresponding to the maximum strength of the correction signal as a positioning signal of the data frame number, thereby determining which internet of things gateway the sender sending the signal with the data frame number is in the detection range of which internet of things gateway at a specific moment, and further calculating the retention time of the sender in the detection range of each internet of things gateway according to the positioning signal.
Further, according to the intelligent safe campus positioning method, system, device and readable storage medium provided by the invention, under the condition that a plurality of senders exist, on the basis of calculating the residence time of each sender in the detection range of each Internet of things gateway, an alarm can be given to the abnormal condition of the residence time of the sender in the detection range of a certain Internet of things gateway.
Drawings
FIG. 1 is a general flow diagram of a smart secure campus positioning method in accordance with a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for calibrating signal strength to generate a calibrated signal strength according to a first embodiment of the smart security campus positioning method of the present invention;
FIG. 3 is a flowchart of a first embodiment of a smart safe campus positioning method according to the present invention classifying tag signals of the same data frame number according to the receiving time to generate at least one comparison set;
fig. 4 is a flowchart illustrating a smart security campus positioning method according to a first embodiment of the present invention, which calculates the residence time of a sender in the detection range of each internet-of-things gateway according to a positioning signal, and generates a positioning residence time;
FIG. 5 is a flowchart of a second embodiment of a smart secure campus positioning method of the present invention;
FIG. 6 is a schematic diagram illustrating a smart safe campus positioning system according to a first embodiment of the present invention;
FIG. 7 is a schematic diagram of a signal calibration module according to a first embodiment of the smart secure campus positioning system of the present invention;
FIG. 8 is a block diagram of a categorized positioning module of a first embodiment of the smart security campus positioning system of the present invention;
FIG. 9 is a block diagram of a residence time calculation module of a first embodiment of the smart secure campus positioning system of the present invention;
FIG. 10 is a schematic diagram illustrating a smart secure campus positioning system according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings. It is only noted that the invention is intended to be limited to the specific forms set forth herein, including any reference to the drawings, as well as any other specific forms of embodiments of the invention.
FIG. 1 is a general flowchart of a first embodiment of a smart safe campus positioning method of the present invention, comprising:
s101, obtaining all label signals sent by a sender and received by the gateway of the Internet of things.
It should be noted that the sending party herein refers to a signal sending device, such as a radio frequency tag card, and the radio frequency signal sent by the sending party can be received and processed by the gateway of the internet of things. The signal sent by the sender is defined as a tag signal, and the tag signal includes a series of data, specifically including a data frame number, a receiving time, and a signal strength.
S102, correcting the signal intensity to generate corrected signal intensity.
After the label signal is obtained, the signal intensity is corrected, errors of signal intensity values received by different internet of things gateways can be corrected, and signal accuracy is improved.
S103, classifying the label signals of the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the corrected signal intensity, and taking the label signal corresponding to the maximum corrected signal intensity as the positioning signal of the data frame sequence number.
And classifying the tag signals, classifying the tag signals at the same receiving time into a comparison set, then sequencing the tag signals in the comparison set according to the strength of the correction signal, and taking the tag signal corresponding to the maximum strength of the correction signal as a positioning signal of the data frame number.
It should be noted that the signal strength of the positioning signal is strongest, and the internet of things gateway receiving the positioning signal is closest to the sender, which means that the sender is in the detection range of the internet of things gateway receiving the positioning signal with the data frame number at a specific receiving time.
And S104, calculating the stay time of the sender in the detection range of each Internet of things gateway according to the positioning signal to generate the positioning stay time.
Fig. 2 is a flowchart for performing a correction process on a signal strength to generate a corrected signal strength, which includes:
s201, acquiring a preset calibration value.
It should be noted that the preset calibration value is preset by a manufacturer according to the hardware configuration of each internet of things gateway, and the preset calibration value of the internet of things gateway is fixed after the internet of things gateway leaves a factory.
S202, calculating and correcting signal intensity Sr：
Sr＝Si+R
Wherein S isrTo correct the signal strength, SiFor signal strength, R is a preset calibration value. And the signal intensity is uniformly corrected, so that the accuracy of the signal intensity can be improved.
FIG. 3 is a flow chart of sorting tag signals of the same data frame number according to receiving time to generate at least one comparison set, comprising:
s301, sorting the label signals with the same data frame sequence number according to receiving time to generate a signal sequence.
S302, receiving time differences of the first tag signal and other tag signals in the signal sequence are calculated respectively.
And S303, judging whether the receiving time difference is less than or equal to the preset error time one by one, if so, classifying the two tag signals corresponding to the receiving time difference into a comparison set, and if not, classifying the first tag signal into the comparison set.
S304, judging whether label signals are not included in the comparison set in the signal sequence, if so, sorting the label signals which are not included in the comparison set according to the receiving time to generate a new signal sequence, and returning to the step S302.
The range of data frame numbers is 00-FF, and total number of data frame numbers is 256. This is because the interval of signal transmission is short, for example, only 3 seconds, if the signal is encoded by using a small number of data frame numbers, even by using only one data frame number, when the time difference occurs when different internet of things gateways receive the signal with the same data frame number, it is difficult to distinguish whether the time difference is the time difference of receiving the same signal, or two signals with the same data frame number are actually received due to the fact that the transmission period of the signal with the data frame number has passed. 256 data frame numbers are set, the sending interval of two signals with the same data frame number is 256 multiplied by 3 seconds, namely 768 seconds, and the period can be distinguished from the time difference of the same signal received by different internet of things gateways.
When classifying the label signals with the same data frame serial number, firstly, the label signals of the same data frame are sequenced according to the receiving time. After sorting, the receiving time of the tag signals in the signal sequence is from first to last, and the time difference between two adjacent tag signals is the smallest. At this time, receiving time differences between the first tag signal and other tag signals in the signal sequence are respectively calculated, and whether the receiving time differences are smaller than or equal to a preset error time is respectively judged, the preset error time should be much smaller than 768 seconds to prevent misjudgment, for example, the preset error time can be set to 6 seconds, and when the receiving time differences are judged to be smaller than or equal to the preset error time, the receiving time differences are normal time errors existing when different internet of things gateways receive the signals. At this time, the two tag signals corresponding to the receiving time difference may be classified into a comparison set, which is a set of tag signals at the same receiving time. In classification, if the tag signal has been previously included in the comparison set, there is no need to repeat the classification. For example, if the receiving time difference between the first tag signal and the second tag signal is smaller than the preset error time, the first tag signal and the second tag signal are classified into the comparison set, and then it is determined whether the receiving time difference between the first tag signal and the third tag signal is smaller than the preset error time.
In addition, in the two tag signals corresponding to the receiving time difference, the receiving time difference between the tag signal received later and the tag signal before the tag signal in the signal sequence is less than the preset error time. For example, if there are three tag signals in the signal sequence and the preset error time is 6 seconds, the second tag signals S are calculated respectively2With the first tag signal S1Is received with a time difference Δ t1And a third tag signal S3With the first tag signal S1Is received with a time difference Δ t2Then, determine Δ t1If the time difference is less than or equal to 6 seconds, if the judgment result is yes, the receiving time difference can be allowed, S is carried out1、S1Two tag signals are classified into a comparison set as tag signals of the same receiving time, otherwise, the receiving time difference is more than 6 seconds, and the second tag signal S2Should not be considered as the first tag signalS1Simultaneously received tag signals, in which case only the first tag signal S should be transmitted1And (4) classifying into a comparison set.
For the receiving time difference Δ t2And the difference Δ t between the determination and processing of (d) and the above-mentioned reception time1The judgment of (2) is similar to the process. Note that, when the reception time difference Δ t is obtained2When the time is less than or equal to 6 seconds, the tag signals in the signal sequence are sequenced according to the receiving time, so that the tag signals S are not only in the case1And the tag signal S3Is less than or equal to 6 seconds, the tag signal S2And the tag signal S3Is also less than or equal to 6 seconds.
In summary, the algorithm processing makes the receiving time difference between any two tag signals in the comparison set smaller than the preset error time.
Finally, a comparison set is typically generated. And if the label signals in the signal sequence are not classified into the comparison set, sorting the label signals which are not classified into the comparison set according to the receiving time to generate a new signal sequence, and returning to the step S302 for processing. In this case more than one comparison set is generated, which means that signals of the same data frame number are received at multiple times.
Fig. 4 is a flowchart for calculating the stay time of the sender in the detection range of each internet of things gateway according to the positioning signal, and generating the positioning stay time, which includes:
s401, calculating the number of the positioning signals of all the data frame serial numbers received by each Internet of things gateway, and generating the number of the positioning signals.
S402, acquiring a preset data frame sending period.
S403, calculating and positioning the stay time Ts：
Ts＝Tc×C
Wherein, TsFor positioning the dwell time, TcC is the number of positioning signals for a preset data frame transmission period. Note that T issThe time that the sender stays within the detection range of the specific internet of things gateway. Since two signals eachThe positioning signal is sent once every other fixed period, so that the positioning dwell time can be calculated by multiplying the period by the number of the positioning signals. In addition, when there are multiple internet of things gateways, the residence time of the sender in the detection range of different internet of things gateways needs to be calculated according to steps S401 to 403.
The tag signal also includes sender identity information.
FIG. 5 is a flow chart of a second embodiment of a smart secure campus positioning method, the second embodiment further comprising, in comparison to the first embodiment:
s501, positioning stay time of all senders is calculated according to the steps of the first embodiment;
s502, calculating the average value of the positioning residence time of all the senders in the detection range of the same Internet of things gateway, and generating the average value of the stop time.
S503, calculating a critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs a critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient.
S504, whether the positioning residence time of the sender in the detection range of the same Internet of things gateway is larger than a critical alarm value or not is judged, and if yes, the identification information of the Internet of things gateway, the identity information of the sender and the positioning residence time are sent to the client.
In the second embodiment, when a plurality of senders exist, on the basis of calculating the stay time of each sender in the detection range of each internet of things gateway, an alarm is given to the abnormal situation of the stay time of the sender in the detection range of a certain internet of things gateway. The determination that the residence time of the sender in a certain detection range of the internet of things gateway is abnormal can be made by judging whether the residence time of the sender in the detection range of the internet of things gateway is greatly deviated from the average residence time of all senders in the area. Mean time of rest TavgIs the average of the localized dwell times for all tags. The adjustment factor K is used to define the magnitude by which the positioning dwell time can be allowed to deviate from the average value of the dwell time. Such asA K of 1.2 means that the positioning dwell time can be allowed to deviate by 20% from the average value at rest. Beyond this magnitude, reporting is required.
The second embodiment method may be applied to observe the activity law of students. For example, set up the thing networking gateway in different areas of school, set up the thing networking gateway in library for example to and set up the thing networking gateway in the gymnasium, and let the student wear signalling equipment such as radio frequency identification card daily, thereby detect, calculate the dwell time of student at library, gymnasium, and judge whether the student is too long in these two regional dwell time and observe.
Correspondingly, the invention also discloses an intelligent safety campus positioning system,
FIG. 6 is a schematic diagram of a smart secure campus positioning system 100, comprising:
the signal acquisition module 1 is configured to acquire all tag signals sent by a sender and received by the internet of things gateway.
It should be noted that the sending party herein refers to a signal sending device, such as a radio frequency tag card, and the radio frequency signal sent by the sending party can be received and processed by the gateway of the internet of things. The signal sent by the sender is defined as a tag signal, and the tag signal includes a series of data, specifically including a data frame number, a receiving time, and a signal strength.
And the signal correction module 2 is used for correcting the signal intensity to generate corrected signal intensity.
After the label signal is obtained, the signal correction module 2 corrects the signal strength, can correct the signal strength value errors received by different internet of things gateways, and improves the signal accuracy.
And the classification positioning module 3 is used for classifying the label signals with the same data frame number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the strength of the correction signal, and taking the label signal corresponding to the maximum strength of the correction signal as the positioning signal of the data frame number.
The classification positioning module 3 classifies the tag signals, further classifies the tag signals at the same receiving time into a comparison set, then sorts the tag signals in the comparison set according to the corrected signal intensity, and takes the tag signal corresponding to the maximum corrected signal intensity as the positioning signal of the data frame number.
It should be noted that the signal strength of the positioning signal is strongest, and the internet of things gateway receiving the positioning signal is closest to the sender, which means that the sender is in the detection range of the internet of things gateway receiving the positioning signal with the data frame number at a specific receiving time.
And the residence time calculation module 4 is used for calculating the residence time of the sender in the detection range of each internet of things gateway according to the positioning signal and generating the positioning residence time.
Fig. 7 is a schematic structural diagram of the signal correction module 2, which includes:
and a calibration value obtaining unit 21 configured to obtain a preset calibration value.
It should be noted that the preset calibration value is preset by a manufacturer according to the hardware configuration of each internet of things gateway, and the preset calibration value of the internet of things gateway is fixed after the internet of things gateway leaves a factory.
A calibration signal calculation unit 22 for calculating a corrected signal intensity Sr：
Sr＝Si+R
Wherein S isrTo correct the signal strength, SiFor signal strength, R is a preset calibration value. The calibration signal calculation unit 22 performs uniform correction on all signal intensities, so that the accuracy of the signal intensities can be improved.
Fig. 8 is a schematic structural diagram of the classification positioning module 3, which includes:
the signal sorting unit 31 is configured to sort the tag signals with the same data frame number according to the receiving time, and generate a signal sequence.
And a time difference calculating unit 32, configured to calculate the receiving time differences between the first tag signal and the other tag signals in the signal sequence.
The signal classifying unit 33 is configured to determine whether the receiving time difference is smaller than or equal to a preset error time one by one, if yes, classify two tag signals corresponding to the receiving time difference into a comparison set, and if no, classify the first tag signal into the comparison set.
And the residual signal processing unit 34 is configured to determine whether a tag signal in the signal sequence is not included in the comparison set, if so, sort the tag signal that is not included in the comparison set according to the receiving time to generate a new signal sequence, and return to the time difference calculation unit.
The range of data frame numbers is 00-FF, and total number of data frame numbers is 256. This is because the interval of signal transmission is short, for example, only 3 seconds, if the signal is encoded by using a small number of data frame numbers, even by using only one data frame number, when the time difference occurs when different internet of things gateways receive the signal with the same data frame number, it is difficult to distinguish whether the time difference is the time difference of receiving the same signal, or two signals with the same data frame number are actually received due to the fact that the transmission period of the signal with the data frame number has passed. 256 data frame numbers are set, the sending interval of two signals with the same data frame number is 256 multiplied by 3 seconds, namely 768 seconds, and the period can be distinguished from the time difference of the same signal received by different internet of things gateways.
The signal sorting unit 31 sorts tag signals of the same data frame by reception time. After sorting, the receiving time of the tag signals in the signal sequence is from first to last, and the time difference between two adjacent tag signals is the smallest. The time difference calculating unit 32 calculates the reception time difference between the first tag signal and the other tag signals in the signal sequence. The signal classifying unit 33 determines whether the receiving time differences are smaller than or equal to a preset error time, which should be much smaller than 768 seconds to prevent erroneous determination, for example, it may be set to 6 seconds, and when the receiving time differences are smaller than or equal to the preset error time, it indicates that the receiving time differences are normal time errors existing when different internet of things gateways receive the signals. In this case, the signal classifying unit 33 may classify the two tag signals corresponding to the receiving time difference into a comparison set, where the comparison set is a set of tag signals at the same receiving time. The signal classification unit 33 does not need to repeat the classification if the tag signal has been previously included in the comparison set at the time of classification. For example, if the difference between the receiving time of the first tag signal and the receiving time of the second tag signal is smaller than the preset error time, the signal classifying unit 33 classifies the first tag signal and the second tag signal into the comparison set, and then the signal classifying unit 33 determines whether the difference between the receiving time of the first tag signal and the receiving time of the third tag signal is smaller than the preset error time.
In addition, in the two tag signals corresponding to the receiving time difference, the receiving time difference between the tag signal received later and the tag signal before the tag signal in the signal sequence is less than the preset error time. For example, if there are three tag signals in the signal sequence and the preset error time is 6 seconds, the second tag signals S are calculated respectively2With the first tag signal S1Is received with a time difference Δ t1And a third tag signal S3With the first tag signal S1Is received with a time difference Δ t2Then the signal classifying unit 33 judges Δ t1If the time is less than or equal to 6 seconds, if the judgment is yes, the receiving time difference can be allowed, the signal classifying unit 33 classifies S1、S1Two tag signals are classified into a comparison set as tag signals of the same receiving time, otherwise, the receiving time difference is more than 6 seconds, and the second tag signal S2Should not be considered as the first tag signal S1Simultaneously received tag signals, the signal classifying unit 33 should only classify the first tag signal S1And (4) classifying into a comparison set.
The signal classifying unit 33 classifies the reception time difference Δ t2And the difference Δ t between the determination and processing of (d) and the above-mentioned reception time1The judgment of (2) is similar to the process. Note that, when the reception time difference Δ t is obtained2When the time is less than or equal to 6 seconds, the tag signals in the signal sequence are sequenced according to the receiving time, so that the tag signals S are not only in the case1And the tag signal S3Is less than or equal to 6 seconds, the tag signal S2With tag letterNumber S3Is also less than or equal to 6 seconds.
In summary, the processing of the functional unit makes the difference between the receiving times of any two tag signals in the comparison set smaller than the preset error time.
Finally, a comparison set is generated by the signal sorting unit 31, the time difference calculation unit 32 and the signal classification unit 33. If there is a label signal in the signal sequence that is not included in the comparison set, the remaining signal processing unit 34 sorts the label signal that is not included in the comparison set according to the receiving time, generates a new signal sequence, and returns to the time difference calculation unit 32 for processing. In this case more than one comparison set is generated, which means that signals of the same data frame number are received at multiple times.
Fig. 9 is a schematic structural diagram of the residence time calculation module 4, which includes:
the positioning counting unit 41 is configured to calculate the number of positioning signals of all data frame numbers received by each internet of things gateway, and generate the number of the positioning signals;
a period obtaining unit 42, configured to obtain a preset data frame sending period;
a stop time calculation unit 43 for calculating the positioning stop time Ts：
Ts＝Tc×C
Wherein, TsFor positioning the dwell time, TcC is the number of positioning signals for a preset data frame transmission period.
Note that T issThe time that the sender stays within the detection range of the specific internet of things gateway. Since two signals are transmitted every fixed period, the stop time calculation unit 43 multiplies the period by the number of positioning signals to calculate the positioning stop time. In addition, when there are multiple internet of things gateways, the positioning counting unit 41, the period obtaining unit 42, and the time-out calculating unit 43 need to calculate the staying time of the sender in the detection range of different internet of things gateways.
Further, the tag signal also includes sender identity information.
Fig. 10 is a schematic diagram of a second embodiment 200 of a smart secure campus positioning system, the second embodiment further including, in comparison to the first embodiment:
each party positioning stop time calculation module 5 is used for calculating the positioning stop time of all the senders through the functional module of the scheme;
the average value calculating module 6 is used for calculating the average value of the positioning residence time of all the senders in the detection range of the same Internet of things gateway and generating the average value of the stop time;
a critical value calculating module 7 for calculating a critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs a critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient;
and the critical alarm module 8 is used for judging whether the positioning residence time of the sender is greater than a critical alarm value in the detection range of the same internet of things gateway, and if so, sending the identification information of the internet of things gateway, the identity information of the sender and the positioning residence time to the client.
In the second embodiment of the intelligent safety campus positioning system, under the condition that a plurality of senders exist, on the basis of calculating the residence time of each sender in the detection range of each Internet of things gateway, an alarm is given to the abnormal condition of the residence time of the sender in the detection range of one Internet of things gateway. To determine that the residence time of the sender in a certain detection range of the internet of things gateway is abnormal, the critical alarm module 8 may determine whether the residence time of the sender in the detection range of the internet of things gateway is greatly deviated from the average residence time of all senders in the area. Mean time of rest TavgIs the average of the localized dwell times for all tags. The adjustment factor K is used to define the magnitude by which the positioning dwell time can be allowed to deviate from the average value of the dwell time. For example, K is 1.2, meaning that the positioning dwell time can be allowed to deviate by 20% from the average value at rest. Beyond this magnitude, reporting is required.
The second embodiment of the smart safe campus positioning system of the invention can be applied to observe the activity rule of students. For example, the internet of things gateways are arranged in different areas of a school, for example, the internet of things gateways are arranged in a library, the internet of things gateways are arranged in a gymnasium, and students wear radio frequency tag cards and other signal sending devices in daily life, so that the second embodiment of the smart campus security positioning system detects and calculates the residence time of the students in the library and the gymnasium, and judges whether the residence time of the students in the two areas is too long to observe.
Correspondingly, the invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores computer programs, and the processor realizes the steps of the intelligent safety campus positioning method when executing the computer programs.
Meanwhile, the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to realize the steps of the intelligent safety campus positioning method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A smart secure campus positioning method, comprising:
acquiring label signals sent by a sender and received by all Internet of things gateways, wherein the label signals comprise data frame sequence numbers, receiving time and signal intensity;
correcting the signal intensity to generate corrected signal intensity;
classifying the label signals with the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the correction signal intensity, and taking the label signal corresponding to the maximum correction signal intensity as a positioning signal of the data frame sequence number;
and calculating the residence time of the sender in the detection range of each Internet of things gateway according to the positioning signal to generate the positioning residence time.
2. The smart campus positioning method of claim 1 wherein the step of performing a calibration process on the signal strength to generate a calibrated signal strength comprises:
acquiring a preset calibration value;
calculating a corrected signal strength Sr：
Sr＝Si+R
Wherein S isrFor said correction of signal strength, SiAnd R is the preset calibration value for the signal intensity.
3. The smart campus positioning method of claim 1 wherein the step of classifying tag signals of a same data frame number based on time of receipt to generate at least one comparison set comprises:
s1, sequencing the label signals with the same data frame sequence number according to the receiving time to generate a signal sequence;
s2, respectively calculating the receiving time difference between the first label signal and other label signals in the signal sequence;
s3, judging whether the receiving time difference is less than or equal to a preset error time one by one, if so, classifying the two tag signals corresponding to the receiving time difference into the comparison set, and if not, classifying the first tag signal into the comparison set;
and S4, judging whether a label signal is not included in the comparison set in the signal sequence, if so, sequencing the label signal which is not included in the comparison set according to the receiving time to generate a new signal sequence, and returning to the step S2.
4. The smart campus positioning method according to claim 1, wherein the step of calculating the residence time of the sender in the detection range of each gateway of the internet of things according to the positioning signal to generate the positioning residence time comprises:
calculating the number of positioning signals of all data frame serial numbers received by each Internet of things gateway to generate the number of the positioning signals;
acquiring a preset data frame sending period;
calculating the positioning dwell time Ts：
Ts＝Tc×C
Wherein, TsFor said positioning of the dwell time, TcAnd C is the number of the positioning signals.
5. The smart secure campus positioning method of claim 1 wherein said tag signal further includes sender identity information, said smart secure campus positioning method further comprising:
calculating the positioning dwell time of all senders according to the steps of claims 1-4;
calculating the average value of the positioning residence time of all the senders in the detection range of the same Internet of things gateway, and generating a stopping time average value;
calculating critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs the critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient;
and judging whether the positioning residence time of the sender is greater than the critical alarm value or not in the detection range of the same Internet of things gateway, and if so, sending the identification information of the Internet of things gateway, the identity information of the sender and the positioning residence time to a client.
6. A smart security campus positioning system, comprising:
the system comprises a signal acquisition module, a signal processing module and a signal processing module, wherein the signal acquisition module is used for acquiring label signals sent by a sender and received by all Internet of things gateways, and the label signals comprise data frame numbers, receiving time and signal strength;
the signal correction module is used for correcting the signal intensity to generate corrected signal intensity;
the classification positioning module is used for classifying the label signals with the same data frame sequence number according to the receiving time to generate at least one comparison set, sequencing the label signals in each comparison set according to the correction signal intensity, and taking the label signal corresponding to the maximum correction signal intensity as the positioning signal of the data frame sequence number;
and the residence time calculation module is used for calculating the residence time of the sender in the detection range of each Internet of things gateway according to the positioning signal to generate the positioning residence time.
7. The smart safe campus positioning system of claim 6 wherein said signal correction module comprises:
a calibration value obtaining unit for obtaining a preset calibration value;
a calibration signal calculation unit for calculating a correction signal intensity Sr：
Sr＝Si+R
Wherein S isrFor said correction of signal strength, SiFor the signal strength, R is the preset calibration value;
the classification positioning module comprises:
the signal sequencing unit is used for sequencing the label signals with the same data frame sequence number according to the receiving time to generate a signal sequence;
a time difference calculation unit, configured to calculate receiving time differences between the first tag signal and the other tag signals in the signal sequence, respectively;
the signal classification unit is used for judging whether the receiving time difference is smaller than or equal to the preset error time one by one, if so, classifying the two label signals corresponding to the receiving time difference into the comparison set, and if not, classifying the first label signal into the comparison set;
a residual signal processing unit, configured to determine whether a tag signal in the signal sequence is not included in the comparison set, if so, sort the tag signal that is not included in the comparison set according to the receiving time to generate a new signal sequence, and return the new signal sequence to the time difference calculation unit;
the residence time calculation module includes:
the positioning counting unit is used for calculating the number of the positioning signals of all the data frame serial numbers received by each Internet of things gateway and generating the number of the positioning signals;
a period obtaining unit, configured to obtain a preset data frame sending period;
a stop time calculation unit for calculating the positioning stop time Ts：
Ts＝Tc×C
Wherein, TsFor said positioning of the dwell time, TcAnd C is the number of the positioning signals.
8. The smart secure campus positioning system of claim 6 wherein said tag signal further includes sender identity information, said smart secure campus positioning system further comprising:
a party positioning stop time calculation module for calculating the positioning stop time of all senders through the functional module of claims 6-7;
the average value calculating module is used for calculating the average value of the positioning residence time of all the senders in the detection range of the same Internet of things gateway and generating a stopping time average value;
a critical value calculating module for calculating critical alarm value Ta：
Ta＝Tavg×K
Wherein, TaIs the critical alarm value, TavgThe average value of the stopping time is K, and K is an adjusting coefficient;
and the critical alarm module is used for judging whether the positioning residence time of the sender is greater than the critical alarm value in the detection range of the same Internet of things gateway, and if so, sending the identification information of the Internet of things gateway, the identity information of the sender and the positioning residence time to the client.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
CN201911414456.1A 2019-12-31 2019-12-31 Intelligent safety campus positioning method, system, equipment and readable storage medium Pending CN111132052A (en)

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