CN112035537A - Method for rapidly counting behaviors of moving object monitored by multiple sensors - Google Patents
Method for rapidly counting behaviors of moving object monitored by multiple sensors Download PDFInfo
- Publication number
- CN112035537A CN112035537A CN202010661062.2A CN202010661062A CN112035537A CN 112035537 A CN112035537 A CN 112035537A CN 202010661062 A CN202010661062 A CN 202010661062A CN 112035537 A CN112035537 A CN 112035537A
- Authority
- CN
- China
- Prior art keywords
- time
- people
- data
- sensor
- store
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/258—Data format conversion from or to a database
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/31—Programming languages or programming paradigms
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Data Mining & Analysis (AREA)
- Fuzzy Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for rapidly counting the behaviors of a moving object monitored by multiple sensors, which comprises the following steps: 1) a business process; 2) a technical architecture; 3) the technical scheme is as follows. The invention belongs to the technical field of a data statistical device for the behavior of a moving object under the line, in particular to a method for rapidly counting the behavior of the moving object monitored by a plurality of sensors.
Description
Technical Field
The invention belongs to the technical field of a device for counting behavior data of an on-line moving object, and particularly relates to a method for quickly counting behaviors of a moving object monitored by multiple sensors.
Background
The existing scheme mainly focuses on analyzing the jumping relation between pages, and the statistics of the stay time length is rarely related; the flexible statistics of the stay time at the server side is lacked, and the stay time statistics provided by individual schemes is mainly based on the statistics of a timer at the client side, and the statistical method is as follows: when a client enters a page, a timer is started to time until the client jumps to other pages, which has the disadvantage that the stay time cannot be reported to a server in time when the client closes a browser; the offline behavior statistics is lacked, most of the existing schemes collect data statistics based on online web pages or added codes in applications, and the stay time and related index statistics for actual object movement under the multi-sensor collection line are not performed.
Disclosure of Invention
Aiming at the situation and overcoming the defects of the prior art, the invention provides a method for rapidly counting the behaviors of a moving object monitored by multiple sensors, which collects unique object identifiers and collection time collected by multiple sensor devices to perform statistical analysis on stay time and access frequency, logically defines a set of data storage formats which occupy small memory and are rapidly retrieved, designs a set of mapping sensor method from logic definition, simply realizes distributed calculation, and adopts a redis plug-in to realize the statistics in the memory based on redis by using a c language.
The technical scheme adopted by the invention is as follows: the invention discloses a method for rapidly counting the behaviors of a moving object monitored by multiple sensors, which comprises the following steps:
first, business process
a. Deploying a plurality of sensors on the exhibition site to collect data of objects or people;
b. an administrator can bind sensors- > monitoring points- > monitoring areas- > stores/activities in the system;
c. extracting data from the sensor and transmitting the data to the system;
d. the system writes the collected data into the redis according to a specified format;
e. setting a related threshold value of the stay time length in the system by a user;
f. when a user checks the chart, the staying time, the frequency of incoming and outgoing are counted in redis by combining the relevant threshold values through the interface;
second, technical framework
a. And (4) report display: the method is mainly developed based on a SpringMvc framework, and a user can set a threshold and view a report;
b. data reception: after data reported by a sensor is collected, the integrity and the legality of the data are verified and analyzed into a readable format;
c. data storage: storing the readable data after data receiving and analyzing to Redis according to a data statistics format;
d. and (3) data statistics: when a user views a report, the user calls an autonomous Redis plug-in command through an interface to read stored data for calculation;
third, the technical scheme
a. An entity relationship;
b. a storage format;
c. a middle layer computing logic;
d. the underlying computational logic.
Further, the sensors in step 1) are used for scanning regularly to collect data, for example, scanning once every 30 seconds, and the signal sent by an object is also intermittent, for example, once every 40 seconds, so that the data of an object on a single sensor is discontinuous, and the server needs to comprehensively draw a timeline of the object for the data of the object by all the sensors in a certain range (store/area) at the same time; after all time points of a certain object are arranged, whether the two points are connected into a line is determined in the interrupt duration set by a user from an interface; data collected by a sensor is typically "sensor ID, object ID, collection time"; the invention uses the ID of the monitoring point as the minimum granularity for statistics, so that the ID of the monitoring point corresponds to different ID of the sensor at different time, for example, the sensor is damaged and needs to be replaced, and the continuity of the data needs to be maintained at the moment.
Further, the physical relationship in step 3) is that a manufacturer has a plurality of stores/activities, a plurality of monitoring areas are provided under each store, a plurality of monitoring points are provided under each monitoring area, each monitoring point corresponds to a sensor, and each sensor collects a plurality of object IDs.
Further, the storage format in step 3) includes a binary type and a set type, the binary type is that a certain object ID appears in minutes on a certain sensor day; key: sensor ID (3B) + object ID (6B) + year × 1000+ day shift (2B); value: 0-1439 minutes (1440 bits) a day; 0 indicates that there is no data for the minute object ID, and 1 indicates that there is data for the minute object ID; the set type is all object ID sets collected by the sensor on a certain day; key: p + sensor ID (3 bytes) + year 1000+ day offset (2B); member (b): a binary set of object IDs (6B).
Further, the middle layer calculation logic in step 3) is to first judge which level the statistical level is according to the query conditions, and extract the corresponding monitoring points according to each level, wherein the monitoring points are the minimum units of the middle layer calculation; inquiring the business hours (such as Monday to Friday) of stores/activities/the activity hours (such as 4 months, 18 days to 4 months, 25 days) and the daily business hours (9:00 to 17:00) set by the user; inquiring the interruption time set by a user, and judging the number of people and the stay time; inquiring the store entrance/concern duration and the deep visit duration set by the user; for calculating store-in/care stay time, visit stay time; judging the time granularity: dividing the query time range into small segments in all, month, week, day and hour; inquiring a sensor ID set corresponding to each monitoring point according to the time period; merging each sensor ID set and calling bottom computing logic to carry out statistics; and summarizing the statistical results and outputting the statistical results to a chart on the interface.
Further, the bottom layer computing logic in step 3) is operated in a Redis plug-in, provides a user-defined Redis command to the outside, directly operates Redis data, does not need to be transmitted through a network, is high in speed, can directly perform bit operation on the data by adopting C language writing, and avoids occupying memory space due to pointer operation.
Further, the bottom layer computing logic comprises binary OR operation, time line splitting, minute data combining of a plurality of sensors and statistics of number of people, number of times of entering/paying attention to people, number of times of deep visiting people and total staying time in a certain day of an object ID; the binary OR operation function is to realize the OR operation of two binary character strings which can be transmitted by the function; the parameters are a pointer of the character string a, the length of the character string a, a pointer of the character string b and the length of the character string b; returning a pointer with the value of OR operation result string array c, and the length; comparing the lengths of two character strings, taking each bit of the shortest character string and each bit of another character string to perform an OR operation, wherein 0|1 ═ 1, 1|0 ═ 1, and 0|0 ═ 0; the time line splitting function is that a time line composed of time points is split into a plurality of time periods according to a specified interrupt duration threshold; the parameters are a pointer of a timeline character string timeline, the length of the timeline character string timeline and the interruption duration; the return value is a time period array in the format of start time 1, end time 1, start time 2, end time 2 … start time n, end time n, e.g., [100, 102, 106, 106] representing minutes 100-102, 106-106; the minimum granularity of the time line is minutes, the time line represents 1 day, the length of the character string is fixed to 1440 bits, namely, 1 day is 1440 minutes, the time line character string is intercepted, the condition that the exceeding of 1440 bits causes border crossing is avoided, each bit is circulated, the starting time is recorded when 1 is met, the ending time moves backwards all the time, the calculation is started when 0 is met, whether the distance from the previous 1 exceeds the interruption duration or not is judged, if the distance does not exceed the first 1, the ending time continues to move until 1440 or the interruption duration is exceeded; the function of merging the minute data of the plurality of sensors is to merge the minute data of the plurality of sensors of the same object ID, because the data collected by each sensor is only a part, all the related data need to be merged; the parameters are sensor ID array, object ID, year and day offset; the return value is the pointer and the length of the minute character string after the return combination; the function of counting the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of an object ID in data collected by a plurality of sensors in a certain day is to count the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of the object ID in the certain day; the input parameters are a sensor ID array, an object ID, year, day offset, interruption time, people stream retention time, store-entering/attention retention time and deep visit retention time; the output parameters are the number of people, the number of people entering a store/paying attention to the store, the number of people visiting deeply and the total staying time; no return value; the result in the process is returned through the output parameter, the input output parameter is the statistical result of the previous object ID, the process adds the statistical result to the statistical result of the previous object, thereby achieving the purpose of counting a plurality of object IDs, the difference between the number of people and the number of people is whether to remove the weight, the number of people is generated by the interruption duration, the shorter the interruption duration is, the more the number of people is, the number of people is the result of removing the weight of a plurality of object IDs with the number of people more than 0 meeting the condition, and when the interruption duration is set to be 1 day, the number of people is equal to the number of people.
Furthermore, the stay time duration refers to a time difference between an object entering an area and an object leaving the area, the moving object refers to an object which can be monitored by a sensor, such as a mobile device, a person, an animal and the like, in the invention, the unique object identifier refers to an identifier which can be collected in data and is used for distinguishing a certain type of object, such as a fingerprint, an iris, a weight and a height of a person, a network card address of a mobile phone, bluetooth, a sim card and the like; the Redis (remote Dictionary Server), namely remote Dictionary service, is an open source log-type and Key-Value database which is written by using ANSI C language, supports network, can be based on memory and can also be persistent, and provides API of multiple languages; the monitoring point is a logic concept, which means a sensor placed at a certain position, generally one monitoring point corresponds to one sensor, but if the sensor is damaged, the sensor can be replaced, and at this time, the ID of the monitoring point corresponding to the ID of the sensor needs to be bound in the invention; the interruption time length is a threshold value in the invention, if the certain object ID has no data in a period (exceeding the threshold value) of time, the object ID is judged to leave once, for example, the interruption time length is set to be 5 minutes, the object ID has data at 9 points 59 time-sharing, no data at 10 points 0, and no data at 10 points 5, the object ID is considered to leave once; the BIT (BIT) is a unit of information quantity, is obtained by transliteration of English BIT, is a BIT in Binary digits, is a unit of measurement of information quantity, is a minimum unit of information quantity, and is necessary information for reducing the number of alternative stimuli by half under the condition that different choices are needed, namely the information quantity (BIT number) of a signal is equal to a logarithmic value of the number of stimuli of the signal with the base of 2, and L. Hartley 1928 considers that the measurement of the unit of selection of the information quantity is most suitable; the Value is a data Value stored in Redis, and the Key is a unique name of data in Redis and is used for searching the data Value; the entering/paying attention time is a threshold value in the invention, if some object ID has data all the time in a period (exceeding the threshold value), the entering/paying attention is judged, if some object ID is in the store/activity range and has not left in the business hours, and the duration time exceeds the threshold value; the flow rate is the number of all object IDs collected by all sensors in the store/event (once per departure); the number of people is the number of people after the weight of the flow of people is removed; the first store/attention is the number of people who enter/pay attention to the first store/attention object in the time period; the multiple store/attention is the number of people who enter/pay attention for 2 times or more in the store/attention object in the time period; the Tomcat server is a free WEB application server with open source codes, belongs to a lightweight application server, is commonly used in small and medium-sized systems and occasions where concurrent access users are not many, and is the first choice for developing and debugging JSP programs; the Spring MVC belongs to a follow-up product of Spring FrameWork and is fused in Spring WEB Flow, a Spring framework provides a full-function MVC module for constructing a WEB application program, and an MVC framework in which Spring can be inserted is used, so that when the Spring is used for WEB development, the Spring MVC framework in which the Spring is used can be selected or other MVC development frameworks such as Struts1 (which are not used generally at present), Struts 2 (which are used in common old projects) and the like can be integrated.
The invention with the structure has the following beneficial effects: the method for rapidly counting the behaviors of the moving objects monitored by the multiple sensors collects unique object identifiers and collection time collected by multiple sensor devices to perform statistical analysis on stay time and access frequency, logically defines a set of data storage formats which occupy small memory and are rapidly retrieved, designs a set of mapping sensor method from logical definition, simply realizes distributed calculation, and realizes counting based on a memory by adopting a redis plug-in using a c language.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a method for rapidly counting the behaviors of a moving object monitored by multiple sensors, which comprises the following steps:
first, business process
g. Deploying a plurality of sensors on the exhibition site to collect data of objects or people;
h. an administrator can bind sensors- > monitoring points- > monitoring areas- > stores/activities in the system;
i. extracting data from the sensor and transmitting the data to the system;
j. the system writes the collected data into the redis according to a specified format;
k. setting a related threshold value of the stay time length in the system by a user;
l, when a user checks the chart, the staying time length, the frequency of access and the like are counted in redis by combining the relevant threshold values through an interface;
second, technical framework
a. And (4) report display: the method is mainly developed based on a SpringMvc framework, and a user can set a threshold and view a report;
b. data reception: after data reported by a sensor is collected, the integrity and the legality of the data are verified and analyzed into a readable format;
c. data storage: storing the readable data after data receiving and analyzing to Redis according to a data statistics format;
d. and (3) data statistics: when a user views a report, the user calls an autonomous Redis plug-in command through an interface to read stored data for calculation;
third, the technical scheme
a. An entity relationship;
b. a storage format;
c. a middle layer computing logic;
d. the underlying computational logic.
The sensors in step 1) scan and collect data at regular time, for example, scan every 30 seconds, and the signal sent by an object is also intermittent, for example, once every 40 seconds, so that the data of an object on a single sensor is discontinuous, and the server needs to comprehensively draw a timeline of the object by all sensors for the data of the object in a certain range (store/area) at the same time; after all time points of a certain object are arranged, whether the two points are connected into a line is determined in the interrupt duration set by a user from an interface; data collected by a sensor is typically "sensor ID, object ID, collection time"; the invention uses the ID of the monitoring point as the minimum granularity for statistics, so that the ID of the monitoring point corresponds to different ID of the sensor at different time, for example, the sensor is damaged and needs to be replaced, and the continuity of the data needs to be maintained at the moment.
The physical relationship in the step 3) is that a manufacturer has a plurality of stores/activities, a plurality of monitoring areas are arranged under each store, a plurality of monitoring points are arranged under each monitoring area, each monitoring point corresponds to one sensor, and each sensor collects a plurality of object IDs.
The storage format in the step 3) comprises a binary type and a set type, wherein the binary type is that a certain object ID appears in minutes on a certain sensor at a certain day; key: sensor ID (3B) + object ID (6B) + year × 1000+ day shift (2B); value: 0-1439 minutes (1440 bits) a day; 0 indicates that there is no data for the minute object ID, and 1 indicates that there is data for the minute object ID; the set type is all object ID sets collected by the sensor on a certain day; key: p + sensor ID (3 bytes) + year 1000+ day offset (2B); member (b): a binary set of object IDs (6B).
The middle layer calculation logic of the step 3) is that firstly, the statistical grade is judged according to the query condition, corresponding monitoring points are respectively extracted according to each grade, and the monitoring points are the minimum units of the middle layer calculation; inquiring the business hours (such as Monday to Friday) of stores/activities/the activity hours (such as 4 months, 18 days to 4 months, 25 days) and the daily business hours (9:00 to 17:00) set by the user; inquiring the interruption time set by a user, and judging the number of people and the stay time; inquiring the store entrance/concern duration and the deep visit duration set by the user; for calculating store-in/care stay time, visit stay time; judging the time granularity: dividing the query time range into small segments in all, month, week, day and hour; inquiring a sensor ID set corresponding to each monitoring point according to the time period; merging each sensor ID set and calling bottom computing logic to carry out statistics; and summarizing the statistical results and outputting the statistical results to a chart on the interface.
The bottom layer computing logic in the step 3) is operated in a Redis plug-in, provides a user-defined Redis command to the outside, directly operates Redis data, does not need to be transmitted through a network, has high speed, can directly perform bit operation on the data by adopting C language writing, operates a pointer, and avoids occupying memory space.
The bottom layer computing logic comprises binary OR operation, time line splitting, minute data combining of a plurality of sensors and statistics of number of people in a certain day of an object ID, number of people, number of times of entering a store/paying attention, number of times of visiting people and total staying time; the binary OR operation function is to realize the OR operation of two binary character strings which can be transmitted by the function; the parameters are a pointer of the character string a, the length of the character string a, a pointer of the character string b and the length of the character string b; returning a pointer with the value of OR operation result string array c, and the length; comparing the lengths of two character strings, taking each bit of the shortest character string and each bit of another character string to perform an OR operation, wherein 0|1 ═ 1, 1|0 ═ 1, and 0|0 ═ 0; the time line splitting function is that a time line composed of time points is split into a plurality of time periods according to a specified interrupt duration threshold; the parameters are a pointer of a timeline character string timeline, the length of the timeline character string timeline and the interruption duration; the return value is a time period array in the format of start time 1, end time 1, start time 2, end time 2 … start time n, end time n, e.g., [100, 102, 106, 106] representing minutes 100-102, 106-106; the minimum granularity of the time line is minutes, the time line represents 1 day, the length of the character string is fixed to 1440 bits, namely, 1 day is 1440 minutes, the time line character string is intercepted, the condition that the exceeding of 1440 bits causes border crossing is avoided, each bit is circulated, the starting time is recorded when 1 is met, the ending time moves backwards all the time, the calculation is started when 0 is met, whether the distance from the previous 1 exceeds the interruption duration or not is judged, if the distance does not exceed the first 1, the ending time continues to move until 1440 or the interruption duration is exceeded; the function of merging the minute data of the plurality of sensors is to merge the minute data of the plurality of sensors of the same object ID, because the data collected by each sensor is only a part, all the related data need to be merged; the parameters are sensor ID array, object ID, year and day offset; the return value is the pointer and the length of the minute character string after the return combination; the function of counting the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of an object ID in data collected by a plurality of sensors in a certain day is to count the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of the object ID in the certain day; the input parameters are a sensor ID array, an object ID, year, day offset, interruption time, people stream retention time, store-entering/attention retention time and deep visit retention time; the output parameters are the number of people, the number of people entering a store/paying attention to the store, the number of people visiting deeply and the total staying time; no return value; the result in the process is returned through the output parameter, the input output parameter is the statistical result of the previous object ID, the process adds the statistical result to the statistical result of the previous object, thereby achieving the purpose of counting a plurality of object IDs, the difference between the number of people and the number of people is whether to remove the weight, the number of people is generated by the interruption duration, the shorter the interruption duration is, the more the number of people is, the number of people is the result of removing the weight of a plurality of object IDs with the number of people more than 0 meeting the condition, and when the interruption duration is set to be 1 day, the number of people is equal to the number of people.
The stay time is the time difference between an object entering an area and leaving the area, the moving object is an object which can be monitored by a sensor, such as mobile equipment, people, animals and the like, in the invention, and the object unique identification is an identification which can be used for distinguishing certain types of objects in collected data, such as fingerprints, irises, weights and heights of people, network card addresses of mobile phones, Bluetooth, sim cards and the like; the Redis (remote Dictionary Server), namely remote Dictionary service, is an open source log-type and Key-Value database which is written by using ANSI C language, supports network, can be based on memory and can also be persistent, and provides API of multiple languages; the monitoring point is a logic concept, which means a sensor placed at a certain position, generally one monitoring point corresponds to one sensor, but if the sensor is damaged, the sensor can be replaced, and at this time, the ID of the monitoring point corresponding to the ID of the sensor needs to be bound in the invention; the interruption time length is a threshold value in the invention, if the certain object ID has no data in a period (exceeding the threshold value) of time, the object ID is judged to leave once, for example, the interruption time length is set to be 5 minutes, the object ID has data at 9 points 59 time-sharing, no data at 10 points 0, and no data at 10 points 5, the object ID is considered to leave once; the BIT (BIT) is a unit of information quantity, is obtained by transliteration of English BIT, is a BIT in Binary digits, is a unit of measurement of information quantity, is a minimum unit of information quantity, and is necessary information for reducing the number of alternative stimuli by half under the condition that different choices are needed, namely the information quantity (BIT number) of a signal is equal to a logarithmic value of the number of stimuli of the signal with the base of 2, and L. Hartley 1928 considers that the measurement of the unit of selection of the information quantity is most suitable; the Value is a data Value stored in Redis, and the Key is a unique name of data in Redis and is used for searching the data Value; the entering/paying attention time is a threshold value in the invention, if some object ID has data all the time in a period (exceeding the threshold value), the entering/paying attention is judged, if some object ID is in the store/activity range and has not left in the business hours, and the duration time exceeds the threshold value; the flow rate is the number of all object IDs collected by all sensors in the store/event (once per departure); the number of people is the number of people after the weight of the flow of people is removed; the first store/attention is the number of people who enter/pay attention to the first store/attention object in the time period; the multiple store/attention is the number of people who enter/pay attention for 2 times or more in the store/attention object in the time period; the Tomcat server is a free WEB application server with open source codes, belongs to a lightweight application server, is commonly used in small and medium-sized systems and occasions where concurrent access users are not many, and is the first choice for developing and debugging JSP programs; the Spring MVC belongs to a follow-up product of Spring FrameWork and is fused in Spring WEB Flow, a Spring framework provides a full-function MVC module for constructing a WEB application program, and an MVC framework in which Spring can be inserted is used, so that when the Spring is used for WEB development, the Spring MVC framework in which the Spring is used can be selected or other MVC development frameworks such as Struts1 (which are not used generally at present), Struts 2 (which are used in common old projects) and the like can be integrated.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The present invention and the embodiments thereof have been described above, but the description is not limited to the embodiments, and the actual configuration is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for rapidly counting the behaviors of a moving object monitored by multiple sensors is characterized by comprising the following steps:
first, business process
a. Deploying a plurality of sensors on the exhibition site to collect data of objects or people;
b. an administrator can bind sensors- > monitoring points- > monitoring areas- > stores/activities in the system;
c. extracting data from the sensor and transmitting the data to the system;
d. the system writes the collected data into the redis according to a specified format;
e. setting a related threshold value of the stay time length in the system by a user;
when a user checks the chart, the staying time, the frequency of incoming and outgoing are counted in redis by combining the relevant threshold values through the interface;
second, technical framework
a. And (4) report display: the method is mainly developed based on a SpringMvc framework, and a user can set a threshold and view a report;
b. data reception: after data reported by a sensor is collected, the integrity and the legality of the data are verified and analyzed into a readable format;
c. data storage: storing the readable data after data receiving and analyzing to Redis according to a data statistics format;
d. and (3) data statistics: when a user views a report, the user calls an autonomous Redis plug-in command through an interface to read stored data for calculation;
third, the technical scheme
a. An entity relationship;
b. a storage format;
c. a middle layer computing logic;
d. the underlying computational logic.
2. The method of claim 1, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the sensors in step 1) scan and collect data at regular time, for example, scan every 30 seconds, and the object sends signals intermittently, for example, once every 40 seconds, so that the data of an object on a single sensor is discontinuous, and the server needs to comprehensively draw a time line of the object by all the sensors for the data of the object in a certain range at the same time; after all time points of a certain object are arranged, whether the two points are connected into a line is determined in the interrupt duration set by a user from an interface; data collected by a sensor is typically "sensor ID, object ID, collection time"; the invention uses the ID of the monitoring point as the minimum granularity for statistics, so that the ID of the monitoring point corresponds to different ID of the sensor at different time, for example, the sensor is damaged and needs to be replaced, and the continuity of the data needs to be maintained at the moment.
3. The method of claim 2, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the physical relationship in the step 3) is that a manufacturer has a plurality of stores/activities, a plurality of monitoring areas are arranged under each store, a plurality of monitoring points are arranged under each monitoring area, each monitoring point corresponds to one sensor, and each sensor collects a plurality of object IDs.
4. The method of claim 3, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the storage format in the step 3) comprises a binary type and a set type, wherein the binary type is that a certain object ID appears in minutes on a certain sensor at a certain day; key: sensor ID + object ID + year 1000+ day shift; value: 0-1439 minutes a day; 0 indicates that there is no data for the minute object ID, and 1 indicates that there is data for the minute object ID; the set type is all object ID sets collected by the sensor on a certain day; key: p + sensor ID + year 1000+ day shift; member (b): a binary set of object IDs.
5. The method of claim 4, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the middle layer calculation logic of the step 3) is that firstly, the statistical grade is judged according to the query condition, corresponding monitoring points are respectively extracted according to each grade, and the monitoring points are the minimum units of the middle layer calculation; inquiring the business hours/activity hours of stores/activities set by a user, wherein the business hours are daily; inquiring the interruption time set by a user, and judging the number of people and the stay time; inquiring the store entrance/concern duration and the deep visit duration set by the user; for calculating store-in/care stay time, visit stay time; judging the time granularity: dividing the query time range into small segments in all, month, week, day and hour; inquiring a sensor ID set corresponding to each monitoring point according to the time period; merging each sensor ID set and calling bottom computing logic to carry out statistics; and summarizing the statistical results and outputting the statistical results to a chart on the interface.
6. The method of claim 5, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the bottom layer computing logic in the step 3) is operated in a Redis plug-in, provides a user-defined Redis command to the outside, directly operates Redis data, does not need to be transmitted through a network, has high speed, can directly perform bit operation on the data by adopting C language writing, operates a pointer, and avoids occupying memory space.
7. The method of claim 6, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the bottom layer computing logic comprises binary OR operation, time line splitting, minute data combining of a plurality of sensors and statistics of number of people in a certain day of an object ID, number of people, number of times of entering a store/paying attention, number of times of visiting people and total staying time; the binary OR operation function is to realize the OR operation of two binary character strings which can be transmitted by the function; the parameters are a pointer of the character string a, the length of the character string a, a pointer of the character string b and the length of the character string b; returning a pointer with the value of OR operation result string array c, and the length; comparing the lengths of two character strings, taking each bit of the shortest character string and each bit of another character string to perform an OR operation, wherein 0|1 ═ 1, 1|0 ═ 1, and 0|0 ═ 0; the time line splitting function is that a time line composed of time points is split into a plurality of time periods according to a specified interrupt duration threshold; the parameters are a pointer of a timeline character string timeline, the length of the timeline character string timeline and the interruption duration; the return value is a time period array in the format of start time 1, end time 1, start time 2, end time 2 … start time n, end time n, e.g., [100, 102, 106, 106] representing minutes 100-102, 106-106; the minimum granularity of the time line is minutes, the time line represents 1 day, the length of the character string is fixed to 1440 bits, namely, 1 day is 1440 minutes, the time line character string is intercepted, the condition that the exceeding of 1440 bits causes border crossing is avoided, each bit is circulated, the starting time is recorded when 1 is met, the ending time moves backwards all the time, the calculation is started when 0 is met, whether the distance from the previous 1 exceeds the interruption duration or not is judged, if the distance does not exceed the first 1, the ending time continues to move until 1440 or the interruption duration is exceeded; the function of merging the minute data of the plurality of sensors is to merge the minute data of the plurality of sensors of the same object ID, because the data collected by each sensor is only a part, all the related data need to be merged; the parameters are sensor ID array, object ID, year and day offset; the return value is the pointer and the length of the minute character string after the return combination; the function of counting the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of an object ID in data collected by a plurality of sensors in a certain day is to count the number of people, the number of times of entering a store/close to people, the number of people entering a store/close to people, the number of times of deep visiting people and the total staying time of the object ID in the certain day; the input parameters are a sensor ID array, an object ID, year, day offset, interruption time, people stream retention time, store-entering/attention retention time and deep visit retention time; the output parameters are the number of people, the number of people entering a store/paying attention to the store, the number of people visiting deeply and the total staying time; no return value; the result in the process is returned through the output parameter, the input output parameter is the statistical result of the previous object ID, the process adds the statistical result to the statistical result of the previous object, thereby achieving the purpose of counting a plurality of object IDs, the difference between the number of people and the number of people is whether to remove the weight, the number of people is generated by the interruption duration, the shorter the interruption duration is, the more the number of people is, the number of people is the result of removing the weight of a plurality of object IDs with the number of people more than 0 meeting the condition, and when the interruption duration is set to be 1 day, the number of people is equal to the number of people.
8. The method of claim 7, wherein the fast statistical multi-sensor monitoring behavior of moving objects comprises: the stay time is the time difference between an object entering an area and leaving the area, the moving object is an object which can be monitored by a sensor, such as mobile equipment, people, animals and the like, in the invention, and the object unique identification is an identification which can be used for distinguishing certain types of objects in collected data, such as fingerprints, irises, weights and heights of people, network card addresses of mobile phones, Bluetooth, sim cards and the like; the Redis, namely remote dictionary service, is an open-source log-type and Key-Value database which is compiled by using C language, supports network, can be based on memory and can also be persistent, and provides API of multiple languages; the monitoring point is a logic concept, which means a sensor placed at a certain position, generally one monitoring point corresponds to one sensor, but if the sensor is damaged, the sensor can be replaced, and at this time, the ID of the monitoring point corresponding to the ID of the sensor needs to be bound in the invention; the interruption time length is a threshold value in the invention, if a certain object ID has no data in a period of time, the object ID is judged to leave, for example, the interruption time length is set to be 5 minutes, the object ID has data in 9-point 59 time-sharing, no data exists in 10-point 0 time-sharing, and the object ID is considered to leave in 10-point 5 time-sharing; the BIT, a computer professional term, is an information quantity unit, is translated from English BIT, is also a BIT in a binary number, and is a measurement unit of the information quantity, which is a minimum unit of the information quantity, and is necessary information for reducing the number of alternative stimuli by half under the condition that different choices are needed, namely the information quantity of a signal is equal to a logarithmic value of which the number of stimuli of the signal is 2 as a base number, and L. Hartley 1928 considers that the measurement of the information quantity by using the logarithmic unit is most appropriate; the Value is a data Value stored in Redis, and the Key is a unique name of data in Redis and is used for searching the data Value; the entering/paying attention time is a threshold value in the invention, if the ID of a certain object has data all the time, the entering/paying attention time is judged to be close to the store/paying attention, if the ID of the certain object is in the store/activity range and has not left in the business hours, and the duration time exceeds the threshold value; the pedestrian volume is the number of times of all object IDs collected by all sensors in the store/activity; the number of people is the number of people after the weight of the flow of people is removed; the first store/attention is the number of people who enter/pay attention to the first store/attention object in the time period; the multiple store/attention is the number of people who enter/pay attention for 2 times or more in the store/attention object in the time period; the Tomcat server is a free WEB application server with open source codes, belongs to a lightweight application server, is commonly used in small and medium-sized systems and occasions where concurrent access users are not many, and is the first choice for developing and debugging JSP programs; the Spring MVC belongs to a subsequent product of Spring FrameWork and is fused in Spring WEB Flow, a Spring framework provides a full-function MVC module for constructing an application program, and the MVC framework into which the Spring can be inserted is used, so that when the Spring is used for WEB development, the Spring MVC framework using the Spring can be selected or other MVC development frameworks can be integrated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010661062.2A CN112035537B (en) | 2020-07-10 | 2020-07-10 | Method for rapidly counting behavior of mobile object monitored by multiple sensors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010661062.2A CN112035537B (en) | 2020-07-10 | 2020-07-10 | Method for rapidly counting behavior of mobile object monitored by multiple sensors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112035537A true CN112035537A (en) | 2020-12-04 |
| CN112035537B CN112035537B (en) | 2023-10-20 |
Family
ID=73579012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010661062.2A Active CN112035537B (en) | 2020-07-10 | 2020-07-10 | Method for rapidly counting behavior of mobile object monitored by multiple sensors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112035537B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101051334A (en) * | 2006-04-06 | 2007-10-10 | 香港理工大学 | Structure health monitoring and information managing system and its method |
| CN104123388A (en) * | 2014-08-07 | 2014-10-29 | 武汉大学 | Massive-sensing-network-data-oriented high-concurrency real-time access system and method |
| US20160267157A1 (en) * | 2015-03-10 | 2016-09-15 | The Boeing Company | System and method for large scale data processing of source data |
| CN107967135A (en) * | 2017-10-31 | 2018-04-27 | 平安科技（深圳）有限公司 | Computing engines implementation method, electronic device and storage medium |
| CN109144014A (en) * | 2018-10-10 | 2019-01-04 | 北京交通大学 | The detection system and method for industrial equipment operation conditions |
| CN109862106A (en) * | 2019-03-04 | 2019-06-07 | 知轮(杭州)科技有限公司 | The processing method and system of online monitoring data |
| CN110942200A (en) * | 2019-11-28 | 2020-03-31 | 好买气电子商务有限公司 | LNG energy management method based on Internet of things block chain technology |
-
2020
- 2020-07-10 CN CN202010661062.2A patent/CN112035537B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101051334A (en) * | 2006-04-06 | 2007-10-10 | 香港理工大学 | Structure health monitoring and information managing system and its method |
| CN104123388A (en) * | 2014-08-07 | 2014-10-29 | 武汉大学 | Massive-sensing-network-data-oriented high-concurrency real-time access system and method |
| US20160267157A1 (en) * | 2015-03-10 | 2016-09-15 | The Boeing Company | System and method for large scale data processing of source data |
| CN107967135A (en) * | 2017-10-31 | 2018-04-27 | 平安科技（深圳）有限公司 | Computing engines implementation method, electronic device and storage medium |
| CN109144014A (en) * | 2018-10-10 | 2019-01-04 | 北京交通大学 | The detection system and method for industrial equipment operation conditions |
| CN109862106A (en) * | 2019-03-04 | 2019-06-07 | 知轮(杭州)科技有限公司 | The processing method and system of online monitoring data |
| CN110942200A (en) * | 2019-11-28 | 2020-03-31 | 好买气电子商务有限公司 | LNG energy management method based on Internet of things block chain technology |
Non-Patent Citations (3)
| Title |
|---|
| 向彬彬;马明星;童茂林;彭瑾;苏文秀;高秀敏;: "基于微服务架构的分布式测距***的研究与设计", 计算机应用与软件, no. 05 * |
| 柏彬;陈勇;杜长青;茅鑫同;韩超;李东鑫;黄云天;郑兴;王磊磊;: "基于物联网技术的智能安全监控建筑信息模型", 工业建筑, no. 04 * |
| 肖哥哥: "redis集成到Springmvc中及使用实例", Retrieved from the Internet <URL:https://cloud.tencent.com/developer/article/1394253> * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112035537B (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111241453B (en) | Page access duration acquisition method and device, medium and electronic equipment | |
| US20160210515A1 (en) | Event detection based on video metadata | |
| CN105659263A (en) | Sequence identification | |
| CN111212383B (en) | Method, device, server and medium for determining number of regional permanent population | |
| CN103186600B (en) | The specific analysis method and apparatus of internet public feelings | |
| CN109933984A (en) | A kind of best cluster result screening technique, device and electronic equipment | |
| CN111405030B (en) | Message pushing method and device, electronic equipment and storage medium | |
| CN106371953A (en) | Compact binary event log generation | |
| CN104462167A (en) | Data analysis support system | |
| CN111666298A (en) | Method and device for detecting user service class based on flink, and computer equipment | |
| Bocquier | Migration analysis using demographic surveys and surveillance systems | |
| CN114564930A (en) | Document information integration method, apparatus, device, medium, and program product | |
| CN113704373B (en) | User identification method, device and storage medium based on movement track data | |
| CN112347457A (en) | Abnormal account detection method and device, computer equipment and storage medium | |
| CN112035537A (en) | Method for rapidly counting behaviors of moving object monitored by multiple sensors | |
| CN115578231A (en) | Multi-dimensional analysis method and system | |
| CN111145514B (en) | Multi-dimensional early warning strategy method | |
| JP5223404B2 (en) | Organization activity analysis apparatus and program | |
| CN104376021A (en) | File recommending system and method | |
| CN113157925A (en) | Intelligent government affair analysis system based on big data | |
| CN109871215B (en) | Method and device for software release | |
| CN115965137B (en) | Specific object relevance prediction method, system, terminal and storage medium | |
| KR102557762B1 (en) | Complemented Big Data-based Event Prediction Method | |
| Stefanutti et al. | Does discrimination beat association in the IAT? The discrimination-association model reconceived | |
| CN114817377B (en) | User portrait based data risk detection method, device, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |