CN114445053A - Smart campus data processing method and system - Google Patents
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Abstract
The invention relates to the technical field of campus management, and particularly discloses a smart campus data processing method and a system, wherein the method comprises the steps of acquiring information of personnel entering corresponding areas in real time based on data identification ports of the areas, and generating a dynamic information base with detection points as indexes according to a mapping relation; updating the activity area of each person at regular time according to the dynamic information base; when the detection point acquires the information of the person containing the mark, determining an abnormal value of the person according to the activity area; and when the abnormal value reaches a preset threshold value, generating warning information. According to the invention, the information of students is acquired in real time through the preset detection points to generate the dynamic information base, the activity ranges of the students are predicted based on the dynamic information base, the students are macroscopically known according to the predicted activity ranges, the students are roughly known on the premise of ensuring the freedom of the students, and the emergency situation is conveniently processed.
Description
Technical Field
The invention relates to the technical field of campus management, in particular to a smart campus data processing method and system.
Background
In most of the existing high schools, personnel management is loose; for college students, the students are energetic, have strong curiosity and are easily tempted by various things, the most common situation is that many students like to play games in a dormitory and often escape, and most of the students have regret after graduation; however, it is not practical to manage college students in a high school mode, and therefore how to manage the staff in the college campus to a certain extent is a technical problem to be solved by the technical solution of the present invention.
Disclosure of Invention
The present invention is directed to a method and a system for processing data of a smart campus to solve the above problems.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of smart campus data processing, the method comprising:
acquiring campus construction data, and generating a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
acquiring information of personnel entering corresponding areas in real time based on the data identification ports of the areas, and generating a dynamic information base with detection points as indexes according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
updating the activity area of each person at fixed time according to the dynamic information base at each detection point;
receiving personnel information which is input by a user and contains a mark, and determining an abnormal value of the personnel according to the activity area when the personnel information containing the mark is acquired at a detection point;
and when the abnormal value reaches a preset threshold value, generating warning information.
As a further scheme of the invention: the steps of acquiring the campus construction data and generating the scene model according to the campus construction data comprise:
establishing a connection channel with a construction engineering library, reading a building model of a campus, and generating a three-dimensional scene according to the building model;
reading an engineering drawing of a campus, and determining a two-dimensional scene according to the engineering drawing; the two-dimensional scene at least comprises a two-dimensional scene of an overlooking angle;
inserting the two-dimensional scene into the three-dimensional scene to obtain a scene model;
and acquiring image information of the school at regular time, and updating the scene model in real time according to the image information of the school.
As a further scheme of the invention: the step of acquiring the image information of the school at regular time and updating the scene model in real time according to the image information of the school comprises the following steps:
acquiring image information of a school at regular time; the image information comprises sampling parameters and sampling point position information;
carrying out geometric correction processing on the image information according to the sampling parameters;
and filling the image information after the geometric correction processing into a two-dimensional scene in a scene model based on the position information of the sampling point.
As a further scheme of the invention: the step of updating the activity area of each person at regular time according to the dynamic information base at each detection point comprises the following steps:
sequentially reading dynamic information bases at all detection points, and splitting the dynamic information bases into sub-bases based on preset time nodes;
performing logic and operation on the sub-libraries within the preset time range to determine a repeated personnel list within the preset time range;
reading the repeated personnel tables corresponding to different detection points, and performing logic and operation on the repeated personnel tables corresponding to the different detection points to determine regular population;
reading time information of each person in regular people in different dynamic information bases to generate a motion track of each person;
determining an activity area of each person based on the motion trajectory.
As a further scheme of the invention: the step of reading the time information of each person in the regular crowd in different dynamic information bases and generating the motion trail of each person comprises the following steps:
sequentially reading time information of each person in regular people in different dynamic information bases, acquiring position data of the different dynamic information bases, sequencing the position data based on the time information, and generating a person position table;
calculating the movement speed of the personnel based on the position data and the time information in the personnel position table, and generating a personnel speed table which is in a mapping relation with the personnel position table;
marking abnormal data in a person position table based on the person speed table, and calculating the proportion of the abnormal data;
when the proportion is smaller than a preset proportion threshold value, generating a motion trail of the person in the scene model based on the person position table and the person speed table;
and when the proportion reaches a preset proportion threshold value, replacing the abnormal value of the person with an extreme value.
As a further scheme of the invention: the step of receiving the person information containing the mark input by the user, and determining the abnormal value of the person according to the activity area when the person information containing the mark is acquired at the detection point comprises the following steps:
receiving personnel information containing marks input by a user, and sending the personnel information containing the marks to each detection point;
when the detection point obtains the information of the person containing the mark, obtaining the activity area of the person, and judging whether the person exceeds the activity area based on the activity area; when the person exceeds the activity area, performing incremental operation on the abnormal value;
wherein the outlier is a decreasing function of time.
As a further scheme of the invention: the method further comprises the following steps:
selecting any sub-library as a reference library, and splitting the sub-library into personnel groups based on a preset group length;
inquiring the repetition times of the personnel group in other sublibraries, and comparing the repetition times with a preset time threshold;
when the repetition times reach a preset threshold value, establishing a personal intimacy relationship according to the group of people;
and constructing a personnel relationship network based on the intimacy of all personnel.
The technical scheme of the invention also provides a smart campus data processing system, which comprises:
the model generation module is used for acquiring campus construction data and generating a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
the information base generation module is used for acquiring information of personnel entering the corresponding area in real time based on the data identification port of each area and generating a dynamic information base taking the detection point as an index according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
the region determining module is used for updating the activity regions of the personnel at regular time according to the dynamic information base at the detection points;
the abnormal value calculation module is used for receiving the personnel information which is input by a user and contains the marks, and determining the abnormal value of the personnel according to the activity area when the personnel information containing the marks is obtained at the detection point;
and the warning information generation module is used for generating warning information when the abnormal value reaches a preset threshold value.
As a further scheme of the invention: the region determination module includes:
the information base splitting unit is used for sequentially reading the dynamic information bases at the detection points and splitting the dynamic information bases into sub-bases based on preset time nodes;
the personnel table determining unit is used for performing logic and operation on the sub-libraries within the preset time range and determining a repeated personnel table within the preset time range;
the regular crowd determining unit is used for reading the repeated personnel tables corresponding to different detection points, performing logic and operation on the repeated personnel tables corresponding to the different detection points and determining regular crowds;
the track generating unit is used for reading time information of each person in regular people in different dynamic information bases and generating a motion track of each person;
and the track processing unit is used for determining the activity area of each person based on the motion track.
As a further scheme of the invention: the trajectory generation unit includes:
the position table generating subunit is used for sequentially reading the time information of each person in the regular crowd in different dynamic information bases, acquiring the position data of the different dynamic information bases, sequencing the position data based on the time information and generating a person position table;
the speedometer generating subunit is used for calculating the movement speed of the personnel based on the position data and the time information in the personnel position table and generating a personnel speedometer which is in a mapping relation with the personnel position table;
a proportion calculation subunit for marking the abnormal data in the person position table based on the person speed table and calculating the proportion of the abnormal data;
a first execution subunit, configured to, when the ratio is smaller than a preset ratio threshold, generate a motion trajectory of the person in the scene model based on the person position table and the person speed table;
and the second execution subunit is used for replacing the abnormal value of the person with an extreme value when the proportion reaches a preset proportion threshold value.
Compared with the prior art, the invention has the beneficial effects that: the invention acquires the information of the students in real time through the preset detection points to generate the dynamic information base, predicts the activity range of the students based on the dynamic information base, macroscopically understands the students according to the predicted activity range, roughly understands the students on the premise of ensuring the freedom of the students and is convenient for the processing of emergency situations.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a flow chart of a smart campus data processing method.
FIG. 2 is a first sub-flow diagram of a smart campus data processing method.
FIG. 3 is a second sub-flowchart of a method for smart campus data processing.
FIG. 4 is a third sub-flowchart of a method for processing smart campus data.
FIG. 5 is a block diagram of a data processing system for a smart campus.
FIG. 6 is a block diagram of a region determination module in a smart campus data processing system.
Fig. 7 is a block diagram showing a configuration of a trajectory generation unit in the area determination module.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 is a flow chart of a smart campus data processing method, in an embodiment of the present invention, the method includes steps S100 to S500:
step S100: acquiring campus construction data, and generating a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
the campus construction data are backed up, a model can be generated according to the campus construction data, and a determined scale exists between the model and the actual environment; the benefit of setting up the model is that the user can better understand the campus environment.
Step S200: acquiring information of personnel entering corresponding areas in real time based on the data identification ports of the areas, and generating a dynamic information base with detection points as indexes according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
with the development of the internet of things technology, a plurality of methods for detecting personnel information exist, the simplest method is that a campus card of a student is identified through a card reader, and the specific scheme is that personnel information entering corresponding areas is obtained in real time on the basis of data identification ports of the areas; certainly, it is also a feasible technical solution to identify the personnel information by the face recognition technology or other identity recognition technologies;
it is worth mentioning that each area may be a teaching building, a library or a dining hall, etc., and the data recognition port is generally installed at the entrance.
Step S300: updating the activity area of each person at fixed time according to the dynamic information base at each detection point;
each detection point obtains its own dynamic information base, which contains access information of many persons, and based on the access information, the range of motion of each person, i.e. the motion area, can be determined.
Step S400: receiving personnel information which is input by a user and contains a mark, and determining an abnormal value of the personnel according to the activity area when the personnel information containing the mark is acquired at a detection point;
when a user wants to manage a certain person, the person can be marked, then detection tasks are sent to all detection points, and all the detection points can calculate abnormal values of the person based on the determined activity areas; of course, the abnormal value is only one value, and the specific treatment measures are determined by related personnel together; for example, if a student has poor self-control ability and a large number of hung departments, the student seeks help of an instructor who wants to be able to perform some external control on the student, the instructor can make a control plan and determine penalty measures, and the control plan and the penalty measures can be approved by the student; finally, the instructor marks the student, and the activity condition of the student can be acquired, so that a good help effect is achieved.
Step S500: when the abnormal value reaches a preset threshold value, warning information is generated;
step S500 is an execution step, and when the abnormal value of a certain person reaches a certain degree, the abnormal value is reported to the manager, and where the manager takes the subsequent measures, the technical scheme of the present invention is not limited.
Fig. 2 is a first sub-flow block diagram of the smart campus data processing method, where the step of acquiring campus construction data and generating a scene model according to the campus construction data includes steps S101 to S104:
step S101: establishing a connection channel with a construction engineering library, reading a building model of a campus, and generating a three-dimensional scene according to the building model;
step S102: reading an engineering drawing of a campus, and determining a two-dimensional scene according to the engineering drawing; the two-dimensional scene at least comprises a two-dimensional scene of an overlooking angle;
step S103: inserting the two-dimensional scene into the three-dimensional scene to obtain a scene model;
step S104: and acquiring image information of the school at regular time, and updating the scene model in real time according to the image information of the school.
Further, the step of acquiring the image information of the school at regular time and updating the scene model in real time according to the image information of the school includes:
acquiring image information of a school at regular time; the image information comprises sampling parameters and sampling point position information;
carrying out geometric correction processing on the image information according to the sampling parameters;
and filling the image information after geometric correction processing into a two-dimensional scene in a scene model based on the position information of the sampling point.
The generation process of the scene model is specifically limited by the content, and the scene model is obtained by adopting a 2D/3D common modeling mode. Firstly, designing an initial building model according to a school to obtain a three-dimensional scene, and then performing rendering work in the prior art; finally, reading two-dimensional engineering images, and continuously enriching the details of the three-dimensional scene by using the two-dimensional images; it is worth mentioning that the number of two-dimensional drawings is generally many, and the more the number is, the more the details are perfect. In addition, most of the three-dimensional models and two-dimensional models are existing data, and the operation required by the technical scheme of the invention is only a reading operation.
Fig. 3 is a second sub-flowchart of the smart campus data processing method, wherein the step of updating the activity area of each person periodically according to the dynamic information base at each detection point includes steps S301 to S305:
step S301: sequentially reading the dynamic information base at each detection point, and splitting the dynamic information base into sub-bases based on preset time nodes;
step S302: performing logic and operation on the sub-libraries within the preset time range to determine a repeated personnel list within the preset time range;
step S303: reading the repeated personnel tables corresponding to different detection points, and performing logic and operation on the repeated personnel tables corresponding to the different detection points to determine regular crowd;
step S304: reading time information of each person in regular people in different dynamic information bases to generate a motion track of each person;
step S305: determining an activity area of each person based on the motion trajectory.
Steps S301 to S305 provide a specific determination scheme for an active area, first, reading a dynamic information base corresponding to each detection point, and then splitting the dynamic information base into a plurality of sub-bases, where the time node is preset, such as 6 or 0 points per day; then, respectively carrying out logic and operation on different sub-libraries of the same dynamic information library within a preset time range to obtain personnel who regularly visit the detection point within a certain time range; the time range is more than one, most often one month, because for example in a teaching building, the person entering the teaching building is generally associated with a course, which is mostly months long.
For the obtained regular crowd, the movement tracks of all the members in the crowd can be obtained by counting the access data of the regular crowd at all the detection points, and an activity area can be determined according to the movement tracks, for example, a student in an east school zone only occasionally goes to a west school zone, if the student often goes to the west school zone, the student definitely goes to the west school zone regularly, and at this time, the activity area of the student is the east school zone plus the west school zone.
Fig. 4 is a third sub-flow block diagram of the smart campus data processing method, where the step of reading time information of each person in the regular group in different dynamic information bases and generating a movement trajectory of each person includes steps S3041 to S3045:
step S3041: sequentially reading time information of each person in regular people in different dynamic information bases, acquiring position data of the different dynamic information bases, sequencing the position data based on the time information, and generating a person position table;
step S3042: calculating the movement speed of the personnel based on the position data and the time information in the personnel position table, and generating a personnel speed table which is in a mapping relation with the personnel position table;
step S3043: marking abnormal data in a person position table based on the person speed table, and calculating the proportion of the abnormal data;
step S3044: when the proportion is smaller than a preset proportion threshold value, generating a motion trail of the person in the scene model based on the person position table and the person speed table;
step S3045: and when the proportion reaches a preset proportion threshold value, replacing the abnormal value of the person with an extreme value.
The generation process of the movement track is specifically limited in steps S3041 to S3045, and first, the position information of each person in the regular group is obtained, and then the position information is sorted according to the time information, so as to obtain a person position table using the person identity information as a label. Finally, a person speed table is generated according to the person position table.
The data in the person position table can be optimized based on the person speedometer, eliminating some meaningless data, e.g. if a person appears in two places in 1 minute, both position data are in doubt. When the abnormal data reaches a certain degree, assigning the abnormal value of the person as an extreme value; when the abnormal data is in a certain range, generating a motion track and marking an abnormal part.
It should be noted that when the abnormal value of the user reaches the extreme value, it must also reach the threshold preset in step S500, and when the abnormal value reaches the preset threshold, the warning information is generated.
As a preferred embodiment of the technical solution of the present invention, the step of receiving information of a person with a mark input by a user, and when the information of the person with the mark is acquired at a detection point, determining an abnormal value of the person according to the activity area includes:
receiving personnel information containing marks input by a user, and sending the personnel information containing the marks to each detection point;
when the detection point obtains the information of the person containing the mark, obtaining the activity area of the person, and judging whether the person exceeds the activity area based on the activity area; when the person exceeds the activity area, performing incremental operation on the abnormal value;
wherein the outlier is a decreasing function of time.
For a person with a mark, when an access request is detected, each detection point judges whether the position of the detection point is in an activity area of the person, if not, an increasing operation is carried out on an abnormal value, and when a plurality of access behaviors exceeding the activity area occur, the corresponding abnormal value also reaches a preset threshold value.
As a preferred embodiment of the technical solution of the present invention, the method further comprises:
selecting any sub-library as a reference library, and splitting the sub-library into personnel groups based on a preset group length;
inquiring the repetition times of the personnel group in other sublibraries, and comparing the repetition times with a preset time threshold;
when the repetition times reach a preset threshold value, establishing a personal intimacy relationship according to the group of people;
and constructing a personnel relationship network based on the intimacy of all personnel.
In one example of the technical scheme of the invention, the dynamic information base at each detection point is read in sequence, the dynamic information base is divided into sub-bases based on a preset time node, and personnel are grouped based on the sub-bases, so that a fixed personnel combination at the detection point is established; specifically, students often have their own partners, which go together in class or at meal, and are detected in tandem in the detection process; therefore, through the design of the repeated times, the personal intimacy can be established, the personal intimacy obtained by all the detection points is counted, and a personal relationship network can be established.
Example 2
Fig. 5 is a block diagram illustrating a structure of a smart campus data processing system, according to an embodiment of the present invention, the system 10 includes:
the model generation module 11 is configured to acquire campus construction data and generate a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
the information base generation module 12 is used for acquiring information of personnel entering the corresponding area in real time based on the data identification port of each area, and generating a dynamic information base with the detection point as an index according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
the region determining module 13 is configured to update the activity region of each person at regular time according to the dynamic information base at each detection point;
the abnormal value calculation module 14 is used for receiving the personnel information which is input by the user and contains the marks, and determining the abnormal value of the personnel according to the activity area when the personnel information containing the marks is acquired at the detection point;
and the warning information generating module 15 is configured to generate warning information when the abnormal value reaches a preset threshold value.
Fig. 6 is a block diagram illustrating a structure of a region determining module 13 in a smart campus data processing system, wherein the region determining module 13 includes:
an information base splitting unit 131, configured to sequentially read the dynamic information bases at each detection point, and split the dynamic information bases into sub-bases based on a preset time node;
the staff table determining unit 132 is configured to perform logical and operation on the sub-libraries within the preset time range, and determine a repeated staff table within the preset time range;
the regular crowd determining unit 133 is configured to read the repetitive staff tables corresponding to different detection points, perform logic and operation on the repetitive staff tables corresponding to the different detection points, and determine regular crowds;
the track generation unit 134 is used for reading time information of each person in regular people in different dynamic information bases and generating a motion track of each person;
a trajectory processing unit 135 for determining the activity area of each person based on the motion trajectory.
Fig. 7 is a block diagram illustrating a structure of a track generation unit 134 in the area determination module, where the track generation unit 134 includes:
a position table generating subunit 1341, configured to sequentially read time information of each person in regular people in different dynamic information bases, obtain position data of the different dynamic information bases, sort the position data based on the time information, and generate a person position table;
a speedometer generating subunit 1342, configured to calculate a person movement speed based on the position data and the time information in the person position table, and generate a person speedometer in a mapping relationship with the person position table;
a scale calculation subunit 1343 for marking the abnormal data in the person position table based on the person speed table and calculating the scale of the abnormal data;
a first executing subunit 1344, configured to, when the ratio is smaller than a preset ratio threshold, generate a motion trajectory of the person in the scene model based on the person position table and the person speed table;
a second execution subunit 1345, configured to replace the abnormal value of the person with an extreme value when the ratio reaches a preset ratio threshold.
The functions of the intelligent campus data processing method can be realized by a computer device, wherein the computer device comprises one or more processors and one or more memories, and at least one program code is stored in the one or more memories and is loaded and executed by the one or more processors to realize the functions of the intelligent campus data processing method.
The processor fetches instructions and analyzes the instructions from the memory one by one, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A smart campus data processing method, the method comprising:
acquiring campus construction data, and generating a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
acquiring information of personnel entering corresponding areas in real time based on the data identification ports of the areas, and generating a dynamic information base with detection points as indexes according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
updating the activity area of each person at fixed time according to the dynamic information base at each detection point;
receiving personnel information which is input by a user and contains a mark, and determining an abnormal value of the personnel according to the activity area when the personnel information containing the mark is acquired at a detection point;
and when the abnormal value reaches a preset threshold value, generating warning information.
2. The method of claim 1, wherein the step of obtaining campus construction data and generating a scenario model based on the campus construction data comprises:
establishing a connection channel with a construction engineering library, reading a building model of a campus, and generating a three-dimensional scene according to the building model;
reading an engineering drawing of a campus, and determining a two-dimensional scene according to the engineering drawing; the two-dimensional scene at least comprises a two-dimensional scene of an overlooking angle;
inserting the two-dimensional scene into the three-dimensional scene to obtain a scene model;
and acquiring image information of the school at regular time, and updating the scene model in real time according to the image information of the school.
3. The smart campus data processing method according to claim 2, wherein the step of periodically acquiring image information of schools and updating the scene model in real time according to the image information of schools includes:
acquiring image information of a school at regular time; the image information comprises sampling parameters and sampling point position information;
carrying out geometric correction processing on the image information according to the sampling parameters;
and filling the image information after geometric correction processing into a two-dimensional scene in a scene model based on the position information of the sampling point.
4. The method as claimed in claim 1, wherein the step of updating the activity area of each person periodically according to the dynamic database at each detection point comprises:
sequentially reading the dynamic information base at each detection point, and splitting the dynamic information base into sub-bases based on preset time nodes;
performing logic and operation on the sub-libraries within the preset time range to determine a repeated personnel list within the preset time range;
reading the repeated personnel tables corresponding to different detection points, and performing logic and operation on the repeated personnel tables corresponding to the different detection points to determine regular population;
reading time information of each person in regular people in different dynamic information bases to generate a motion track of each person;
determining an activity area of each person based on the motion trajectory.
5. The method as claimed in claim 4, wherein the step of reading time information of each person in the regular group of people in different dynamic information bases and generating the movement track of each person comprises:
sequentially reading time information of each person in regular people in different dynamic information bases, acquiring position data of the different dynamic information bases, sequencing the position data based on the time information, and generating a person position table;
calculating the movement speed of the personnel based on the position data and the time information in the personnel position table, and generating a personnel speed table which is in a mapping relation with the personnel position table;
marking abnormal data in a person position table based on the person speed table, and calculating the proportion of the abnormal data;
when the proportion is smaller than a preset proportion threshold value, generating a motion trail of the person in the scene model based on the person position table and the person speed table;
and when the proportion reaches a preset proportion threshold value, replacing the abnormal value of the person with an extreme value.
6. The method for processing wisdom campus data according to claim 4, wherein said step of receiving user input information of people who have tags, and when information of people who have tags is acquired at a detection point, determining abnormal values of the people according to the activity areas comprises:
receiving personnel information containing marks input by a user, and sending the personnel information containing the marks to each detection point;
when the detection point acquires the information of the person containing the mark, acquiring the activity area of the person, and judging whether the person exceeds the activity area or not based on the activity area; when the person exceeds the activity area, performing incremental operation on the abnormal value;
wherein the outlier is a decreasing function of time.
7. The method of claim 6, further comprising:
selecting any sub-library as a reference library, and splitting the sub-library into personnel groups based on a preset group length;
inquiring the repetition times of the personnel group in other sublibraries, and comparing the repetition times with a preset time threshold;
when the repetition times reach a preset threshold value, establishing a personal intimacy relationship according to the group of people;
and constructing a personnel relationship network based on the intimacy relationship of all personnel.
8. A wisdom campus data processing system, the system comprising:
the model generation module is used for acquiring campus construction data and generating a scene model according to the campus construction data; wherein the scene model includes a detection point; the detection point and an area containing a data identification port are in a mapping relation;
the information base generation module is used for acquiring information of personnel entering the corresponding area in real time based on the data identification port of each area and generating a dynamic information base taking the detection point as an index according to the mapping relation; the dynamic information in the dynamic information base at least comprises personnel tags and time information;
the region determining module is used for updating the activity regions of the personnel at regular time according to the dynamic information base at the detection points;
the abnormal value calculation module is used for receiving the personnel information which is input by a user and contains the marks, and determining the abnormal value of the personnel according to the activity area when the personnel information containing the marks is obtained at the detection point;
and the warning information generation module is used for generating warning information when the abnormal value reaches a preset threshold value.
9. The smart campus data processing system of claim 8, wherein the zone determination module comprises:
the information base splitting unit is used for sequentially reading the dynamic information bases at the detection points and splitting the dynamic information bases into sub-bases based on preset time nodes;
the personnel table determining unit is used for performing logic and operation on the sub-libraries within the preset time range and determining a repeated personnel table within the preset time range;
the regular crowd determining unit is used for reading the repeated personnel tables corresponding to different detection points, performing logic and operation on the repeated personnel tables corresponding to the different detection points and determining regular crowds;
the track generating unit is used for reading time information of each person in regular people in different dynamic information bases and generating a motion track of each person;
and the track processing unit is used for determining the activity area of each person based on the motion track.
10. The smart campus data processing system of claim 9, wherein the track generation unit comprises:
the position table generating subunit is used for sequentially reading the time information of each person in the regular crowd in different dynamic information bases, acquiring the position data of the different dynamic information bases, sequencing the position data based on the time information and generating a person position table;
the speedometer generating subunit is used for calculating the movement speed of the personnel based on the position data and the time information in the personnel position table and generating a personnel speedometer which is in a mapping relation with the personnel position table;
a proportion calculation subunit for marking the abnormal data in the person position table based on the person speed table and calculating the proportion of the abnormal data;
a first execution subunit, configured to, when the ratio is smaller than a preset ratio threshold, generate a motion trajectory of the person in the scene model based on the person position table and the person speed table;
and the second execution subunit is used for replacing the abnormal value of the person with an extreme value when the proportion reaches a preset proportion threshold value.
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