CN114004486A - Rail transit passenger flow scheduling system, method, storage medium and equipment - Google Patents
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Abstract
The invention provides a rail transit passenger flow dispatching system, a rail transit passenger flow dispatching method, a storage medium and rail transit passenger flow dispatching equipment, which belong to the technical field of station passenger flow control, wherein the rail transit passenger flow dispatching system comprises a face recognition module, a face data processing module and a passenger flow monitoring and dispatching module; the method is supported by big data, based on a face recognition technology, efficient automatic limit and release control is carried out, passengers are guided to be shunted to each vehicle door for taking a bus, the problem of pain points in the existing subway operation is effectively solved, and automatic passenger flow control is realized.
Description
Technical Field
The invention relates to technologies such as face recognition, face processing, passenger flow scheduling, an automatic ticket checker, a software system and the like, in particular to a rail transit passenger flow scheduling system, method, storage medium and device, which can be applied to the fields of urban rail transit and the like.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art or the prior art.
With the acceleration of national urbanization construction, rail transit is one of important infrastructures for urbanization construction, and has been rapidly developed in recent years, the mileage of urban rail transit in various regions is frequently broken through, and the passenger flow is rapidly increased.
The urban public transport traffic system is influenced by the characteristics of urban public transport travel, the rail transit has obvious high and low peak periods, the passenger flow is influenced by the population density degree and the like, and the urban public transport system has the characteristics of uneven distribution and the like. The problems of peak current limiting, queue arrangement, carriage congestion and the like which are urgently needed to be solved frequently occur in a part of extra-large and large cities. Passengers complain of the vocal cords, and have a high potential safety hazard for a long time. At present, the current flow limiting solution of urban subways is manual release of a dispatching system, and comprehensive assessment and scientific release of the number of the whole passenger flow cannot be safely and effectively carried out.
At present, the current limiting measures are taken at stations with empty crowded stations, the current limiting measures are taken in cooperation with stations with large passenger flows, particularly transfer stations with large passenger flows, the current limiting measures are taken at stations with large passenger flows, the purpose is to balance the passenger flows of all stations on lines, prevent a carriage from being fully loaded prematurely, enable passengers at subsequent stations to take buses orderly, and ensure that partial passengers at each station can get on the bus. The disadvantages of the above solution include:
1. the large data support is lacked, or the number of people in the station hall cannot be accurately known, and the number of people can be seated on the vehicle;
2. no automated current limiting device;
3. manual current limiting consumes human resources, and conflicts are easily caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a rail transit passenger flow dispatching system, a rail transit passenger flow dispatching method, a rail transit passenger flow dispatching storage medium and a rail transit passenger flow dispatching device, which can solve the problems.
The terminology that the scheme may refer to explains:
the abbreviation of AFC-Auto Fare Collection refers to the automatic ticketing system. It is a closed automatic network system with automatic ticket selling (including semi-automatic ticket selling), automatic ticket checking and automatic charging and counting, which is centrally controlled by computer. The AFC automatic ticket selling and checking system can realize the automation of rail transit ticket selling, ticket checking, charging, counting, sorting and management based on the technologies of computer, communication, network, automatic control and the like.
ATC-Automatic Train Control.
ATP-Automatic Train Protection.
ATO- -Automatic Train Operation, Train autopilot.
ATS-Automatic Train Supervision system.
AGM-Automatic Gate Machine Automatic ticket checker/Gate Machine
EnG-Entry Gate machine
ExG-Exit Gate machine
The overall scheme is as follows: in order to solve the above problem, the overall design of the present application is as follows.
A rail transit passenger flow dispatching system based on face recognition comprises a face recognition module, a face data processing module and a passenger flow monitoring and dispatching module; the face recognition module processes the getting-on face information and the getting-off face information acquired by the camera in real time through image processing and a face recognition algorithm to obtain getting-on passenger data, getting-off passenger data and door-front waiting passenger data, and sends the data to the face data processing module; the face data processing module calculates the data of passengers getting on the bus, the data of passengers getting off the bus and the data of passengers waiting in front of the door to obtain the data of the passengers getting off the bus and the data of the passengers waiting in front of the door, and sends the data to the passenger flow monitoring and scheduling module; the passenger flow monitoring and dispatching module guides the data of passengers getting off and the data of passengers waiting in front of the door into a passenger flow data model for operation, obtains the number of people who can queue up, the number of people who can pass through and the remaining time of arriving at the station of each carriage coming into the station, displays the data through a station display screen, and controls the opening and closing of a gate through an automatic fare collection system, thereby realizing the intelligent control of passenger flow.
The invention also provides a rail transit passenger flow scheduling method based on face recognition, which comprises the following steps:
s1, deriving an initial data model according to the existing data;
s2, calculating the number of passengers getting off;
s3, calculating the number of passengers getting on the bus;
s4, calculating an actual deviation and a deviation allocation method;
and S5, calculating the number of passable passengers.
Further, the initial data model derivation of step S1 includes:
s11, assuming that there are S1, S2, … Sn, N stations on a line, the number of passengers that can be loaded in a train of subways is T, and the theoretical loadable number Sx of a certain station Sx between S1 to Sn is calculated:
sx ═ T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx) … … … … … … … formula 1;
wherein, iS1 represents the number of passengers getting on the S1 station, oS1 represents the number of passengers getting off the S1 station, and so on;
s12, setting a reservation coefficient p, and reserving vacancies for subsequent stations according to the existing operation rule of the subway, so that the number of loadable people needs to calculate a proportional value as the reservation coefficient p according to the passenger flow distribution of the existing stations, namely: p1+ p2+ … + pn ═ 1;
s13, calculating the actual number Sxp of the passable people in the station x:
sxp ═ Sx ═ px ═ (T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx)) × px … … formula 2.
Further, the getting-off passenger data calculation method in step S2 is obtained by using a pre-algorithm, where the getting-off passenger data calculation pre-algorithm includes:
s21, collecting passenger face information by using a camera in a gate or a subway station, and performing primary screening;
s22, installing a camera in front of the subway shield door, acquiring passenger information passing through each shield door through the camera, transmitting the passenger information to a background for comparison, and recording the boarding station of passengers;
s23, acquiring information of the passengers getting off the bus through subway screen door cameras or exit gate cameras facing the passengers;
s24, marking whether the passenger is a frequent passenger or not;
s25, determining a get-off station of each frequent traveler according to the fixed travel route of the frequent traveler;
and S26, predicting possible passengers getting off the train when the train passes the station x and the number of the passengers allowed to pass the train at the station x.
Compared with the prior art, the invention has the beneficial effects that: the invention can support big data, carry out high-efficiency automatic limit and release control, guide passengers to shunt to each vehicle door for taking a bus, and effectively solve the problem of the existing subway operation pain point.
Drawings
FIG. 1 is a schematic diagram of a system framework of the present invention;
FIG. 2 is a system topology diagram;
FIG. 3 is a schematic diagram of a gate for separating passengers ascending and descending;
FIG. 4 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be understood that "system", "module", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
First embodiment
A rail transit passenger flow dispatching system based on face recognition is disclosed, referring to figures 1-3, and the system comprises a face recognition module, a face data processing module and a passenger flow monitoring and dispatching module.
The face recognition module processes the getting-on face information and the getting-off face information acquired by the camera in real time through image processing and a face recognition algorithm to obtain getting-on passenger data, getting-off passenger data and door-front waiting passenger data, and sends the data to the face data processing module.
The face data processing module calculates the data of passengers getting on the bus, the data of passengers getting off the bus and the data of passengers waiting in front of the door to obtain the data of the passengers getting off the bus and the data of the passengers waiting in front of the door, and sends the data to the passenger flow monitoring and scheduling module.
The passenger flow monitoring and dispatching module guides passenger data about getting off and data about waiting passengers in front of a door into a passenger flow data model for operation, obtains the number of people who can queue up, the number of people who can pass through and the remaining time of arriving at the station of each carriage about to get in, displays the data through a station display screen, controls the opening and closing of a gate through an automatic fare collection system, and realizes passenger flow intelligent control.
The camera comprises a gate camera, a station shielding door getting-on camera and a station shielding door getting-off camera.
The display terminal adopts a display screen form, and can comprise a control center, a station hall or an entrance display screen in the forms of a display, a liquid crystal display, an LED screen and the like.
And finally, dividing the gate (AMG) channels into an uplink channel and a downlink channel, referring to fig. 3, guiding the passengers to select corresponding channels according to the travel routes, counting the passengers through the rear gate and transmitting the passengers to a passenger flow monitoring and scheduling module, wherein the passenger flow monitoring and scheduling module can also transmit the number of the passengers who can pass the uplink and downlink to the gate, and when the maximum allowable number of the passengers is reached, the gate is automatically closed to prompt that the passenger flow is full, and the passengers are required to swipe cards for a later time. When the passenger flow is relieved, the gate automatically turns to normal service, and passengers are allowed to swipe cards to enter the station.
The system can be compatible with an automatic fare collection system AFC, an automatic train control system ATC and an automatic train monitoring system ATS so as to be integrated into a control center, and can also be independently arranged.
Second embodiment
A rail transit passenger flow scheduling method based on face recognition is disclosed, referring to fig. 4, and the method comprises the following steps:
s1, deriving an initial data model according to the existing data;
s2, calculating the number of passengers getting off;
s3, calculating the number of passengers getting on the bus;
s4, calculating an actual deviation and a deviation allocation method;
and S5, calculating the number of passable passengers.
Wherein the initial data model derivation of step S1 includes:
s11, assuming that there are S1, S2, … Sn, N stations on a line, the number of passengers that can be loaded in a train of subways is T, and the theoretical loadable number Sx of a certain station Sx between S1 to Sn is calculated:
sx ═ T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx) … … … … … … … formula 1;
wherein, iS1 represents the number of passengers getting on the S1 station, oS1 represents the number of passengers getting off the S1 station, and so on;
s12, setting a reservation coefficient p, and reserving vacancies for subsequent stations according to the existing operation rule of the subway, so that the number of loadable people needs to calculate a proportional value as the reservation coefficient p according to the passenger flow distribution of the existing stations, namely: p1+ p2+ … + pn ═ 1;
s13, calculating the actual number Sxp of the passable people in the station x:
sxp ═ Sx ═ px ═ (T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx)) × px … formula 2.
The process is to derive an initial data model from existing data.
In step S2, the getting-off passenger data calculation method is obtained by using a pre-algorithm or a complementary algorithm.
1) The get-off passenger data calculation pre-algorithm comprises the following steps:
s21, collecting passenger face information by using a camera in a gate or a subway station, and performing primary screening;
s22, installing a camera in front of the subway shield door, acquiring passenger information passing through each shield door through the camera, transmitting the passenger information to a background for comparison, and recording the boarding station of passengers;
s23, acquiring information of the passengers getting off the bus through subway screen door cameras or exit gate cameras facing the passengers;
s24, marking whether the passenger is a frequent passenger or not;
example (c): when the boarding station and the alighting station of the passenger A are consistent within a certain time t and reach a certain number of times n, the passenger A is marked as a frequent passenger (the values of t and n are determined according to the calculation precision requirement, when the value of t is constant, the larger the value of n is, the more accurate the calculated number O1 of people to get off is).
S25, determining a get-off station of each frequent traveler according to the fixed travel route of the frequent traveler;
since the passenger flow in the peak period is relatively stable, for example, the travel route of the frequent traveler A is relatively fixed, the getting-off station of each frequent traveler can be obtained by analogy.
And S26, predicting possible passengers getting off the train when the train passes the station x and the number of the passengers allowed to pass the train at the station x.
Therefore, after the system runs for a period of time, the station at which the passenger A gets off can be obtained, so that the forecast is made, when the train passes through the station x, the passenger on the train can get off, and the number of persons that the train can pass at the station x can be forecasted.
2) The get-off passenger data calculation pre-algorithm, namely the passenger data deviation in the step S4 is calculated by using a complementary algorithm, and specifically includes:
s41, calculating the actual deviation: calculating the number of passengers getting off from the shielded gate through a camera facing the direction of the passengers getting out of the station, and subtracting the estimated number of passengers to obtain a difference value;
s42, deviation blending: the difference is fed as a balance coefficient into the next station or the next station's release calculation.
The acquisition of the reserved coefficient p comprises budgeting and calculating:
1) predicting a reserved coefficient: obtaining a reserved coefficient p according to subway historical data:
p ═ x/y … … … … … … … … … … … … … … … … … … … … … … … … formula 3;
in the formula, y is the total passenger flow of the line in a certain time period, and x is the number of passenger flows of n stations of the subway and x;
2) and (3) calculating a reserved coefficient: the method comprises the steps of calculating real-time passenger flow y of a wire net by using a camera at a station entrance, calculating the time of entering a station and the number of waiting passengers from the station to obtain the number ratio of the passengers of a certain bus in the future, calculating the number of released passengers according to the number ratio, and updating in real time.
Third embodiment
A computer storage medium having computer instructions stored thereon, characterized in that: the computer instructions when executed perform the foregoing method. For details, the method is described in the foregoing section, and is not repeated here.
It will be appreciated by those of ordinary skill in the art that all or a portion of the steps of the various methods of the embodiments described above may be performed by associated hardware as instructed by a program that may be stored on a computer readable storage medium, which may include non-transitory and non-transitory, removable and non-removable media, to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visualbasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Fourth embodiment
The invention also provides a device comprising a memory and a processor, the memory having stored thereon computer instructions capable of being executed on the processor, the processor executing the computer instructions to perform the aforementioned method. For details, the method is described in the foregoing section, and is not repeated here.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A rail transit passenger flow dispatching system based on face recognition is characterized by comprising a face recognition module, a face data processing module and a passenger flow monitoring and dispatching module;
the face recognition module processes the getting-on face information and the getting-off face information acquired by the camera in real time through image processing and a face recognition algorithm to obtain getting-on passenger data, getting-off passenger data and door-front waiting passenger data, and sends the data to the face data processing module;
the face data processing module calculates the data of passengers getting on the bus, the data of passengers getting off the bus and the data of passengers waiting in front of the door to obtain the data of the passengers getting off the bus and the data of the passengers waiting in front of the door, and sends the data to the passenger flow monitoring and scheduling module;
the passenger flow monitoring and dispatching module guides the data of passengers getting off and the data of passengers waiting in front of the door into a passenger flow data model for operation, obtains the number of people who can queue up, the number of people who can pass through and the remaining time of arriving at the station of each carriage coming into the station, displays the data through a station display screen, and controls the opening and closing of a gate through an automatic fare collection system, thereby realizing the intelligent control of passenger flow.
2. The rail transit passenger flow dispatching system of claim 1, wherein: the camera comprises a gate camera, a station shielding door getting-on camera and a station shielding door getting-off camera.
3. A rail transit passenger flow scheduling method based on face recognition is characterized by comprising the following steps:
s1, deriving an initial data model according to the existing data;
s2, calculating the number of passengers getting off;
s3, calculating the number of passengers getting on the bus;
s4, calculating an actual deviation and a deviation allocation method;
and S5, calculating the number of passable passengers.
4. The method of claim 3, wherein the initial data model derivation of step S1 comprises:
s11, assuming that there are S1, S2, … Sn, N stations on a line, the number of passengers that can be loaded in a train of subways is T, and the theoretical loadable number Sx of a certain station Sx between S1 to Sn is calculated:
sx ═ T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx) … … … … … formula 1;
wherein, iS1 represents the number of passengers getting on the S1 station, oS1 represents the number of passengers getting off the S1 station, and so on;
s12, setting a reservation coefficient p, and reserving vacancies for subsequent stations according to the existing operation rule of the subway, so that the number of loadable people needs to calculate a proportional value as the reservation coefficient p according to the passenger flow distribution of the existing stations, namely: p1+ p2+ … + pn ═ 1;
s13, calculating the actual number Sxp of the passable people in the station x:
sxp ═ Sx ═ px ═ (T- (iS1+ iS2+ … + iSx-1) + (oS1+ oS2+ … + oSx)) × px … formula 2.
5. The method as claimed in claim 3, wherein the alighting passenger data calculation method in step S2 is obtained using a pre-algorithm, the alighting passenger data calculation pre-algorithm including:
s21, collecting passenger face information by using a camera in a gate or a subway station, and performing primary screening;
s22, installing a camera in front of the subway shield door, acquiring passenger information passing through each shield door through the camera, transmitting the passenger information to a background for comparison, and recording the boarding station of passengers;
s23, acquiring information of the passengers getting off the bus through subway screen door cameras or exit gate cameras facing the passengers;
s24, marking whether the passenger is a frequent passenger or not;
s25, determining a get-off station of each frequent traveler according to the fixed travel route of the frequent traveler;
and S26, predicting possible passengers getting off the train when the train passes the station x and the number of the passengers allowed to pass the train at the station x.
6. The method according to claim 3, wherein the passenger data deviation of step S4 is calculated by a complementary algorithm, specifically comprising:
s41, calculating the actual deviation: calculating the number of passengers getting off from the shielded gate through a camera facing the direction of the passengers getting out of the station, and subtracting the estimated number of passengers to obtain a difference value;
s42, deviation blending: the difference is fed as a balance coefficient into the next station or the next station's release calculation.
7. The method according to claim 4, wherein the obtaining of the reservation factor p comprises both budgeting and calculating:
predicting a reserved coefficient: obtaining a reserved coefficient p according to subway historical data:
p ═ x/y … … … … … … … … … … … … … … … … … … … … … … … … formula 3;
in the formula, y is the total passenger flow of the line in a certain time period, and x is the number of passenger flows of n stations entering the subway;
and (3) calculating a reserved coefficient: the method comprises the steps of calculating real-time passenger flow y of a wire net by using a camera at a station entrance, calculating the time of entering a station and the number of waiting passengers from the station to obtain the number ratio of the passengers of a certain bus in the future, calculating the number of released passengers according to the number ratio, and updating in real time.
8. A computer storage medium having computer instructions stored thereon, characterized in that: the computer instructions when executed perform the method of any of claims 3-7.
9. An apparatus comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, the apparatus comprising: the processor, when executing the computer instructions, performs the method of any of claims 3-7.
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Cited By (2)
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|---|---|---|---|---|
| CN116430787A (en) * | 2023-06-13 | 2023-07-14 | 通号工程局集团北京研究设计实验中心有限公司 | Rail transit comprehensive monitoring system and method |
| CN117896505A (en) * | 2024-03-11 | 2024-04-16 | 常州大数据有限公司 | Security monitoring management system based on big data Internet of things |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107578241A (en) * | 2016-07-05 | 2018-01-12 | 上海掌腾智能科技有限公司 | A kind of subway payment system based on regular guest's information |
| CN110647855A (en) * | 2019-09-29 | 2020-01-03 | 常州大学 | Subway station passenger flow statistical method based on face recognition |
| CN111695786A (en) * | 2020-05-22 | 2020-09-22 | 广州地铁设计研究院股份有限公司 | Passenger portrait system suitable for urban rail transit and construction method thereof |
| CN112100230A (en) * | 2020-07-02 | 2020-12-18 | 深圳市时耕科技有限公司 | Passenger flow statistical analysis method |
| CN113276913A (en) * | 2021-05-25 | 2021-08-20 | 五邑大学 | Method and system for dynamically balancing passenger flow of subway carriage |
-
2021
- 2021-10-29 CN CN202111271274.0A patent/CN114004486A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107578241A (en) * | 2016-07-05 | 2018-01-12 | 上海掌腾智能科技有限公司 | A kind of subway payment system based on regular guest's information |
| CN110647855A (en) * | 2019-09-29 | 2020-01-03 | 常州大学 | Subway station passenger flow statistical method based on face recognition |
| CN111695786A (en) * | 2020-05-22 | 2020-09-22 | 广州地铁设计研究院股份有限公司 | Passenger portrait system suitable for urban rail transit and construction method thereof |
| CN112100230A (en) * | 2020-07-02 | 2020-12-18 | 深圳市时耕科技有限公司 | Passenger flow statistical analysis method |
| CN113276913A (en) * | 2021-05-25 | 2021-08-20 | 五邑大学 | Method and system for dynamically balancing passenger flow of subway carriage |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116430787A (en) * | 2023-06-13 | 2023-07-14 | 通号工程局集团北京研究设计实验中心有限公司 | Rail transit comprehensive monitoring system and method |
| CN116430787B (en) * | 2023-06-13 | 2023-09-01 | 通号工程局集团北京研究设计实验中心有限公司 | Rail transit comprehensive monitoring system and method |
| CN117896505A (en) * | 2024-03-11 | 2024-04-16 | 常州大数据有限公司 | Security monitoring management system based on big data Internet of things |
| CN117896505B (en) * | 2024-03-11 | 2024-05-07 | 常州大数据有限公司 | Security monitoring management system based on big data Internet of things |
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