Remote intelligent control method and system for barrier gate machine and medium

Abstract

The invention relates to a remote intelligent control method and system of a barrier gate machine and a medium, and belongs to the technical field of barrier gate machine control. Face recognition image data information is acquired, so that estimated network occupation information of the face recognition image data information during information transmission is acquired; a first control scheme or a second control scheme is generated according to estimated network occupation information of the face recognition image data information during information transmission and real-time communication network data information of the barrier gate, and finally remote intelligent control is performed based on the first control scheme or the second control scheme. According to the invention, the image acquisition quality of the barrier gate machine face recognition system during data acquisition is controlled according to the real-time communication data information of the barrier gate machine, so that the barrier gate machine can be dynamically adjusted according to the network environment, the control of the barrier gate machine is more reasonable, and the control delay of the barrier gate machine when the network is poor is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of barrier gate control, and in particular to a remote intelligent control method, system and medium for a barrier gate. Background Art

[0002] The barrier machine is a means of protecting pedestrians and other vehicles when a highway crosses other tracks. The barrier machine is a channel blocking device (channel management equipment) used to manage the flow of people and regulate the entry and exit of pedestrians. Its most basic and core function is to achieve only one person passing through at a time, and it can be used for entrances and exits in various occasions. Existing barrier machines are of various types, such as offline control type and online control type. The offline control type stores data in the data storage module. When the data storage capacity of the data storage module of this type of barrier machine is large, it is not conducive to management, which will lead to slow recognition speed of the barrier machine; while the online control type transmits the facial data to the server and service platform through the mobile terminal and computer terminal, which is conducive to the management of facial data. However, this type of barrier machine also has certain limitations. For example, when the communication quality is poor, the barrier machine cannot recognize the face normally, and thus cannot recognize whether the user has the authority. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a remote intelligent control method, system and medium for a barrier gate.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides a remote intelligent control method for a barrier gate, comprising the following steps: Collecting real-time communication network data information of each gate machine, and obtaining communication analysis results by analyzing the real-time communication network data information of the gate machine; Recommending the image acquisition quality of the face recognition system when performing data acquisition according to the communication analysis result, and performing data acquisition according to the image acquisition quality of the face recognition system when performing data acquisition to obtain face recognition image data information; Obtaining estimated network occupancy information of face recognition image data information when information is transmitted, and generating a first control scheme or a second control scheme according to the estimated network occupancy information of the face recognition image data information when information is transmitted and the real-time communication network data information of the gate machine; Remote intelligent control is performed based on the first control scheme or the second control scheme.

[0005] Furthermore, in the remote intelligent control method of the barrier gate machine, the real-time communication network data information of each barrier gate machine is collected, and the communication analysis result is obtained by analyzing the real-time communication network data information of the barrier gate machine, which specifically includes: Collecting the real-time communication network data information of each gate machine, and setting the communication network data information threshold, and judging whether the real-time communication network data information of the gate machine is greater than the communication network data information threshold; If the real-time communication network data information of the barrier gate machine is greater than the communication network data information threshold, the corresponding barrier gate machine is regarded as a barrier gate machine for normal information transmission, and a communication analysis result is generated according to the barrier gate machine for normal information transmission; If the real-time communication network data information of the barrier gate is not greater than the communication network data information threshold, the corresponding barrier gate is regarded as a barrier gate that performs abnormal information transmission, and a communication analysis result is generated based on the barrier gate that performs abnormal information transmission.

[0006] Furthermore, in the remote intelligent control method of the barrier gate machine, the image acquisition quality of the face recognition system during data acquisition is recommended according to the communication analysis result, specifically including: If the communication analysis result is that the barrier gate is not transmitting information normally, the image acquisition quality plan of the face recognition system in the barrier gate when collecting data is obtained, and the real-time communication data information of the communication analysis result is obtained; Initialize the image acquisition quality scheme of the face recognition system in the barrier gate machine in the target area when performing data acquisition according to the real-time communication data information of the communication analysis result, and obtain the image file size data corresponding to the image quality data in the current image acquisition quality scheme; When the image file size data corresponding to the image quality data in the current image acquisition quality solution is greater than the real-time communication data information of the communication analysis result, the image acquisition quality of the face recognition system during data acquisition is re-planned; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is not greater than the real-time communication data information of the communication analysis result, output the image acquisition quality scheme of the face recognition system in the gate machine when performing data acquisition; If the image file size data corresponding to the image quality data in all image acquisition quality schemes are greater than the real-time communication data information of the communication analysis results, the lowest image acquisition quality scheme is selected as the image acquisition quality scheme for the face recognition system in the gate machine when performing data acquisition.

[0007] Furthermore, in the remote intelligent control method of the barrier gate machine, obtaining the estimated network occupancy information of the face recognition image data information during information transmission specifically includes: Obtaining estimated network occupancy information of each face recognition image size data when information is transmitted, and constructing a knowledge graph, and inputting the estimated network occupancy information of each face recognition image size data when information is transmitted into the knowledge graph for storage; Obtaining file size data of current face recognition image data information, and inputting the file size data of the current face recognition image data information into the knowledge graph for data matching; Through data matching, the estimated network occupancy information of the face recognition image data information when the information is transmitted is obtained, and the estimated network occupancy information of the face recognition image data information when the information is transmitted is output.

[0008] Furthermore, in the remote intelligent control method of the barrier gate, the first control scheme or the second control scheme is generated according to the estimated network occupancy information of the face recognition image data information during information transmission and the real-time communication network data information of the barrier gate, specifically including: Determine whether the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine; When the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine, a first control scheme is generated; When the estimated network occupancy information of the facial recognition image data information during information transmission is not greater than the real-time communication network data information of the barrier gate, a second control scheme is generated.

[0009] Furthermore, in the remote intelligent control method of the barrier gate machine, remote intelligent control is performed based on the first control scheme or the second control scheme, specifically including: If the control scheme is the first control scheme, segmenting the face recognition image data information, extracting the characteristic parts of the face, obtaining the segmented face recognition image data information, and transmitting the segmented face recognition image data information for recognition; If the control scheme is the second control scheme, the face recognition image data information is transmitted for recognition.

[0010] A second aspect of the present invention provides a remote intelligent control system for a barrier gate machine, comprising a memory and a processor, wherein the memory comprises a remote intelligent control method program for the barrier gate machine, and when the remote intelligent control method program for the barrier gate machine is executed by the processor, the steps of any one of the remote intelligent control methods for the barrier gate machine are implemented.

[0011] A third aspect of the present invention provides a computer-readable storage medium, which includes a remote intelligent control method program for a barrier gate machine. When the remote intelligent control method program for a barrier gate machine is executed by a processor, any one of the remote intelligent control methods for a barrier gate machine is implemented.

[0012] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention collects the real-time communication network data information of each barrier gate, analyzes the real-time communication network data information of the barrier gate, obtains the communication analysis result, and then recommends the image acquisition quality of the face recognition system when performing data acquisition according to the communication analysis result, and performs data acquisition according to the image acquisition quality of the face recognition system when performing data acquisition, obtains the face recognition image data information, thereby obtaining the estimated network occupancy information of the face recognition image data information when performing information transmission, and generates the first control scheme or the second control scheme according to the estimated network occupancy information of the face recognition image data information when performing information transmission and the real-time communication network data information of the barrier gate, and finally performs remote intelligent control based on the first control scheme or the second control scheme. The present invention controls the image acquisition quality of the barrier gate face recognition system when performing data acquisition according to the real-time communication data information of the barrier gate, so that the barrier gate can be dynamically adjusted according to the network environment, so that the control of the barrier gate is more reasonable, and the control delay of the barrier gate when the network is poor is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The overall flow chart of the remote intelligent control method of the gate machine is shown; Figure 2 A partial flow chart of a remote intelligent control method for a barrier gate machine is shown; Figure 3 The system block diagram of the remote intelligent control system of the gate machine is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] like Figure 1 As shown, the first aspect of the present invention provides a remote intelligent control method for a barrier gate machine, comprising the following steps: S102: collecting real-time communication network data information of each gate machine, and obtaining communication analysis results by analyzing the real-time communication network data information of the gate machine; S104: recommending the image acquisition quality of the face recognition system when performing data acquisition according to the communication analysis result, and performing data acquisition according to the image acquisition quality of the face recognition system when performing data acquisition to obtain face recognition image data information; S106: Obtaining estimated network occupancy information when the face recognition image data information is being transmitted, and generating a first control scheme or a second control scheme according to the estimated network occupancy information when the face recognition image data information is being transmitted and the real-time communication network data information of the gate machine; S108: Perform remote intelligent control based on the first control scheme or the second control scheme.

[0018] It should be noted that the present invention controls the image acquisition quality of the barrier machine's face recognition system during data acquisition based on the barrier machine's real-time communication data information, thereby enabling the barrier machine to dynamically adjust according to the network environment, making the barrier machine's control more reasonable and reducing the barrier machine's control delay when the network is poor.

[0019] Furthermore, in the remote intelligent control method of the barrier gate machine, the real-time communication network data information of each barrier gate machine is collected, and the communication analysis results are obtained by analyzing the real-time communication network data information of the barrier gate machine, which specifically includes: Collect the real-time communication network data information of each gate machine, set the communication network data information threshold, and determine whether the real-time communication network data information of the gate machine is greater than the communication network data information threshold; If the real-time communication network data information of the gate machine is greater than the communication network data information threshold, the corresponding gate machine is regarded as a gate machine for normal information transmission, and the communication analysis result is generated according to the gate machine for normal information transmission; If the real-time communication network data information of the barrier gate machine is not greater than the communication network data information threshold, the corresponding barrier gate machine will be regarded as a barrier gate machine that is abnormally transmitting information, and a communication analysis result will be generated based on the barrier gate machine that is abnormally transmitting information.

[0020] It should be noted that the real-time communication network data information of the gate machine includes real-time information transmission rate, bit error rate, information upload rate, information download rate and other data.

[0021] Furthermore, in the remote intelligent control method of the barrier gate machine, the image acquisition quality of the face recognition system during data acquisition is recommended according to the communication analysis result, specifically including: If the communication analysis result shows that the barrier gate is not transmitting information normally, the image acquisition quality plan of the face recognition system in the barrier gate during data acquisition is obtained, and the real-time communication data information of the communication analysis result is obtained; Initialize the image acquisition quality scheme of the face recognition system in the barrier gate machine in the target area when performing data acquisition according to the real-time communication data information of the communication analysis result, and obtain the image file size data corresponding to the image quality data in the current image acquisition quality scheme; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is greater than the real-time communication data information of the communication analysis result, the image acquisition quality of the face recognition system during data acquisition is replanned; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is not greater than the real-time communication data information of the communication analysis result, the image acquisition quality scheme of the face recognition system in the gate machine when performing data acquisition is output; If the image file size data corresponding to the image quality data in all image acquisition quality schemes are greater than the real-time communication data information of the communication analysis results, the lowest image acquisition quality scheme is selected as the image acquisition quality scheme for the face recognition system in the gate machine when performing data acquisition.

[0022] It should be noted that the face recognition system has a variety of image acquisition quality options during data acquisition, such as high quality, ultra-high quality, medium quality, low quality, etc. When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is greater than the real-time communication data information of the communication analysis result, the image acquisition quality of the face recognition system during data acquisition is re-planned. When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is not greater than the real-time communication data information of the communication analysis result, the image acquisition quality scheme of the face recognition system in the gate machine during data acquisition is output. If the image file size data corresponding to the image quality data in all image acquisition quality schemes is greater than the real-time communication data information of the communication analysis result, the lowest image acquisition quality scheme is selected as the image acquisition quality scheme of the face recognition system in the gate machine during data acquisition. This method can improve the control rationality of the gate machine and recommend a more reasonable image acquisition scheme. Network occupancy data includes real-time information transmission rate, bit error rate, information upload rate, information download rate and other data.

[0023] Furthermore, in the remote intelligent control method of the barrier gate machine, obtaining the estimated network occupancy information of the face recognition image data information during information transmission specifically includes: Obtain estimated network occupancy information of each face recognition image size data when transmitting information, construct a knowledge graph, and input the estimated network occupancy information of each face recognition image size data when transmitting information into the knowledge graph for storage; Obtain the file size data of the current face recognition image data information, and input the file size data of the current face recognition image data information into the knowledge graph for data matching; Through data matching, the estimated network occupancy information of the face recognition image data information when transmitting information is obtained, and the estimated network occupancy information of the face recognition image data information when transmitting information is output.

[0024] like Figure 2 As shown, further, in the remote intelligent control method of the barrier gate machine, the first control scheme or the second control scheme is generated according to the estimated network occupancy information of the face recognition image data information during information transmission and the real-time communication network data information of the barrier gate machine, specifically including: S202: Determine whether the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine; S204: When the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine, a first control scheme is generated; S206: When the estimated network occupancy information of the facial recognition image data during information transmission is not greater than the real-time communication network data information of the gate machine, a second control scheme is generated.

[0025] Furthermore, in the remote intelligent control method of the barrier gate machine, remote intelligent control is performed based on the first control scheme or the second control scheme, specifically including: If the control scheme is the first control scheme, segment the face recognition image data information, extract the characteristic part of the face, obtain the segmented face recognition image data information, and transmit the segmented face recognition image data information for recognition; If the control scheme is the second control scheme, the face recognition image data information is transmitted for recognition.

[0026] In addition, the method further comprises: The communication quality data information of the gate machine under various meteorological characteristic environments is obtained through big data, and a gate machine communication quality data prediction model is built based on a deep neural network, and a graph neural network is introduced; Input the communication quality data information of the barrier gate machine under each meteorological characteristic environment into the graph neural network, take the meteorological characteristic as the first node, and the communication quality plastic information of the barrier gate machine as the second node, to construct a directed description relationship; Connecting the first node and the second node based on the directed description relationship to construct a topological structure graph, obtaining a related adjacency matrix based on the topological structure graph, and inputting the adjacency matrix into the gate machine communication quality data prediction model for training; Acquire meteorological characteristic data of the area where the barrier gate is located within a preset time, input the meteorological characteristic data of the area where the barrier gate is located within the preset time into the communication quality data prediction model of the barrier gate for prediction, and acquire the communication quality data of the barrier gate within the preset time; When the communication quality data of the gate machine is lower than the preset communication quality data threshold within the preset time, the gate machine in the corresponding area is warned, and the warning is released to the remote terminal in the corresponding area in a preset manner; At the same time, the communication data information occupied by each channel is obtained, and the number of channels that can be used normally is calculated based on the communication quality data of the gate machine within the preset time and the communication data information occupied by each channel, and displayed in a preset manner.

[0027] It should be noted that this method can predict the communication quality data of the barrier gate according to meteorological characteristics, so as to give early warning to the barrier gate in the corresponding area, and can predict the control situation of the barrier gate under extreme weather in advance and give early warning, so as to shut down the automatic control function of the barrier gate in specific scenarios, so that the barrier gate can be used normally and remind the staff to activate the emergency plan. Moreover, when the data collection quality is the same, the network data occupied by the face recognition system in each channel of the barrier gate is the same, and this method can also recommend the communication data information occupied by each channel to improve the control rationality of the barrier gate.

[0028] like Figure 3 As shown, the second aspect of the present invention provides a remote intelligent control system 4 for a barrier gate, comprising a memory 41 and a processor 42. The memory 41 includes a remote intelligent control method program for the barrier gate. When the remote intelligent control method program for the barrier gate is executed by the processor 42, the following steps are implemented: Collect the real-time communication network data information of each gate machine, and obtain the communication analysis results by analyzing the real-time communication network data information of the gate machine; Recommending the image acquisition quality of the face recognition system when performing data acquisition according to the communication analysis result, and performing data acquisition according to the image acquisition quality of the face recognition system when performing data acquisition to obtain face recognition image data information; Obtaining estimated network occupancy information of face recognition image data information when information is transmitted, and generating a first control scheme or a second control scheme according to the estimated network occupancy information of face recognition image data information when information is transmitted and real-time communication network data information of the gate machine; Remote intelligent control is performed based on the first control scheme or the second control scheme.

[0029] Furthermore, in the remote intelligent control system of the barrier gate machine, the real-time communication network data information of each barrier gate machine is collected, and the communication analysis results are obtained by analyzing the real-time communication network data information of the barrier gate machine, including: Collect the real-time communication network data information of each gate machine, set the communication network data information threshold, and determine whether the real-time communication network data information of the gate machine is greater than the communication network data information threshold; If the real-time communication network data information of the gate machine is greater than the communication network data information threshold, the corresponding gate machine is regarded as a gate machine for normal information transmission, and the communication analysis result is generated according to the gate machine for normal information transmission; If the real-time communication network data information of the barrier gate machine is not greater than the communication network data information threshold, the corresponding barrier gate machine will be regarded as a barrier gate machine that is abnormally transmitting information, and a communication analysis result will be generated based on the barrier gate machine that is abnormally transmitting information.

[0030] Furthermore, in the remote intelligent control system of the barrier gate, the image acquisition quality of the face recognition system during data acquisition is recommended based on the communication analysis results, including: If the communication analysis result shows that the barrier gate is not transmitting information normally, the image acquisition quality plan of the face recognition system in the barrier gate during data acquisition is obtained, and the real-time communication data information of the communication analysis result is obtained; Initialize the image acquisition quality scheme of the face recognition system in the barrier gate machine in the target area when performing data acquisition according to the real-time communication data information of the communication analysis result, and obtain the image file size data corresponding to the image quality data in the current image acquisition quality scheme; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is greater than the real-time communication data information of the communication analysis result, the image acquisition quality of the face recognition system during data acquisition is replanned; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is not greater than the real-time communication data information of the communication analysis result, the image acquisition quality scheme of the face recognition system in the gate machine when performing data acquisition is output; If the image file size data corresponding to the image quality data in all image acquisition quality schemes are greater than the real-time communication data information of the communication analysis results, the lowest image acquisition quality scheme is selected as the image acquisition quality scheme for the face recognition system in the gate machine when performing data acquisition.

[0031] Furthermore, in the remote intelligent control system of the barrier gate machine, the estimated network occupancy information of the face recognition image data information during information transmission is obtained, specifically including: Obtain estimated network occupancy information of each face recognition image size data when transmitting information, construct a knowledge graph, and input the estimated network occupancy information of each face recognition image size data when transmitting information into the knowledge graph for storage; Obtain the file size data of the current face recognition image data information, and input the file size data of the current face recognition image data information into the knowledge graph for data matching; Through data matching, the estimated network occupancy information of the face recognition image data information when transmitting information is obtained, and the estimated network occupancy information of the face recognition image data information when transmitting information is output.

[0032] Furthermore, in the remote intelligent control system of the barrier gate, the first control scheme or the second control scheme is generated according to the estimated network occupancy information of the face recognition image data information during information transmission and the real-time communication network data information of the barrier gate, specifically including: Determine whether the estimated network occupancy information of the face recognition image data during information transmission is greater than the real-time communication network data information of the gate machine; When the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine, a first control scheme is generated; When the estimated network occupancy information of the facial recognition image data during information transmission is not greater than the real-time communication network data information of the gate machine, a second control scheme is generated.

[0033] Furthermore, in the remote intelligent control system of the barrier gate machine, remote intelligent control is performed based on the first control scheme or the second control scheme, specifically including: If the control scheme is the first control scheme, segment the face recognition image data information, extract the characteristic part of the face, obtain the segmented face recognition image data information, and transmit the segmented face recognition image data information for recognition; If the control scheme is the second control scheme, the face recognition image data information is transmitted for recognition.

[0034] A third aspect of the present invention provides a computer-readable storage medium, which includes a remote intelligent control method program for a barrier gate machine. When the remote intelligent control method program for a barrier gate machine is executed by a processor, any one of the remote intelligent control methods for a barrier gate machine is implemented.

[0035] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0036] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0037] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0038] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0039] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0040] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A remote intelligent control method for a barrier gate, characterized in that: The following steps are involved: Collecting real-time communication network data information of each gate machine, and obtaining communication analysis results by analyzing the real-time communication network data information of the gate machine; Recommending the image acquisition quality of the face recognition system when performing data acquisition according to the communication analysis result, and performing data acquisition according to the image acquisition quality of the face recognition system when performing data acquisition to obtain face recognition image data information; Obtaining estimated network occupancy information of face recognition image data information when information is transmitted, and generating a first control scheme or a second control scheme according to the estimated network occupancy information of the face recognition image data information when information is transmitted and the real-time communication network data information of the gate machine; Remote intelligent control is performed based on the first control scheme or the second control scheme.

2. A remote intelligent control method for a barrier gate according to claim 1, characterized in that: Collect the real-time communication network data information of each gate machine, and obtain the communication analysis results by analyzing the real-time communication network data information of the gate machine, which specifically includes: Collecting the real-time communication network data information of each gate machine, and setting the communication network data information threshold, and judging whether the real-time communication network data information of the gate machine is greater than the communication network data information threshold; If the real-time communication network data information of the barrier gate machine is greater than the communication network data information threshold, the corresponding barrier gate machine is regarded as a barrier gate machine for normal information transmission, and a communication analysis result is generated according to the barrier gate machine for normal information transmission; If the real-time communication network data information of the barrier gate is not greater than the communication network data information threshold, the corresponding barrier gate is regarded as a barrier gate that performs abnormal information transmission, and a communication analysis result is generated based on the barrier gate that performs abnormal information transmission.

3. The remote intelligent control method of a barrier gate according to claim 1, characterized in that: The image acquisition quality of the face recognition system during data acquisition is recommended according to the communication analysis result, specifically including: If the communication analysis result is that the barrier gate is not transmitting information normally, the image acquisition quality plan of the face recognition system in the barrier gate when collecting data is obtained, and the real-time communication data information of the communication analysis result is obtained; Initialize the image acquisition quality scheme of the face recognition system in the barrier gate machine in the target area when performing data acquisition according to the real-time communication data information of the communication analysis result, and obtain the image file size data corresponding to the image quality data in the current image acquisition quality scheme; When the image file size data corresponding to the image quality data in the current image acquisition quality solution is greater than the real-time communication data information of the communication analysis result, the image acquisition quality of the face recognition system during data acquisition is re-planned; When the image file size data corresponding to the image quality data in the current image acquisition quality scheme is not greater than the real-time communication data information of the communication analysis result, output the image acquisition quality scheme of the face recognition system in the gate machine when performing data acquisition; If the image file size data corresponding to the image quality data in all image acquisition quality schemes are greater than the real-time communication data information of the communication analysis results, the lowest image acquisition quality scheme is selected as the image acquisition quality scheme for the face recognition system in the gate machine when performing data acquisition.

4. The remote intelligent control method of a barrier gate according to claim 1, characterized in that: Obtain the estimated network occupancy information of face recognition image data information during information transmission, including: Obtaining estimated network occupancy information of each face recognition image size data when information is transmitted, and constructing a knowledge graph, and inputting the estimated network occupancy information of each face recognition image size data when information is transmitted into the knowledge graph for storage; Obtaining file size data of current face recognition image data information, and inputting the file size data of the current face recognition image data information into the knowledge graph for data matching; Through data matching, the estimated network occupancy information of the face recognition image data information when the information is transmitted is obtained, and the estimated network occupancy information of the face recognition image data information when the information is transmitted is output.

5. The remote intelligent control method of a barrier gate according to claim 1, characterized in that: Generate a first control scheme or a second control scheme according to the estimated network occupancy information of the face recognition image data information during information transmission and the real-time communication network data information of the gate machine, specifically including: Determine whether the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine; When the estimated network occupancy information of the face recognition image data information during information transmission is greater than the real-time communication network data information of the gate machine, a first control scheme is generated; When the estimated network occupancy information of the facial recognition image data information during information transmission is not greater than the real-time communication network data information of the barrier gate, a second control scheme is generated.

6. The remote intelligent control method of a barrier gate according to claim 1, characterized in that: The remote intelligent control based on the first control scheme or the second control scheme specifically includes: If the control scheme is the first control scheme, segmenting the face recognition image data information, extracting the characteristic parts of the face, obtaining the segmented face recognition image data information, and transmitting the segmented face recognition image data information for recognition; If the control scheme is the second control scheme, the face recognition image data information is transmitted for recognition.

7. A remote intelligent control system for a barrier gate, characterized in that: It includes a memory and a processor, wherein the memory includes a remote intelligent control method program for a barrier gate machine, and when the remote intelligent control method program for a barrier gate machine is executed by the processor, the steps of the remote intelligent control method for a barrier gate machine as described in any one of claims 1-6 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a remote intelligent control method program for a barrier gate machine. When the remote intelligent control method program for a barrier gate machine is executed by a processor, the steps of the remote intelligent control method for a barrier gate machine as described in any one of claims 1-6 are implemented.

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