Security check image processing method and device, equipment, storage medium and program product

Abstract

The invention discloses a security check image processing method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing. The method comprises the following steps: an intelligent terminal sends acquired image data of a user article to a distribution server; the shunting server obtains a first server load ratio of each node server in a service cluster and then sends the image data to a target node server, the service cluster comprises a plurality of node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used for indicating whether the target article exists in the image data or not.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a method, device, equipment, storage medium and program product for processing security inspection images. Background Art

[0002] With the continuous advancement of image preprocessing technology and the development of intelligent image resolution technology, intelligent security inspection has become a reality. The combination of these advanced technologies has made it possible to automatically detect contraband in public places such as stations and hospitals, greatly improving security inspection efficiency and reducing the workload of security inspectors. However, during peak hours, the server needs to process a large amount of image data during intelligent security inspection. If the server processing capacity is insufficient, the security inspection system will respond slowly, or even experience delays or freezes. This will not only reduce security inspection efficiency, but may also cause passenger dissatisfaction and safety hazards. Summary of the invention

[0003] The embodiments of the present application provide a method, apparatus, device, storage medium and program product for processing security inspection images, which can improve the processing capability of a service cluster and thereby improve the response speed of a security inspection system.

[0004] In a first aspect, an embodiment of the present application provides a method for processing a security inspection image, which is applied to a traffic diversion server, and the method includes:

[0005] Receiving image data of user items sent by the smart terminal;

[0006] Obtaining a first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers;

[0007] The image data is sent to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate whether a target object exists in the image data.

[0008] In a second aspect, an embodiment of the present application provides a method for processing a security inspection image, which is applied to a smart terminal, and the method includes:

[0009] Get image data of user items;

[0010] The image data is sent to a diversion server so that the diversion server obtains the first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers; the image data is sent to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate the presence or absence of a target object in the image data.

[0011] In a third aspect, an embodiment of the present application provides a method for processing a security inspection image, which is applied to a service cluster, wherein the service cluster includes a plurality of node servers, and the method includes:

[0012] The target node server receives the image data sent by the shunt server, and the image data is sent by the smart terminal to the shunt server. The target node server is the node server with the lowest first server load ratio in the service cluster, and the first server load ratio of each node server in the service cluster is obtained by the shunt server;

[0013] The target node server detects the image data and generates a detection result, where the detection result is used to indicate whether a target object exists in the image data;

[0014] The target node server sends the detection result to the smart terminal so that the detection result is displayed on the display interface of the smart terminal.

[0015] In a fourth aspect, an embodiment of the present application provides a security inspection image processing device, which is applied to a traffic diversion server, and the device includes:

[0016] A first receiving module, used to receive image data of user items sent by the smart terminal;

[0017] A first acquisition module, used to acquire a first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers;

[0018] The first sending module is used to send the image data to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate whether a target object exists in the image data.

[0019] In a fifth aspect, an embodiment of the present application provides a security inspection image processing device, which is applied to a smart terminal, and the device includes:

[0020] A second acquisition module is used to acquire image data of user items;

[0021] The second sending module is used to send the image data to the diversion server so that the diversion server obtains the first server load ratio of each node server in the service cluster, and the service cluster includes multiple node servers; send the image data to the target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is the node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate the presence or absence of the target object in the image data.

[0022] In a sixth aspect, an embodiment of the present application provides a security inspection image processing device, which is applied to a service cluster, wherein the service cluster includes a plurality of node servers, and the device includes:

[0023] A second receiving module is used for the target node server to receive the image data sent by the shunt server. The image data is sent by the smart terminal to the shunt server. The target node server is the node server with the lowest first server load ratio in the service cluster. The first server load ratio of each node server in the service cluster is obtained by the shunt server.

[0024] The first detection module is used for the target node server to detect the image data and generate a detection result, where the detection result is used to indicate whether a target object exists in the image data.

[0025] In a seventh aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for processing security inspection images as described in any one of the above items is implemented.

[0026] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the security inspection image processing method as described in any one of the above items is implemented.

[0027] In a ninth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the security inspection image processing method as described in any one of the above.

[0028] In the security inspection image processing method, device, equipment, storage medium and program product of the embodiment of the present application, the intelligent terminal can send the image data of the user's item to the diversion server; the diversion server obtains the first server load ratio of each node server in the service cluster, and then sends the image data to the target node server, the service cluster includes multiple node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used to indicate whether the target item exists in the image data. In this way, in the embodiment of the present application, by distributing the intelligent terminals, diversion servers and service clusters in the security inspection system, the diversion server can distribute the image data to the node server with the lowest first server load ratio according to the first server load ratio of each node server in the service cluster, so as to achieve load balancing of the service cluster, thereby improving the processing capacity of the service cluster, and then improving the response speed of the security inspection system, ensuring stable service quality during peak security inspection periods or when facing sudden loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 It is an architecture diagram of the security inspection system provided in the embodiment of the present application;

[0031] Figure 2 It is a flowchart of a method for processing security inspection images provided in an embodiment of the present application;

[0032] Figure 3 It is an interactive flow chart provided by an embodiment of the present application;

[0033] Figure 4 It is a single-point service interaction flow chart provided in an embodiment of the present application;

[0034] Figure 5 It is a structural schematic diagram of a security inspection image processing device provided in an embodiment of the present application;

[0035] Figure 6 is a structural schematic diagram of another security inspection image processing device provided in an embodiment of the present application;

[0036] Figure 7 is a structural schematic diagram of another security inspection image processing device provided in an embodiment of the present application;

[0037] Figure 8It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0040] With the continuous advancement of image preprocessing technology and the development of intelligent image resolution technology, intelligent security inspection has become a reality. The combination of these advanced technologies has made it possible to automatically detect contraband in public places such as stations and hospitals, greatly improving security inspection efficiency and reducing the workload of security inspectors. However, during peak hours, the server needs to process a large amount of image data during intelligent security inspection. If the server processing capacity is insufficient, the security inspection system will respond slowly, or even experience delays or freezes. This will not only reduce security inspection efficiency, but may also cause passenger dissatisfaction and safety hazards.

[0041] In order to solve the problems of the prior art, the embodiments of the present application provide a method, device, equipment, storage medium and program product for processing security inspection images. The following first introduces the security inspection system to which the method for processing security inspection images provided in the embodiments of the present application is applicable.

[0042] Figure 1 FIG. 1 shows an architecture diagram of a security inspection system provided by an embodiment of the present application. Figure 1As shown, the security inspection system 100 may include: an intelligent terminal 101, a distribution server 102 and a service cluster 103, wherein the service cluster 103 includes a plurality of node servers.

[0043] The above-mentioned smart terminal 101 can be a device connected to the security inspection machine at the security inspection point and integrated with artificial intelligence technology, which can be used to improve the efficiency and accuracy of security inspection. The smart terminal can also use the built-in AI algorithm to perform real-time analysis on the image data generated by the X-ray security inspection machine, identify the items therein, and compare them with the images of prohibited items in the database, thereby assisting security inspectors to quickly identify potential contraband. In addition, the above-mentioned smart terminal can also have a display screen for displaying the security inspection results to the security inspectors.

[0044] The above-mentioned shunt server 102 can be deployed in the cloud or a traditional physical device. In a traditional data center or private cloud environment, the shunt server may be a physical device, such as F5BIG-IP, Citrix NetScaler, etc. Physical shunt servers require the purchase, installation and maintenance of hardware devices. They are usually deployed at the network entrance of the data center to distribute the traffic entering the data center. In a cloud computing environment, the shunt server is usually provided in the form of a cloud service, called Cloud Load Balancer. The cloud load balancer can automatically adjust the traffic distribution, seamlessly integrate with the cloud service cluster, and provide high performance and high availability. Whether it is a cloud load balancer or a physical shunt server, their core functions are to manage and distribute network traffic to improve the availability and reliability of applications. The advantages of cloud load balancers are easy expansion, pay-as-you-go, rapid deployment and integration of other services of cloud service providers. Physical shunt servers may be more suitable for organizations that have specific performance requirements or want to control hardware in a private environment.

[0045] The above service cluster 103 can be a collection of multiple computing node servers. The CPU, memory, disk and other factors of each node server, including network bandwidth, will affect the response speed of the computing server, that is, different node servers have different processing capabilities. The distribution server will calculate the computing load of the node server based on the above factors combined with the number of connections of each node server, and finally distribute the image data to the node server with the smallest computing load.

[0046] A service cluster can be a cloud service cluster or a traditional physical server cluster, depending on the environment and architecture of the service deployment. Cloud service cluster: In a cloud computing environment, a service cluster usually refers to a group of virtual machines or container instances that run in the cloud and provide specific services. These service instances can be dynamically expanded and reduced to cope with changing load requirements. Cloud service clusters leverage the elasticity and scalability of the cloud platform to provide high availability and fault tolerance. Cloud service clusters can integrate various services of cloud providers, such as storage, database, monitoring, and automation tools. Physical server cluster: In a traditional data center, a service cluster may consist of a group of physical servers that are located in the same geographical location and connected through a network. Physical server clusters require the purchase and maintenance of actual hardware resources. Although physical clusters may not be as flexible as cloud service clusters in terms of scalability, they can provide customized hardware configuration and performance optimization. Whether it is a cloud service cluster or a physical server cluster, they are designed to improve the reliability, availability, and scalability of services through the collaborative work of multiple servers. The advantages of cloud service clusters are their rapid deployment, easy management, and automatic expansion capabilities, while physical server clusters may provide more control in terms of performance and cost control. In actual applications, the choice of service cluster depends on many factors, such as business needs, cost budget, technology preferences, and compliance requirements. Some organizations may adopt a hybrid cloud strategy, deploying some services in cloud service clusters and other services in physical server clusters.

[0047] The intelligent terminal 101, the traffic distribution server 102 and the service cluster 103 are connected in communication. Specifically, the communication can be performed using a connection method such as Ethernet or a wireless network. Of course, the communication method is not limited to this, and can also be a communication method of other network connections, which is not specifically limited here.

[0048] The following is an introduction to the security inspection image processing method provided in the embodiment of the present application.

[0049] Figure 2 The flowchart of the method for processing security inspection images provided by an embodiment of the present application is shown. Optionally, the method of the embodiment of the present application can be applied to the above Figure 1 The smart terminal 101, the distribution server 102 and the service cluster 103 are shown. Figure 2 As shown, a method 200 for processing a security inspection image may include the following steps S201 to S208:

[0050] S201, the intelligent terminal obtains image data of the user's item;

[0051] S202, the intelligent terminal sends the image data to the distribution server;

[0052] S203, the distribution server obtains a first server load ratio of each node server in the service cluster, where the service cluster includes multiple node servers;

[0053] S204, the distribution server sends the image data to the target node server, the target node server is the node server with the lowest first server load ratio in the service cluster;

[0054] S205: The target node server detects the image data and generates a detection result, where the detection result is used to indicate whether the target object exists in the image data.

[0055] In the method for processing security inspection images in the embodiment of the present application, the intelligent terminal can send the image data of the user's items to the diversion server; the diversion server obtains the first server load ratio of each node server in the service cluster, and then sends the image data to the target node server, the service cluster includes multiple node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used to indicate whether the target item exists in the image data. In this way, in the embodiment of the present application, by distributing the intelligent terminals, diversion servers and service clusters in the security inspection system, the diversion server can distribute the image data to the node server with the lowest first server load ratio according to the first server load ratio of each node server in the service cluster, so as to achieve load balancing of the service cluster, thereby improving the processing capacity of the service cluster, and then improving the response speed of the security inspection system, ensuring stable service quality during peak security inspection periods or when facing sudden loads.

[0056] In S201, the image data of the user's luggage is obtained, exemplarily, by obtaining the original image of the user's luggage through X-ray scanning by a security inspection machine connected to the smart terminal, and transmitting the original image to the smart terminal, which then performs preprocessing (such as image segmentation) and feature extraction (such as item location and item category, etc.) on the original image to obtain the image data of the user's luggage.

[0057] In S202, the smart terminal sends the image data to the distribution server. For example, the smart terminal may send the image data to the distribution server via a wireless network.

[0058] In S203, the load ratio of each first server is the ratio of the number of connections of each node server at the first moment to a preset server performance value.

[0059] The above-mentioned obtaining of the first server load ratio of each node server in the service cluster may, exemplarily, be obtaining the number of connections of each node server in the service cluster at a first moment; and determining the ratio of the number of connections of each node server at the first moment to a preset server performance value as the first server load ratio.

[0060] In S204, the target node server may be a node server with the lowest first server load ratio among a plurality of node servers in the service cluster at the first moment.

[0061] The above-mentioned shunt server sends the image data to the target node server. Exemplarily, the shunt server may send the image data to the target node server via a wireless network.

[0062] In S205, the target item may be a prohibited item in security inspection.

[0063] The target node server detects the image data and generates a detection result. For example, the target node server can compare the image data with each prohibited item in the prohibited item list and the safe item list based on the artificial intelligence model therein to generate the detection result. The artificial intelligence model is trained by a sample set of target items.

[0064] The above detection results can be used to indicate the presence or absence of the target object in the image data. In addition, when the detection result indicates the presence of the target object in the image data, the detection result also includes detailed information such as the image of the prohibited item, the specific location in the luggage, and the category to which the prohibited item belongs.

[0065] In some embodiments, the above S203 may specifically include:

[0066] Get the number of connections of each node server in the service cluster at the first moment;

[0067] The ratio of the number of connections of each node server at the first moment to the preset server performance value is determined as the load ratio of each first server.

[0068] The number of connections at the first moment above indicates the number of connections that the node server is processing at the first moment. This number changes with time and the situation of the server processing requests. The server performance value is a pre-configured value that can be configured by various factors such as the CPU, memory, and disk of each node server. It reflects the processing capacity of each node server. Different node servers have different processing capacities. The node server with a higher value can handle more connections.

[0069] In an embodiment of the present application, by obtaining the number of connections of each node server in the service cluster at the first moment, and determining the ratio of the number of connections of each node server at the first moment to a preset server performance value as the load ratio of each server, real-time load monitoring can be achieved, which is helpful for load balancing optimization and resource utilization improvement, thereby enhancing system stability.

[0070] As an implementation of the present application, in order to improve the security inspection efficiency, after the above S205, the above method may further include:

[0071] The target node server sends the detection results to the smart terminal;

[0072] The smart terminal displays the test results on the display interface.

[0073] The target node server sends the detection result to the smart terminal. Exemplarily, the target node server may send the detection result to the smart terminal via a wireless network.

[0074] The above-mentioned intelligent terminal displays the detection result in the display interface. For example, the intelligent terminal may mark the location where the detection result indicates that the target object exists in the image data and display it in the display interface. If the detection result indicates that the target object does not exist in the image data, no marking process is required.

[0075] In the embodiment of the present application, the test results are displayed through the smart terminal, which simplifies the security inspection process and reduces the complexity of manual operations. Security inspectors do not need to manually record and transmit the test results, thereby improving work efficiency.

[0076] As an implementation of the present application, in order to ensure that the network of the service cluster can still work normally after a certain number of failures occur, before the above S204, the above method may further include:

[0077] The offload server obtains the fault tolerance rate of the service cluster, which is the ratio of the difference between the total network capacity and the fault capacity in the service cluster to the total network capacity;

[0078] The above S204 may specifically include:

[0079] When the fault tolerance rate is greater than or equal to a preset threshold, the offload server sends the image data to the target node server.

[0080] The above fault tolerance rate can be the ratio of the difference between the total network capacity and the fault capacity in the service cluster to the total network capacity, where the total network capacity refers to the maximum capacity of the network without faults, and the fault capacity refers to the amount of faults in the network.

[0081] The above-mentioned preset threshold value may be a fault tolerance threshold value calculated based on the maximum amount of faults that can be tolerated in the network and the total capacity of the network.

[0082] In some embodiments, if the fault tolerance rate is less than a preset threshold, capacity expansion or emergency switching of a standby cluster may be adopted to handle disaster recovery.

[0083] In an embodiment of the present application, the fault tolerance rate of the service cluster is first obtained, and the image data is sent to the target node server only when the fault tolerance rate is greater than or equal to a preset threshold. This ensures that the network of the service cluster can still operate normally after a certain number of failures occur.

[0084] In some embodiments, the above S204 may specifically include:

[0085] Obtaining the fault status information of the target node server, where the fault status information is used to indicate whether the target node server is faulty;

[0086] When the fault status information indicates that the target node server is not faulty, the image data is sent to the target node server.

[0087] The above fault status information can be used to indicate whether the target node server is faulty.

[0088] The above-mentioned acquisition of the fault status information of the target node server may, for example, be that the service cluster performs a health check on the target node server and feeds back the test results to the diversion server to understand the fault status information of the target node server; or, the diversion server may send a heartbeat signal to the target node server. If the diversion server does not receive the heartbeat of the target node server within a certain period of time, it may indicate that the target node server has a fault.

[0089] In the embodiment of the present application, the diversion server obtains the fault status information of the target node server to understand whether the target node server is faulty, and only sends the image data to the target node server when the fault status information indicates that the target node server is not faulty, thereby ensuring that the target node server can detect the image data and guaranteeing the processing capacity of the security inspection system.

[0090] As another implementation of the present application, in order to further ensure the processing capability of the security inspection system, the above method may further include:

[0091] When the fault status information indicates that the target node server is faulty, the offload server obtains a second server load ratio of each node server in the service cluster, where each second server load ratio is a ratio of the number of connections of each node server at a second moment to a server performance value, and the second moment is greater than the first moment;

[0092] The distribution server determines the node server with the lowest second server load ratio in the service cluster as the standby node server;

[0093] The shunt server sends the image data to the standby node server;

[0094] The standby node server detects the image data and generates a detection result, where the detection result is used to indicate whether a target object exists in the image data;

[0095] The standby node server sends the detection result to the smart terminal so that the detection result is displayed on the display interface of the smart terminal.

[0096] The standby node server is a node server with the lowest second server load ratio in the service cluster at the second moment. The second moment is greater than the first moment, which means that the second moment is later than the first moment.

[0097] The above-mentioned standby node server detects the image data and generates the detection result, which is the same as the implementation process of the above-mentioned target node server detecting the image data and generating the detection result, and will not be repeated here.

[0098] In an embodiment of the present application, the diversion server can obtain the second server load ratio of each node server in the service cluster at the second moment when the fault status information indicates a failure of the target node server, and send the image data to the backup node server with the lowest second server load ratio, and detect the image data through the backup node server. When the target node server fails, the image data can be detected through the backup node server, thereby further ensuring the processing capacity of the security inspection system.

[0099] As another implementation of the present application, in order to reduce the amount of data sent to the offload server and reduce the data processing pressure for the service cluster, before the above S202, the above method may further include:

[0100] The intelligent terminal recognizes the image data and generates a recognition result, which is used to indicate whether the target object exists in the image data;

[0101] The above S202 may specifically include:

[0102] When the recognition result indicates that the target object does not exist in the image data, the image data is sent to the diversion server.

[0103] The above recognition result can be used to indicate whether the target object exists in the image data.

[0104] The above-mentioned smart terminal identifies the image data and generates an identification result. For example, the smart terminal can use the integrated edge computing technology to compare the image data with the prohibited items list and the safe items list to generate the identification result. Once the prohibited items are found, they will be reported immediately. The reported information includes the image of the prohibited items, the specific location in the luggage, and the category of the prohibited items. If no prohibited items are detected, the safe items identified by AI will be removed from the image list. For the uncertain image data still in the image list, it will be compressed and packaged, marked with the number of the smart terminal, and then uploaded to the diversion server for further analysis and processing.

[0105] In some embodiments, the above method may further include:

[0106] When the recognition result indicates that the target object exists in the image data, the recognition result is displayed in the display interface.

[0107] In the case where the recognition result indicates that the target object exists in the image data, the recognition result is directly displayed in the display interface, and the relevant information of the target object will be displayed in real time on the display interface of the smart terminal so that the staff can respond quickly. In addition, this part of the image data will not be transmitted to the service cluster through the diversion server, which can further reduce the data processing pressure of the service cluster.

[0108] In an embodiment of the present application, the intelligent terminal can first identify the image data and generate an identification result. Only when the identification result indicates that the target object does not exist in the image data, the image data will be sent to the diversion server. This can not only reduce the amount of data sent to the diversion server and reduce the data processing pressure on the service cluster, but also the service cluster can re-detect the image data whose identification result indicates that the target object does not exist in the image data, thereby further improving the accuracy of security inspection.

[0109] In order to facilitate the understanding of the security inspection image processing method in the embodiment of the present application, the actual application process of the security inspection image processing method is described as follows:

[0110] The workflow of intelligent security inspection can be specifically described through the following interactive flowchart. This flowchart shows in detail the various steps from data collection to analysis and processing, as well as how to improve security inspection efficiency and response speed by optimizing the network structure. Figure 3 and Figure 4 The security inspection process shown in the figure, the implementation of this application is described as follows:

[0111] Data collection: The security inspection machine obtains image data of passenger luggage through X-ray scanning and sends it to the smart terminal device for image preprocessing.

[0112] Terminal diversion: The intelligent terminal uses integrated edge computing technology to disassemble the original image and compare the disassembled images with the prohibited items list and the safe items list respectively. Through the AI ​​intelligent recognition module, once prohibited items are found, the system will report them immediately. The reported information includes detailed information such as the image of the prohibited items, the specific location in the luggage, and the category to which the prohibited items belong. At the same time, the relevant information of the prohibited items will be displayed in real time on the display screen of the security inspection terminal so that the staff can respond quickly. If no prohibited items are detected, the system will remove the safe items identified by AI from the image list. For uncertain image data that still exists in the image list, the system will compress and package them, mark them with the number of the intelligent security inspection terminal, and then upload them to the diversion system (equivalent to the above-mentioned diversion server) for further analysis and processing.

[0113] Data diversion: After receiving the data list of unknown items uploaded by the smart terminal, the diversion system first identifies the application scenario to which the item belongs based on the smart terminal number bound to the data packet. Subsequently, the system intelligently dispatches the data packet to the corresponding processing cluster to ensure that the data can be processed accurately and efficiently. In a specific processing cluster, load balancing technology is used to evenly distribute the received massive data to the computing power server nodes equipped with AI recognition modules (equivalent to each node server in the above service cluster). The AI ​​recognition module uses feature recognition technology to determine whether there are prohibited items in the item list.

[0114] AI recognition: The uploaded security inspection image data is sent to the service cluster in the corresponding field according to the assignment of the diversion system. The service cluster begins to extract image features, and then uses the AI ​​recognition module with a more rigorous algorithm to identify the image, detect contraband, and generate detection results.

[0115] Result feedback: The results of AI analysis are quickly fed back to the smart terminal at the security checkpoint to guide security personnel to take corresponding measures.

[0116] Through this process, the Intelligent Inspection Platform has achieved efficient and accurate intelligent security inspection, greatly improving security inspection efficiency and response speed.

[0117] Diversion system: By deploying a distributed architecture, the system can achieve load balancing, ensuring that data is evenly distributed to each node server, thereby improving the overall processing capacity and response speed. The dynamic resource allocation mechanism combined with the network redundancy design further enhances the flexibility and reliability of the system, ensuring that even during peak hours or when facing sudden loads, it can maintain stable service quality.

[0118] The main measure of diversion is to handle the load balancing of data. Load balancing technology is used to distribute workloads among multiple servers, network links or data centers to improve resource utilization, maximize throughput, reduce response time, and avoid overloading of any single resource. This application uses the weighted least connection algorithm, which is a weighted version of the least connection algorithm. It takes into account the processing power of the server (the processing power of all single machines is calculated by comprehensively calculating multiple factors such as the CPU, memory, disk or bandwidth of the server host, and this value is defined as the server weight of the single machine (equivalent to the above-mentioned server performance value)), and adjusts its number of connections by assigning a weight to each server. The algorithm fully considers the differences between servers based on the principles of weight allocation, number of connections monitoring, request allocation, and dynamic adjustment, and makes dynamic adjustments in order to achieve the highest efficiency. The formula of the weighted least connection algorithm is used to calculate the load weight of each server (equivalent to the above-mentioned load ratio) to determine which server a new request should be assigned to. The core idea of ​​this algorithm is to calculate a ratio based on the current number of connections to each server and the weight assigned to it, and then select the server with the lowest ratio to handle new requests. The lower the ratio, the faster the response speed of the node. The formula is as follows:

[0119] Server load weight = current number of connections / server weight

[0120] The two key parameters here are:

[0121] Current number of connections: The number of connections currently being processed by the server. This number changes over time and as the server processes requests.

[0122] Server Weight: This is a preconfigured value that reflects the processing power of the server. Servers with higher weights can handle more connections.

[0123] In addition to using load balancing to divert data, the stability and timeliness of data processing by single-point servers must also be considered. Network redundancy design is a strategy to improve network reliability and fault tolerance. It introduces additional components or paths in the network to ensure that the entire network can still work normally even in the event of partial network failure. An important indicator of network redundancy design is the fault tolerance rate, which is the ability of the network to still work normally after a certain number of failures. The fault tolerance rate can be determined by calculating the maximum number of failures in the network. The formula can be:

[0124] [Fault tolerance rate = (total network capacity - fault capacity) / total network capacity]

[0125] The total network capacity refers to the maximum capacity of the network without any faults, and the fault capacity refers to the number of faults in the network. You can set a threshold monitoring for this parameter. When the number of faults exceeds a certain threshold or the fault tolerance rate is lower than a certain threshold, you can expand the capacity or urgently switch to the backup cluster to handle disaster recovery.

[0126] Design multiple different communication paths in the network to avoid single point failures. This application can be understood as having multiple nodes in the cluster to receive data forwarded by the diversion system, and the system needs to pre-calculate the processing power of each node, which is the server weight mentioned above. This can be calculated and optimized through the shortest path algorithm in graph theory. The shortest path algorithm is an algorithm used to find the shortest path between two points in a weighted graph. The shortest path algorithm is applicable to directed and undirected graphs as long as the weights of all edges in the graph are non-negative. What is calculated in this application is the response time between the diversion system and each node server in the cluster, which is the server load weight mentioned above.

[0127] Basic steps of the algorithm

[0128] Step 1: Initialization, calculate the response time of a connection according to the different server weights of each host in the cluster. Create a priority queue in ascending order of response time, and set the response time of all nodes in the queue to 0 because there is no data access yet.

[0129] Step 2: Access. When the diversion system sends data to the cluster, the cluster will decompose the access data into independent item data (including item image, smart terminal number, application category, image feature parameters, etc.) and distribute it to the computing terminal server. It selects the first node (with the fastest response speed) from the priority queue as the current point and marks it as visited.

[0130] Step 3: Update the second item data, and then get the second node from the priority queue and mark it as visited. When all nodes receive the access data, calculate the server load weight based on the current number of connections and the server weight, that is, the response time after processing all current connections. Always update the priority queue in order of response time from small to large. The principle of selecting nodes in the queue is to give priority to unvisited nodes, and then select the node with the fastest response based on the node's response time. The node with the fastest response processes the item data.

[0131] Step 4: Repeat, step 2 and step 3 until all points have been visited or data distribution is completed.

[0132] Step 5: End. When all data distribution is completed, the queue will still be sorted according to the initial state.

[0133] The time complexity of the shortest path algorithm depends on the implementation of the priority queue used. If a Fibonacci heap is used, the time complexity can be reduced to O(E+V log V), where E is the number of edges and V is the number of vertices. If a simple array is used to implement the priority queue, the time complexity will be O(V^2).

[0134] In addition, the security of the network structure is another key consideration. The security inspection system integrates multiple security measures, including data encryption, firewalls, and intrusion detection systems, to fully protect data security and user privacy. The integration of these security mechanisms not only ensures the integrity of data during transmission, but also prevents potential network threats and unauthorized access, building a solid security line of defense for the intelligent security inspection system.

[0135] Based on the security inspection image processing method provided in the above embodiment, the present application also provides a specific implementation of the security inspection image processing device. Please refer to the following embodiment.

[0136] like Figure 5 As shown, a security inspection image processing device 500 provided in an embodiment of the present application is applied to a diversion server. The device 500 may include the following modules: a first receiving module 501, a first acquiring module 502 and a first sending module 503.

[0137] The first receiving module 501 is used to receive the image data of the user's item sent by the smart terminal;

[0138] A first acquisition module 502 is used to acquire a first server load ratio of each node server in a service cluster, where the service cluster includes a plurality of node servers;

[0139] The first sending module 503 is used to send the image data to the target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is the node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate whether the target object exists in the image data.

[0140] In the security inspection image processing device of the embodiment of the present application, the intelligent terminal can send the image data of the user's item to the diversion server; the diversion server obtains the first server load ratio of each node server in the service cluster, and then sends the image data to the target node server, the service cluster includes multiple node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used to indicate whether the target item exists in the image data. In this way, in the embodiment of the present application, the intelligent terminal, diversion server and service cluster in the security inspection system are distributedly deployed, wherein the diversion server can distribute the image data to the node server with the lowest first server load ratio according to the first server load ratio of each node server in the service cluster, so as to achieve load balancing of the service cluster, thereby improving the processing capacity of the service cluster, and then improving the response speed of the security inspection system, ensuring stable service quality during peak security inspection periods or when facing sudden loads.

[0141] In some embodiments, the first acquisition module 502 may specifically include:

[0142] An acquisition unit, used to acquire the number of connections of each node server in the service cluster at a first moment;

[0143] The determination unit is used to determine the ratio of the number of connections of each node server at a first moment to a preset server performance value as the load ratio of each first server.

[0144] As an implementation of the present application, in order to ensure that the network of the service cluster can still work normally after a certain number of failures occur, the above-mentioned device 500 may also include:

[0145] The third acquisition module is used to obtain the fault tolerance rate of the service cluster, where the fault tolerance rate is the ratio of the difference between the total network capacity and the fault capacity in the service cluster to the total network capacity;

[0146] The first sending module 504 is specifically configured to send the image data to the target node server when the fault tolerance rate is greater than or equal to a preset threshold.

[0147] In some embodiments, the first sending module 504 may specifically include:

[0148] A first acquisition unit is used to acquire fault status information of a target node server, where the fault status information is used to indicate whether the target node server is faulty;

[0149] The sending unit is used to send the image data to the target node server when the fault status information indicates that the target node server is not faulty.

[0150] As another implementation of the present application, in order to further ensure the processing capability of the security inspection system, the first sending module 504 may further include:

[0151] A second acquisition unit is used to acquire a second server load ratio of each node server in the service cluster when the fault state information indicates that the target node server has a fault, wherein each second server load ratio is a ratio of the number of connections of each node server at a second moment to a server performance value, and the second moment is greater than the first moment;

[0152] A determination unit, configured to determine a node server having the lowest second server load ratio in the service cluster as a standby node server;

[0153] The above-mentioned sending unit is also used to send the image data to the backup node server so that the backup node server detects the image data and generates a detection result, which is used to indicate the presence or absence of the target object in the image data; and send the detection result to the smart terminal so that the detection result is displayed in the display interface of the smart terminal.

[0154] like Figure 6 As shown, another security inspection image processing device 600 provided in an embodiment of the present application is applied to a smart terminal. The device 600 may include the following modules: a second acquisition module 601 and a second sending module 602.

[0155] The second acquisition module 601 is used to acquire image data of the user's items;

[0156] The second sending module 602 is used to send the image data to the diversion server so that the diversion server obtains the first server load ratio of each node server in the service cluster, and the service cluster includes multiple node servers; the image data is sent to the target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is the node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate the presence or absence of the target object in the image data.

[0157] In the security inspection image processing device of the embodiment of the present application, the intelligent terminal can send the image data of the user's item to the diversion server; the diversion server obtains the first server load ratio of each node server in the service cluster, and then sends the image data to the target node server, the service cluster includes multiple node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used to indicate whether the target item exists in the image data. In this way, in the embodiment of the present application, the intelligent terminal, diversion server and service cluster in the security inspection system are distributedly deployed, wherein the diversion server can distribute the image data to the node server with the lowest first server load ratio according to the first server load ratio of each node server in the service cluster, so as to achieve load balancing of the service cluster, thereby improving the processing capacity of the service cluster, and then improving the response speed of the security inspection system, ensuring stable service quality during peak security inspection periods or when facing sudden loads.

[0158] As an implementation of the present application, in order to improve security inspection efficiency, the above-mentioned device 600 may further include:

[0159] A third receiving module is used to receive the detection result sent by the target node server;

[0160] The display module is used to display the test results on the display interface of the smart terminal.

[0161] As another implementation of the present application, in order to reduce the amount of data sent to the offload server and reduce the data processing pressure for the service cluster, the above-mentioned device 600 may also include:

[0162] A recognition module, used to recognize the image data and generate a recognition result, wherein the recognition result is used to indicate whether a target object exists in the image data;

[0163] The second sending module 602 is specifically configured to send the image data to the diversion server when the recognition result indicates that the target object does not exist in the image data.

[0164] like Figure 7 As shown, another security inspection image processing device 700 provided in an embodiment of the present application is applied to a service cluster. The device 700 may include the following modules: a second receiving module 701 and a first detecting module 702 .

[0165] The second receiving module 701 is used for the target node server to receive the image data sent by the shunt server. The image data is sent by the smart terminal to the shunt server. The target node server is the node server with the lowest first server load ratio in the service cluster. The first server load ratio is the ratio of the number of connections of each node server at the first moment to the preset server performance value. The first server load ratio of each node server in the service cluster is obtained by the shunt server.

[0166] The first detection module 702 is used for the target node server to detect the image data and generate a detection result, where the detection result is used to indicate whether the target object exists in the image data.

[0167] In the security inspection image processing device of the embodiment of the present application, the intelligent terminal can send the image data of the user's item to the diversion server; the diversion server obtains the first server load ratio of each node server in the service cluster, and then sends the image data to the target node server, the service cluster includes multiple node servers, and the target node server is the node server with the lowest first server load ratio in the service cluster; the target node server detects the image data and generates a detection result, and the detection result is used to indicate whether the target item exists in the image data. In this way, in the embodiment of the present application, the intelligent terminal, diversion server and service cluster in the security inspection system are distributedly deployed, wherein the diversion server can distribute the image data to the node server with the lowest first server load ratio according to the first server load ratio of each node server in the service cluster, so as to achieve load balancing of the service cluster, thereby improving the processing capacity of the service cluster, and then improving the response speed of the security inspection system, ensuring stable service quality during peak security inspection periods or when facing sudden loads.

[0168] As an implementation of the present application, in order to improve security inspection efficiency, the above-mentioned device 700 may further include:

[0169] The third sending module is used for the target node server to send the detection result to the smart terminal so that the detection result is displayed in the display interface of the smart terminal.

[0170] As another implementation of the present application, in order to further ensure the processing capability of the security inspection system, the above-mentioned device 700 may also include:

[0171] A fourth receiving module is used for a backup node server to receive image data sent by a shunt server. The backup node server is a node server with the lowest second server load ratio in the service cluster. Each second server load ratio is a ratio of the number of connections of each node server at a second moment to a server performance value. The second moment is greater than the first moment. The second server load ratio of each node server in the service cluster is obtained by the shunt server when the fault state information indicates a target node server fault.

[0172] A second detection module is used for the backup node server to detect the image data and generate a detection result, where the detection result is used to indicate whether the target object exists in the image data;

[0173] The fourth sending module is used for the backup node server to send the detection result to the smart terminal, so that the detection result is displayed on the display interface of the smart terminal.

[0174] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0175] The electronic device may include a processor 801 and a memory 802 storing computer program instructions.

[0176] Specifically, the processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0177] The memory 802 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 802 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 802 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid-state memory.

[0178] In certain embodiments, the memory 802 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0179] The processor 801 implements any one of the security inspection image processing methods in the above embodiments by reading and executing computer program instructions stored in the memory 802 .

[0180] In one example, the electronic device may further include a communication interface 803 and a bus 810. Figure 8 As shown, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 810 and communicate with each other.

[0181] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0182] Bus 810 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 810 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0183] The electronic device can execute the security inspection image processing method in the embodiment of the present application, thereby realizing the combination Figure 2 , Figures 5 to 7 The invention describes a method and device for processing security inspection images.

[0184] In addition, in combination with the security inspection image processing method in the above embodiment, the embodiment of the present application can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the security inspection image processing methods in the above embodiment is implemented.

[0185] In combination with the security inspection image processing method in the above embodiments, an embodiment of the present application may provide a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes any of the security inspection image processing methods as above.

[0186] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0187] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0188] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0189] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0190] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for processing security inspection images, characterized in that: Applied to a traffic distribution server, the method comprises: Receiving image data of user items sent by the smart terminal; Obtaining a first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers; The image data is sent to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate whether a target object exists in the image data.

2. The method according to claim 1, characterized in that The obtaining of the first server load ratio of each node server in the service cluster includes: Get the number of connections of each node server in the service cluster at the first moment; The ratio of the number of connections of each of the node servers at the first moment to a preset server performance value is determined as the load ratio of each of the first servers.

3. The method according to claim 1, characterized in that Before sending the image data to the target node server, the method further includes: Acquire a fault tolerance rate of the service cluster, where the fault tolerance rate is a ratio of a difference between a total network capacity and a fault capacity in the service cluster to the total network capacity; The sending the image data to the target node server comprises: When the fault tolerance rate is greater than or equal to a preset threshold, the image data is sent to the target node server.

4. The method according to claim 1, characterized in that The sending the image data to the target node server comprises: Acquire fault status information of the target node server, where the fault status information is used to indicate whether the target node server is faulty; When the fault status information indicates that the target node server is not faulty, the image data is sent to the target node server.

5. The method according to claim 4, characterized in that The method further comprises: When the fault status information indicates that the target node server is faulty, obtaining a second server load ratio of each node server in the service cluster, where each second server load ratio is a ratio of the number of connections of each node server at a second moment to the server performance value, and the second moment is greater than the first moment; Determine a node server with the lowest second server load ratio in the service cluster as a standby node server; The image data is sent to the backup node server so that the backup node server detects the image data and generates a detection result, wherein the detection result is used to indicate the presence or absence of the target object in the image data; the detection result is sent to the smart terminal so that the detection result is displayed on the display interface of the smart terminal.

6. A method for processing security inspection images, characterized in that: Applied to a smart terminal, the method comprises: Get image data of user items; The image data is sent to a diversion server so that the diversion server obtains the first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers; the image data is sent to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate the presence or absence of a target object in the image data.

7. The method according to claim 6, characterized in that The method further comprises: Receiving the detection result sent by the target node server; The detection result is displayed on the display interface of the smart terminal.

8. The method according to claim 6, characterized in that Before sending the image data to the shunt server, the method further includes: Recognize the image data and generate a recognition result, wherein the recognition result is used to indicate whether a target object exists in the image data; The sending of the image data to the shunt server comprises: When the recognition result indicates that the target object does not exist in the image data, the image data is sent to a diversion server.

9. A method for processing security inspection images, characterized in that: Applied to a service cluster, the service cluster includes a plurality of node servers, and the method includes: The target node server receives the image data sent by the shunt server, and the image data is sent by the smart terminal to the shunt server. The target node server is the node server with the lowest first server load ratio in the service cluster, and the first server load ratio of each node server in the service cluster is obtained by the shunt server; The target node server detects the image data and generates a detection result, where the detection result is used to indicate whether a target object exists in the image data.

10. The method according to claim 9, characterized in that The method further comprises: The target node server sends the detection result to the smart terminal so that the detection result is displayed on the display interface of the smart terminal.

11. The method according to claim 9, characterized in that The method further comprises: The backup node server receives the image data sent by the shunt server, the backup node server is a node server with the lowest second server load ratio in the service cluster, each second server load ratio is a ratio of the number of connections of each node server at a second moment to the server performance value, the second moment is greater than the first moment, and the second server load ratio of each node server in the service cluster is obtained by the shunt server when the fault state information indicates that the target node server has a fault; The backup node server detects the image data and generates a detection result, where the detection result is used to indicate whether the target object exists in the image data; The backup node server sends the detection result to the smart terminal so that the detection result is displayed on the display interface of the smart terminal.

12. A security inspection image processing device, characterized in that: Applied to a traffic distribution server, the device comprises: A first receiving module, used to receive image data of user items sent by the smart terminal; A first acquisition module, used to acquire a first server load ratio of each node server in a service cluster, wherein the service cluster includes a plurality of the node servers; The first sending module is used to send the image data to a target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is a node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate whether a target object exists in the image data.

13. A security inspection image processing device, characterized in that: Applied to a smart terminal, the device comprises: A second acquisition module is used to acquire image data of user items; The second sending module is used to send the image data to the diversion server so that the diversion server obtains the first server load ratio of each node server in the service cluster, and the service cluster includes multiple node servers; send the image data to the target node server so that the target node server detects the image data and generates a detection result, wherein the target node server is the node server with the lowest first server load ratio in the service cluster, and the detection result is used to indicate the presence or absence of the target object in the image data.

14. A security inspection image processing device, characterized in that: Applied to a service cluster, the service cluster includes a plurality of node servers, and the device includes: A second receiving module is used for the target node server to receive the image data sent by the shunt server. The image data is sent by the smart terminal to the shunt server. The target node server is the node server with the lowest first server load ratio in the service cluster. The first server load ratio of each node server in the service cluster is obtained by the shunt server. The first detection module is used for the target node server to detect the image data and generate a detection result, where the detection result is used to indicate whether a target object exists in the image data.

15. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for processing security inspection images according to any one of claims 1 to 11 is implemented.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for processing security inspection images according to any one of claims 1 to 11 is implemented.

17. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the security inspection image processing method as described in any one of claims 1 to 11.

Images