Camera position determination method and apparatus, and nonvolatile storage medium

Abstract

The application discloses a camera position determination method and device and a nonvolatile storage medium. The method comprises the following steps: obtaining target object attribute information in a preset range, wherein the target object attribute information at least comprises a target object behavior index and a target object hardware index, the target object behavior index is used for indicating behavior information associated with the target object, and the target object hardware index is used for indicating hardware information associated with the target object in a data transmission process; determining a behavior characteristic portrait based on the target object attribute information; determining a target audience portrait based on the target object attribute information and the behavior characteristic portrait, wherein the target audience portrait is used for representing the association between the behavior characteristics of the target object and a camera collecting the behavior characteristics; and determining the position of the camera based on the target audience portrait. The application solves the technical problem that the camera cannot track the target object in the preset range, thereby failing to meet the security requirements.

Description

Technical Field

[0001] This application relates to the field of data processing, and more specifically, to a method, apparatus, and non-volatile storage medium for determining the location of a camera. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), cameras, as an important tool for information collection, are widely used in various scenarios such as monitoring and data acquisition. However, traditional cameras have the following problems when monitoring targets within a preset range: 1) The increase in the number of cameras leads to a decrease in information collection efficiency and an increase in blind spots; 2) Camera deployment often depends on the subjective will of the installer, making it impossible to consider the overall situation; 3) Unreasonable camera deployment prevents cameras from achieving their intended effectiveness. For example, in a community monitored by a community management system, community public safety needs to be strengthened. There are many hidden dangers in the management and security of the area surrounding and within the community. Some older communities lack basic public facilities, video surveillance has blind spots, and the level of intelligence in community security and fire emergency management is low. Many community video surveillance systems cannot meet the needs of security and prevention. Therefore, there is currently a problem that cameras cannot track targets within a preset range, resulting in a failure to meet security requirements.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and non-volatile storage medium for determining the location of a camera, in order to at least solve the current technical problem that it is impossible to track target objects within a preset range, thus failing to meet security requirements.

[0005] According to one aspect of the embodiments of this application, a method for determining the location of a camera is provided, comprising: acquiring target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavior indicators and target object hardware indicators, the target object behavior indicators indicating behavior information associated with the target object, and the target object hardware indicators indicating hardware information associated with the target object during data transmission; determining a behavior feature profile based on the target object attribute information, wherein the behavior feature profile represents the attribute distribution of different behavior features of the target object; determining a target audience profile based on the target object attribute information and the behavior feature profile, wherein the target audience profile represents the association between the behavior features of the target object and the camera that collects the behavior features; and determining the location of the camera based on the target audience profile.

[0006] In some embodiments of this application, before obtaining the target object attribute information within a preset range, the method further includes: obtaining all data within the preset range, wherein all data includes target object attribute information; determining the level to which all data belongs; clustering all data with each level as the cluster center, and determining the target data collected at each level.

[0007] In some embodiments of this application, obtaining target object attribute information within a preset range includes: obtaining log data associated with the target object; after preprocessing the log data, extracting target object behavior indicators and target object hardware indicators, as well as scene information and camera information associated with the target object from the preprocessed log data; and determining target object attribute information based on the target object behavior indicators, target object hardware indicators, scene information, and camera information.

[0008] In some embodiments of this application, determining a behavioral feature profile based on target object attribute information includes: clustering target object behavior indicators in the target object attribute information to obtain multiple behavioral feature sets, wherein each behavioral feature set contains a behavioral feature and a target object classified to the behavioral feature; determining the attribute distribution of each behavioral feature set, wherein the attribute distribution is used to represent the probability distribution of the attribute of each behavioral feature in the corresponding behavioral feature set; and determining a behavioral feature profile based on the behavioral feature set and the attribute distribution of the behavioral feature set.

[0009] In some embodiments of this application, determining the target audience profile based on target object attribute information and behavioral feature profile includes: obtaining the target object hardware indicators and scene information and camera information associated with the target object from the target object attribute information; associating the behavioral feature profile with the target object hardware indicators, the scene information associated with the target object, and the camera information to determine the hardware requirements of each behavioral feature set in the behavioral feature profile, wherein the hardware requirements are used to indicate the hardware resource usage of the target object under the corresponding behavioral feature and the location of the camera; and determining the target audience profile based on the hardware requirements.

[0010] In some embodiments of this application, determining the location of a camera based on a target audience profile includes: determining a first set of behavioral features corresponding to a first target object in a behavioral feature profile, wherein the first target object is any target object; determining a first hardware requirement corresponding to the first set of behavioral features in the target audience profile; and determining the location of the camera based on the first hardware requirement.

[0011] In some embodiments of this application, the method further includes: when the monitoring scenes corresponding to multiple cameras overlap, determining the transmission nodes corresponding to the overlapping data of the multiple cameras in the monitoring scene; determining two adjacent transmission nodes as a master node, and having the master node manage the transmission data of the cameras corresponding to the two adjacent transmission nodes.

[0012] In some embodiments of this application, the method further includes: obtaining a target file stored in a database, wherein the target file is a file used to store target object attribute information; after identifying that the target file has been tampered with, determining the attack path corresponding to the target file; marking files that overlap with the attack path as isolated files, and marking files that do not overlap with the attack path as non-isolated files; deleting isolated files, and feeding back the processing result to the terminal.

[0013] According to another aspect of the embodiments of this application, a camera location determination device is also provided, comprising: an acquisition module, configured to acquire target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavior indicators and target object hardware indicators, the target object behavior indicators indicating behavior information associated with the target object, and the target object hardware indicators indicating hardware information associated with the target object during data transmission; a first determination module, configured to determine a behavior feature profile based on the target object attribute information, wherein the behavior feature profile represents the attribute distribution of different behavior features of the target object; a second determination module, configured to determine a target audience profile based on the target object attribute information and the behavior feature profile, wherein the target audience profile represents the association between the behavior features of the target object and the camera that collects the behavior features; and a third determination module, configured to determine the location of the camera based on the target audience profile.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, wherein the program controls the device where the non-volatile storage medium is located to execute the above-described method for determining the camera position when it runs.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes the above-described method for determining the camera position when it runs.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including computer instructions, which, when executed by a processor, implement the above-described method for determining the camera position.

[0017] In this embodiment, the method involves acquiring target object attribute information within a preset range. This target object attribute information includes at least target object behavioral indicators and target object hardware indicators. The behavioral indicators indicate behavioral information associated with the target object, and the hardware indicators indicate hardware information associated with the target object during data transmission. A behavioral feature profile is determined based on the target object attribute information, representing the attribute distribution of different behavioral features of the target object. A target audience profile is determined based on the target object attribute information and the behavioral feature profile, representing the association between the target object's behavioral features and the camera collecting these features. The camera location is determined based on the target audience profile. By acquiring target object attribute information within a preset range, constructing a behavioral feature profile based on the target object attribute information, further determining the target audience profile, and then determining the camera location based on the target audience profile, the method enables camera tracking of the target object. This solves the current technical problem where camera tracking of target objects within a preset range is not possible, resulting in failure to meet security requirements. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a hardware structure block diagram of a computer terminal for implementing a method for determining the position of a camera, according to an embodiment of this application.

[0020] Figure 2 This is a flowchart illustrating a method for determining the position of a camera according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of a camera position determination device provided according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] The information collected in this application embodiment is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, and necessary confidentiality measures have been taken. It does not violate public order and good morals, and provides corresponding operation entry points for users to choose to authorize or reject the automated decision results. If the user chooses to reject, the process will proceed to the expert decision-making process.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0026] K-means++ is an algorithm for selecting initial values ​​(or "seeds") for the K-means clustering algorithm. The selection of the positions of the K initial centroids has a significant impact on the final clustering results and running time, so it is necessary to select K suitable centroids. The basic idea of ​​the K-means algorithm is still to minimize the sum of squared deviations, but in its specific implementation, it adopts a method of successive iterative correction.

[0027] RedCap is a new technology standard protocol launched by the 3rd Generation Partnership Project (3GPP) in the 5G Release 17 phase. Its goal is to comprehensively improve the quality and coverage of 5G networks, while removing some functions, and it can be regarded as a "lightweight 5G". RedCap has many application scenarios, including power, industrial data acquisition, security, vehicle networking, wearable devices and other fields.

[0028] In related technologies, traditional cameras have the following problems when monitoring target objects within a preset range: 1) The increase in the number of cameras leads to a decrease in information collection efficiency and an increase in blind spots; 2) Camera deployment often depends on the subjective will of the installer, making it impossible to consider the overall situation; 3) Unreasonable camera deployment results in cameras failing to perform their due function. For example, in a community monitored by a community management system, community public security needs to be strengthened. Therefore, there is a problem that current cameras cannot track target objects within a preset range, resulting in a failure to meet security requirements. To solve this problem, this application provides a relevant solution.

[0029] According to an embodiment of this application, a method embodiment for determining the position of a camera is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal for implementing a method for determining the position of a camera is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a form of processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the camera position determination method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned camera position determination method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0034] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0035] Under the above operating environment, this application embodiment provides a flowchart illustrating a method for determining the position of a camera, as shown below. Figure 2 As shown, it includes the following steps:

[0036] Step S202: Obtain target object attribute information within a preset range. The target object attribute information includes at least target object behavior indicators and target object hardware indicators. The target object behavior indicators are used to indicate the behavior information associated with the target object, and the target object hardware indicators are used to indicate the hardware information associated with the target object during data transmission.

[0037] In the technical solution provided in step S202, before obtaining the target object attribute information within a preset range, all data within the preset range are obtained, including the target object attribute information; the level to which all data belongs is determined; all data are clustered with each level as the cluster center to determine the target data collected at each level.

[0038] The following is a specific example:

[0039] Currently, data collection across different communities involves a large amount of repetitive and non-shared data. This data is entered into different platforms and managed uniformly by the smart community management platform. The smart community management platform consists of an infrastructure layer, platform layer, application layer, user layer, and management and support system. Data within the preset scope managed by the smart community management platform exhibits data duplication across different levels. Therefore, before obtaining the target object attribute information within the preset scope, all data within the preset scope, including target object attribute information, must be acquired; the level to which all data belongs must be determined; and all data must be clustered using each level as the cluster center to determine the target data collected at each level. For example, this can be achieved through the following methods:

[0040] First, all relevant data is acquired from each level of the smart community management platform, including the infrastructure layer, platform layer, application layer, user layer, and management and support system. This data includes, but is not limited to, video surveillance records, access control system data, IoT sensor information, community service usage records, and resident basic information. The preset scope can be data within a specific time period or data from a specific geographical area. Using the K-means++ algorithm, each level is used as a cluster center to perform cluster analysis on all data. This step ensures that data at each level can be processed efficiently and classified accurately. Through clustering, data with similar characteristics are grouped into the same category, reducing data redundancy and improving data processing efficiency. After clustering, the specific target data collected at each level is determined. For example, the infrastructure layer may primarily focus on video surveillance and IoT sensor data, while the application layer may focus more on community service usage records. Clearly defining the data focus at each level helps to more effectively analyze and utilize data when subsequently building behavioral characteristic profiles and target audience profiles (or monitoring audience profiles). The clustering process is as follows:

[0041] 1. Randomly select a data point from the data at each level of the smart community management platform as the first cluster center. For example, the first data point in the infrastructure layer, the second data point in the platform layer, the third data point in the application layer, the fourth data point in the user layer, and the fifth data point in the management and support system can be selected as candidates for the K-center, and then randomly selected as the initial center u. i .

[0042] 2. For each data point x that has not yet been selected as a center, calculate its distance to the nearest selected cluster center. For example, calculate the distance using the following formula:

[0043] in, This indicates that x is the closest known center u. i The distance between them; this distance can also be Euclidean distance, Manhattan distance, or other distance metrics suitable for measuring the similarity of data points.

[0044] 3. A new data point is randomly selected as the cluster center using a weighted probability distribution, where the probability of a data point x being selected as the new center is proportional to the square of its distance to the nearest selected cluster center. This strategy ensures that data points farther from the current center are more likely to be selected as new centers, thereby improving the efficiency and accuracy of initial center point selection.

[0045] Repeat steps 2 and 3 until K cluster centers (j = k) are selected. This process ensures that the K centers can more evenly cover the dataset, reducing the number of iterations and improving the stability and efficiency of clustering.

[0046] There are several ways to obtain target object attribute information within a preset range. For example: obtain log data associated with the target object; preprocess the log data and extract the target object's behavioral and hardware metrics, as well as scene and camera information associated with the target object; determine the target object's attribute information based on these metrics, along with the scene and camera information. This method can efficiently extract key information from massive amounts of log data, laying a solid foundation for subsequent behavioral feature profiling and target audience profiling, and is particularly suitable for big data analysis and intelligent monitoring systems.

[0047] The following is a specific example:

[0048] The smart community management platform's application layer primarily encompasses three categories: community management, community services, and community security. Since monitoring key personnel involves movement across different scenarios, the monitoring data scope should cover these diverse environments. Therefore, a log-based layered correlation mining strategy is necessary to ensure the integrity of the data analysis. For example, log services can be used to acquire log data related to target objects from the massive amounts of monitoring information managed by the smart community management platform. This log data can be preprocessed, and correlation mining can be performed to obtain target object behavioral indicators, target object hardware indicators, and related scene and camera information. Target object behavioral indicators should at least include correlation data related to conversations, shopping, and other behaviors with the current target object. Target object hardware indicators should at least include correlations with server CPU, memory, disk, processes, virtual machines, etc., traversed during data transmission. This can assist data analysis in complex scenarios by quickly locating target objects through hardware positioning and obtaining identification feature data from different angles. After merging the target object's behavioral metrics and hardware metrics, and tagging scene information (e.g., the location of the scene and building coordinates, conversation content ID, etc.) and camera information (e.g., camera IP, associated camera IP, etc.), the target object's attribute information is obtained.

[0049] The aforementioned target groups can be identified in the following ways: For example, identifying abnormal behaviors that deviate from normal activity patterns, such as loitering in the community late at night, frequently entering sensitive areas, or having unusual contact with specific groups of people. These individuals exhibiting abnormal behavior will be marked as target groups. Known individuals requiring close monitoring, such as those with special needs within the community (e.g., elderly people living alone, minors requiring guardianship), and residents who frequently report inappropriate behavior, will be pre-marked as target groups. In the event of emergencies, such as intrusions, fires, or medical emergencies, key figures or suspicious individuals will be automatically marked as target groups for rapid response and handling. Long-term tracking and analysis of certain types of activities or individuals, such as residents who frequently use community resources or active participants in community activities, will also mark these individuals as target groups. Suspicious packages, vehicles that have been stationary for extended periods, and unauthorized device access will also be considered as target groups.

[0050] Step S204: Determine a behavioral feature profile based on the target object's attribute information, wherein the behavioral feature profile is used to represent the attribute distribution of different behavioral features of the target object.

[0051] In the technical solution provided in step S204, there are multiple ways to determine the behavioral feature profile based on the target object attribute information. For example, it can be done in the following ways: clustering the target object behavior indicators in the target object attribute information to obtain multiple behavioral feature sets, wherein each behavioral feature set contains a behavioral feature and the target object classified to the behavioral feature; determining the attribute distribution of each behavioral feature set, wherein the attribute distribution is used to represent the probability distribution of the attribute of each behavioral feature in the corresponding behavioral feature set; and determining the behavioral feature profile based on the behavioral feature set and the attribute distribution of the behavioral feature set.

[0052] The following is a specific example:

[0053] First, the data in the target object behavior indicators is cleaned and standardized to ensure the validity and consistency of each record, laying the foundation for subsequent cluster analysis. From the preprocessed target object behavior indicators, specific behavior indicators are extracted and clustered using the K-means++ algorithm, selecting initial cluster centers (refer to the implementation method described above). The distance between each data point and the current cluster center is calculated, and new centers are selected based on a weighted probability distribution until K centers are determined. Iterative clustering is performed using the standard K-means algorithm until the clustering results converge, resulting in multiple behavior feature sets. Each behavior feature set contains a behavior feature and the target object classified under that behavior feature. For each behavior feature set, the attribute distribution of its behavior indicators is statistically analyzed. For example, the "frequent late-night outings" feature set may include attributes such as the time period distribution and location distribution of late-night activities. The probability distribution of each attribute in the behavior feature set is calculated, which will be used for subsequent behavior feature profile construction. Based on each behavior feature set and its attribute distribution, a behavior feature profile is constructed to intuitively describe the typical behavior patterns of the target object within that behavior feature set. A profile includes behavioral characteristics attributes (such as activity time, location, frequency, etc.), quantitative descriptions of these attributes, and the probability distribution of these attributes in the feature set. For example, the probability value of feature 'a' corresponding to attribute 'a' in the attribute distribution of the audience for that behavioral characteristic can also be determined using the following formula (the probability value of the corresponding attribute distribution in the behavioral characteristic profile).

[0054]

[0055] in, This indicates the value of the attribute corresponding to the person. This represents the probability value of the corresponding attribute distribution in the behavioral feature profile.

[0056] Suppose a smart community management platform focuses on the issue of "youth activity safety," targeting teenagers within the community. Here's a specific example: Assuming K=3, the K-means++ algorithm is used to cluster the quantified behavioral indicators, resulting in three behavioral feature sets: "After-School Activity," "Study Preference," and "Socially Intensive." Each feature set represents the behavioral patterns of teenagers being active in the community after school, preferring to study within the community (e.g., in the library), and frequently socializing within the community. For the "After-School Activity" feature set, the probability distribution of activity time, location preference, and frequency attributes within the "After-School Activity" feature set is analyzed. The highest probabilities are: 16:00-19:00, location preference: basketball court and park, and frequency: at least once every weekend.

[0057] Based on the above analysis, a profile of "active after school" behavior characteristics is constructed: teenagers frequently appear in community basketball courts and parks between 16:00 and 19:00 after school, frequently participate in sports activities, at least once every weekend.

[0058] Step S206: Determine the target audience profile based on the target object attribute information and behavioral feature profile, wherein the target audience profile is used to represent the association between the target object's behavioral features and the camera that collects the behavioral features.

[0059] In the technical solution provided in step S206, there are multiple ways to determine the target audience profile based on the target object attribute information and behavioral feature profile. For example, the target object hardware indicators and scene information and camera information associated with the target object are obtained from the target object attribute information; the behavioral feature profile is associated with the target object hardware indicators, scene information associated with the target object, and camera information to determine the hardware requirements of each behavioral feature set in the behavioral feature profile, wherein the hardware requirements are used to indicate the hardware resource usage and camera location of the target object under the corresponding behavioral features; and the target audience profile is determined based on the hardware requirements.

[0060] The following is a specific example:

[0061] The process involves acquiring hardware metrics for the target object, including the relationships between server CPU, memory, disk, processes, and virtual machines involved in data transmission. The behavioral profile obtained in step S206 is then correlated with the target object's hardware metrics, scene information, and camera information. Hardware requirements not only include device performance requirements but may also involve the layout and number of cameras in specific scenarios. Hardware requirements indicate the hardware resource usage and camera locations of the target object under corresponding behavioral characteristics. For example, considering the behavioral characteristic of teenagers being active after school, based on the behavioral profile indicating that teenagers frequently appear at community basketball courts and parks between 4:00 PM and 7:00 PM after school and frequently participate in sports activities, the hardware resource usage and camera locations at these courts and parks during this time period can be used as hardware requirements to obtain a target audience profile for teenagers exhibiting this behavioral characteristic. This target audience profile integrates the target object's attribute information, behavioral profile, and hardware requirements. The profile not only describes the target object's behavioral patterns but also includes hardware configuration information to meet the monitoring needs under these patterns, providing a basis for the smart community management platform to plan hardware resource allocation and optimize monitoring strategies.

[0062] Step S208: Determine the location of the camera based on the target audience profile.

[0063] In the technical solution provided in step S208, there are multiple ways to determine the location of the camera based on the target audience profile. For example, determining the first set of behavioral features corresponding to the first target object in the behavioral feature profile, wherein the first target object is any target object; determining the first hardware requirement corresponding to the first set of behavioral features in the target audience profile; and determining the location of the camera based on the first hardware requirement.

[0064] The following is a specific example:

[0065] When the primary target audience is teenagers, the corresponding primary behavioral characteristic set in the behavioral profile is "active after school." This "active after school" activity corresponds to the primary hardware requirement in the target audience profile: the usage of hardware resources in community basketball courts and parks during that time period, and the location of cameras. Therefore, the location of cameras can be directly determined based on this hardware requirement, enabling focused monitoring of existing cameras or deployment of new cameras. These cameras can support the RedCap protocol. In large-scale distributed scenarios, the large data volume and high requirements for transmission protocols make RedCap a popular application. Its "lightweight 5G" characteristics enable efficient transmission of service points and target information within the images. Service points refer to different requirements for images in different scenarios. For example, when camera data transmission images are used in security monitoring systems, the focus is on image clarity and resolution; therefore, service points include clarity and resolution.

[0066] When multiple cameras are monitoring overlapping scenes, the transmission nodes corresponding to the overlapping data of multiple cameras in the monitoring scene are determined; two adjacent transmission nodes are determined as a master node, and the master node manages the transmission data of the cameras corresponding to the two adjacent transmission nodes.

[0067] The following is a specific example:

[0068] The smart community management platform's application layer encompasses three main scenarios and sub-scenarios: community management, community services, and community security. These include video surveillance, access control, personnel and vehicle data at entrances and exits, and other information. Video data (including images) is provided and matched with IoT sensor data. If there is overlap between scenarios and sub-scenarios, a node shrinking algorithm merges adjacent nodes transmitting overlapping data into a new node to manage the data from two sets of cameras. This improves network utilization and enhances the timeliness of tracking key personnel.

[0069] Node shrinking is the process of shrinking a node and its neighbors into a new node. If v i This is a crucial core node; shrinking it allows the entire network to coalesce more effectively. The most typical example is a star network where shrinking the core node causes the entire network to coalesce into a single large node. For instance, in a social network, the easier it is to connect people (the smaller the average shortest path length d) and the fewer the number of people (the smaller the number of nodes n), the higher the network's cohesion. Therefore, the degree of network cohesion is defined as...

[0070]

[0071] Where, d ij Indicates v i With v j The shortest path length.j `for another not with v i For repeated nodes, when n=1, let the network cohesion be... Obviously The node shrinkage method primarily examines the change in network cohesion before and after node shrinkage, thereby determining the importance of nodes in the network. Therefore, a node v is defined. i The importance metric IMC(i) is:

[0072]

[0073] in, This indicates that node v i The cohesion of the network obtained after contraction. This can be further determined using the following formula:

[0074]

[0075] Where n represents the total number of nodes in the network, d(G) represents the average shortest path length of the original network (G), and k i Represents node v i The number of neighboring nodes, i.e., the number of nodes directly connected to v. i The number of connected nodes, d(G) --i ) indicates that network (G) removes node v i The average shortest path length after that.

[0076] In the node shrinking method, the importance of a node is determined by both the number of its neighbors and its position in the network path. Since the average path length of the network must be calculated each time a node is shrunk, the time complexity is relatively high. Therefore, the node shrinking method is not suitable for computing large-scale networks. However, this drawback does not apply to the network connection between the cameras and the platform in this application, which uses a dedicated network line.

[0077] It can also handle situations where the system is attacked: retrieve the target file stored in the database, where the target file is a file used to store the attribute information of the target object; after identifying that the target file has been tampered with, determine the attack path corresponding to the target file; mark the files that overlap with the attack path as isolated files, and mark the files that do not overlap with the attack path as non-isolated files; delete the isolated files, and feed back the processing results to the terminal.

[0078] For example, the following is a specific implementation:

[0079] First, after the target file is tampered with, when the ransomware process file path and related information are traced using Fanotify technology, the tracing scope is broadened by combining the network nodes included in the smart business chain before being sent to the web frontend. Fanotify is a file monitoring technology commonly used in antivirus software or for malicious access control by viruses. When the user receives an alert on the frontend indicating that a file has been encrypted and tampered with, along with the target file's path, they can choose to isolate or delete the target file. Simultaneously, the system compares the file with all nodes in the smart business chain, marking non-duplicated nodes as non-virus isolated to prevent accidental isolation or deletion of the target file. Then, the processing result is fed back. After isolating or deleting the virus file, the processing result is fed back to the frontend, informing the user whether the processing was successful.

[0080] Cost models can also be built to calculate the cost-benefit ratio and account for the network traffic-related costs of the tracked target. For example, if the total actual resources of the smart business chain nodes and links are 10G of traffic, and non-isolated files account for 60%, then the actual resource impact caused by security threats is 40%. Then, based on the main indicators of network threats, the specific cost of the threat can be further obtained from 40%.

[0081] From an infrastructure perspective, revenue is determined by the network's resource requirements, specifically node resource requirements and link resource requirements, mapping revenue I(G). v It is determined by the following formula:

[0082]

[0083] in, This is the computing power requirement value of virtual link j. This represents the computational power requirement of virtual node i, where α is the weighting factor, and N is the value of the virtual node i's computational power requirement. v L is a set of virtual nodes. v A collection of virtual links.

[0084] The overhead of mapping a virtual network is defined as the sum of the actual resources allocated to that virtual network by the underlying network. The mapping overhead C(G) v It is determined by the following formula:

[0085]

[0086] in, This is the computing power requirement value of virtual link j. This represents the computational power requirement of virtual node i, where α is a weighting factor. N represents the actual path length occupied on the underlying network after virtual link j is mapped. v L is a set of virtual nodes. vA collection of virtual links.

[0087] Once the definitions of mapping benefits and mapping costs are established, the benefit-cost ratio can be defined as the ratio of these two (mapping benefits / mapping costs).

[0088] Finally, the revenue-cost ratio is recalculated after node shrinking optimization to obtain a new revenue-cost ratio. If the new revenue-cost ratio is lower than the previous one, it is taken as the optimal revenue-cost ratio; otherwise, the previous one is taken as the optimal one. In this way, the revenue-cost ratio of network resources can be scientifically calculated based on the behavioral feature set of the target object. By continuously adjusting the resource allocation strategy through optimization algorithms, the efficient utilization of network resources can be ensured, while meeting monitoring efficiency and security requirements.

[0089] Through the above steps, the K-means++ algorithm is used to aggregate and classify data from different layers, clarifying data sources and collection channels to avoid duplicate collection. A log-based hierarchical correlation mining strategy is employed to ensure the integrity of data analysis. Furthermore, by constructing target audience profiles, the system can more quickly match the current target object to its category. These profiles enable analysis of the target object's situation, accurately identifying its behavioral patterns and hardware requirements, providing a scientific basis for the rational placement of cameras. This is particularly beneficial in large public places or scenarios requiring refined management, significantly improving monitoring efficiency and security.

[0090] This application provides a schematic diagram of the structure of a camera position determination device, as shown in the embodiment. Figure 3 As shown, it includes:

[0091] The acquisition module 302 is used to acquire target object attribute information within a preset range. The target object attribute information includes at least target object behavior indicators and target object hardware indicators. The target object behavior indicators are used to indicate the behavior information associated with the target object, and the target object hardware indicators are used to indicate the hardware information associated with the target object during data transmission.

[0092] The acquisition module 302 is also used to acquire all data within a preset range, wherein all data includes target object attribute information; determine the level to which all data belongs; cluster all data with each level as the cluster center, and determine the target data collected at each level.

[0093] The acquisition module 302 is also used to acquire log data associated with the target object; after preprocessing the log data, it extracts the target object behavior indicators and target object hardware indicators, as well as the scene information and camera information associated with the target object from the preprocessed log data; and determines the target object attribute information based on the target object behavior indicators, target object hardware indicators, scene information and camera information.

[0094] The first determining module 304 is used to determine a behavioral feature profile based on the target object's attribute information, wherein the behavioral feature profile is used to represent the attribute distribution of different behavioral features of the target object.

[0095] The first determining module 304 is further used to cluster the target object behavior indicators in the target object attribute information to obtain multiple behavior feature sets, wherein each behavior feature set contains a behavior feature and the target object classified to the behavior feature; determine the attribute distribution of each behavior feature set, wherein the attribute distribution is used to represent the probability distribution of the attribute of each behavior feature in the corresponding behavior feature set; and determine the behavior feature profile based on the behavior feature set and the attribute distribution of the behavior feature set.

[0096] The second determining module 306 is used to determine the target audience profile based on the target object attribute information and behavioral feature profile, wherein the target audience profile is used to represent the association between the behavioral features of the target object and the camera that collects the behavioral features.

[0097] The second determining module 306 is also used to obtain the target object's hardware indicators and the scene information and camera information associated with the target object from the target object attribute information; associate the behavioral feature profile with the target object's hardware indicators, the scene information and camera information associated with the target object, and determine the hardware requirements of each behavioral feature set in the behavioral feature profile, wherein the hardware requirements are used to indicate the hardware resource usage and camera location of the target object under the corresponding behavioral features; and determine the target audience profile based on the hardware requirements.

[0098] The third determining module 308 is used to determine the location of the camera based on the target audience profile.

[0099] The third determining module 308 is also used to determine the first behavioral feature set corresponding to the first target object in the behavioral feature profile, wherein the first target object is any target object; determine the first hardware requirement corresponding to the first behavioral feature set in the target audience profile; and determine the location of the camera based on the first hardware requirement.

[0100] The third determining module 308 is also used when there is overlap in the monitoring scenes corresponding to multiple cameras, to determine the transmission nodes corresponding to the overlapping data of multiple cameras in the monitoring scene; to determine two adjacent transmission nodes as a master node, and to manage the transmission data of the cameras corresponding to the two adjacent transmission nodes by the master node.

[0101] The third determination module 308 also needs to obtain the target file stored in the database, wherein the target file is a file used to store the attribute information of the target object; after identifying that the target file has been tampered with, determine the attack path corresponding to the target file; mark the files that overlap with the attack path as isolated files, and mark the files that do not overlap with the attack path as non-isolated files; delete the isolated files, and feed back the processing results to the terminal.

[0102] It should be noted that, Figure 3 The device shown for determining the camera position is used to perform... Figure 2 The method for determining the camera position shown is therefore... Figure 2 The explanations and descriptions in the method for determining the camera position also apply to the device for determining the camera position, and will not be repeated here.

[0103] It should be noted that the modules in the aforementioned camera position determination device can be program modules (e.g., a set of program instructions to implement a specific function) or hardware modules. For the latter, they can take the following forms, but are not limited to them: each of the above modules is represented by a processor, or the functions of each of the above modules are implemented by a processor.

[0104] This application embodiment also provides a non-volatile storage medium, which includes a stored program. During program execution, the device containing the non-volatile storage medium executes the above-described method for determining the camera location. For example, it acquires target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavioral indicators and target object hardware indicators. The target object behavioral indicators indicate behavioral information associated with the target object, and the target object hardware indicators indicate hardware information associated with the target object during data transmission. Based on the target object attribute information, it determines a behavioral feature profile, wherein the behavioral feature profile represents the attribute distribution of different behavioral features of the target object. Based on the target object attribute information and the behavioral feature profile, it determines a target audience profile, wherein the target audience profile represents the association between the target object's behavioral features and the camera that collects the behavioral features. Based on the target audience profile, it determines the location of the camera.

[0105] This application also provides an electronic device, including a processor for running a program, wherein the above-described method for determining the camera location is executed during program execution. For example, the method includes: acquiring target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavioral indicators and target object hardware indicators, the target object behavioral indicators indicating behavioral information associated with the target object, and the target object hardware indicators indicating hardware information associated with the target object during data transmission; determining a behavioral feature profile based on the target object attribute information, wherein the behavioral feature profile represents the attribute distribution of different behavioral features of the target object; determining a target audience profile based on the target object attribute information and the behavioral feature profile, wherein the target audience profile represents the association between the target object's behavioral features and the camera that collects the behavioral features; and determining the location of the camera based on the target audience profile.

[0106] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described method for determining the camera position. For example, it acquires target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavioral indicators and target object hardware indicators, the target object behavioral indicators indicating behavioral information associated with the target object, and the target object hardware indicators indicating hardware information associated with the target object during data transmission; determines a behavioral feature profile based on the target object attribute information, wherein the behavioral feature profile represents the attribute distribution of different behavioral features of the target object; determines a target audience profile based on the target object attribute information and the behavioral feature profile, wherein the target audience profile represents the association between the behavioral features of the target object and the camera that collects the behavioral features; and determines the location of the camera based on the target audience profile.

[0107] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0112] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the location of a camera, characterized in that, include: Obtain target object attribute information within a preset range, wherein the target object attribute information includes at least target object behavior indicators and target object hardware indicators, the target object behavior indicators are used to indicate behavior information associated with the target object, and the target object hardware indicators are used to indicate hardware information associated with the target object during data transmission; A behavioral feature profile is determined based on the target object attribute information, wherein the behavioral feature profile is used to represent the attribute distribution of different behavioral features of the target object; A target audience profile is determined based on the target object attribute information and the behavioral feature profile, wherein the target audience profile is used to represent the association between the behavioral features of the target object and the camera that collects the behavioral features; The location of the camera is determined based on the target audience profile.

2. The method according to claim 1, characterized in that, Before obtaining the target object attribute information within a preset range, the method further includes: Obtain all data within the preset range, wherein all data includes the target object attribute information; Determine the level to which all the data belongs; Cluster all the data using each level as the cluster center to determine the target data collected at each level.

3. The method according to claim 1, characterized in that, Retrieve target object attribute information within a preset range, including: Obtain log data associated with the target object; After preprocessing the log data, the target object's behavior metrics, target object's hardware metrics, scene information associated with the target object, and camera information are extracted from the preprocessed log data. The target object attribute information is determined based on the target object behavior indicators, the target object hardware indicators, the scene information, and the camera information.

4. The method according to claim 1, characterized in that, Determining a behavioral feature profile based on the target object's attribute information includes: Clustering the target object behavior indicators in the target object attribute information yields multiple behavior feature sets, wherein each behavior feature set contains a behavior feature and a target object assigned to the behavior feature; Determine the attribute distribution for each behavioral feature set, wherein the attribute distribution represents the probability distribution of the attributes of each behavioral feature in the corresponding behavioral feature set; The behavioral feature profile is determined based on the behavioral feature set and the attribute distribution of the behavioral feature set.

5. The method according to claim 3, characterized in that, Determining a target audience profile based on the target object attribute information and the behavioral feature profile includes: Obtain the target object's hardware specifications, scene information, and camera information associated with the target object from the target object's attribute information; The behavioral feature profile is associated with the hardware indicators of the target object, the scene information associated with the target object, and the camera information to determine the hardware requirements of each behavioral feature set in the behavioral feature profile. The hardware requirements are used to indicate the hardware resource usage and camera location of the target object under the corresponding behavioral features. The target audience profile is determined based on the aforementioned hardware requirements.

6. The method according to claim 1, characterized in that, Determining the location of the camera based on the target audience profile includes: Determine a first set of behavioral features corresponding to the first target object in the behavioral feature profile, wherein the first target object is any one of the target objects; Determine the first hardware requirement corresponding to the first behavioral feature set in the target audience profile; The location of the camera is determined based on the first hardware requirement.

7. The method according to claim 1, characterized in that, The method further includes: In cases where the monitoring scenes corresponding to multiple cameras overlap, the transmission nodes corresponding to the overlapping data of the multiple cameras in the monitoring scenes are determined. Two adjacent transmission nodes are designated as a master node, which manages the transmission data of the cameras corresponding to the two adjacent transmission nodes.

8. The method according to claim 1, characterized in that, The method further includes: Obtain the target file stored in the database, wherein the target file is a file used to store the attribute information of the target object; After identifying that the target file has been tampered with, the attack path corresponding to the target file is determined; Files that overlap with the attack path are marked as isolated files, and files that do not overlap with the attack path are marked as non-isolated files. Delete the isolated file and send the processing result back to the terminal.

9. A device for determining the position of a camera, characterized in that, include: The acquisition module is used to acquire target object attribute information within a preset range. The target object attribute information includes at least target object behavior indicators and target object hardware indicators. The target object behavior indicators are used to indicate behavior information associated with the target object, and the target object hardware indicators are used to indicate hardware information associated with the target object during data transmission. The first determining module is used to determine a behavioral feature profile based on the target object attribute information, wherein the behavioral feature profile is used to represent the attribute distribution of different behavioral features of the target object; The second determining module is used to determine a target audience profile based on the target object attribute information and the behavioral feature profile, wherein the target audience profile is used to represent the association between the behavioral features of the target object and the camera that collects the behavioral features; The third determining module is used to determine the location of the camera based on the target audience profile.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device containing the non-volatile storage medium to execute the camera position determination method according to any one of claims 1 to 8.

11. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the method for determining the camera position as described in any one of claims 1 to 8.

12. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method for determining the camera position as described in any one of claims 1 to 8.

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