Intelligent data analysis method and system based on distributed radar network

Abstract

The application relates to the technical field of digital signal processing, and provides an intelligent data analysis method and system based on a distributed radar network. The intelligent data analysis method based on the distributed radar network comprises the following steps: acquiring working parameters of all radars at present based on a sensor interface; preprocessing the working parameters to generate attribute parameters; determining an utility function representing a radar working effect based on the attribute parameters, determining a dynamic selection strategy according to the utility function, and constructing a radar network topology to collect radar signals; encrypting the radar signals through a blockchain technology to generate encrypted data; and transmitting the encrypted data to a cloud server. Through the construction of the radar network topology, the scheme can adapt to different application scenarios and requirements, improve the flexibility and scalability of the radar network, enhance the data transmission efficiency and security, and realize real-time acquisition, safe encryption and automatic transmission of radar network data.

Description

Technical Field

[0001] This application relates to the field of digital signal processing technology, and more specifically, to an intelligent data analysis method and system based on a distributed radar network. Background Technology

[0002] Intelligent data analysis methods based on distributed radar networks have shown broad application prospects in many fields. For example, distributed radar networks are widely used in meteorological observation and geological exploration. Distributed radar networks can provide high-precision and real-time observation data, which helps to reveal the nature and laws of natural phenomena. Distributed radar networks can also be applied to the field of traffic management. Distributed radar networks can monitor traffic flow, vehicle speed and vehicle type in real time, providing accurate data support for traffic management.

[0003] In practical applications, the radars in a distributed radar network may be deployed in different locations, facing varying environmental conditions and differences in the performance of data acquisition equipment. The massive amount of data generated by distributed radar networks places higher demands on data processing and transmission capabilities. Current data processing algorithms and transmission technologies may struggle to meet the requirements for real-time performance and accuracy, leading to increased data processing and transmission latency. Summary of the Invention

[0004] This application provides an intelligent data analysis method and system based on distributed radar networks, which can at least partially solve the problems of radar network data processing and transmission delay.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of this application, an intelligent data analysis method based on a distributed radar network is provided, comprising: acquiring current operating parameters of each radar based on a sensor interface; preprocessing the operating parameters to generate attribute parameters; determining a utility function representing the radar's operating effect based on the attribute parameters; determining a dynamic selection strategy based on the utility function to construct a radar network topology for collecting radar signals; encrypting the radar signals using blockchain technology to generate encrypted data; and transmitting the encrypted data to a cloud server.

[0007] In this application, based on the aforementioned scheme, the preprocessing of the working parameters to generate attribute parameters includes: performing noise reduction and standardization processing on the working parameters to generate standard data; and updating the status of the standard data to generate attribute parameters.

[0008] In this application, based on the aforementioned scheme, the step of updating the state of the standard data and generating attribute parameters includes: classifying the multidimensional standard data according to data type to generate a data classification matrix; generating a corresponding state function based on the data classification matrix; and updating the state of the standard data based on the data classification matrix and the state function to generate attribute parameters.

[0009] In this application, based on the aforementioned scheme, the step of determining a utility function representing the radar's operational effectiveness based on the attribute parameters, and determining a dynamic selection strategy based on the utility function to construct a radar network topology for collecting radar signals, includes: generating a utility function based on standard data and a data classification matrix corresponding to the radar, wherein the utility function is used to measure the effectiveness produced by the radar during operation; determining the selection probability of each radar based on the utility function and generating a dynamic selection strategy; determining a target radar based on the dynamic selection strategy, broadcasting the target radar across the entire network to construct a radar network topology among radars in the entire network, and collecting radar signals through the radar network topology.

[0010] In this application, based on the aforementioned scheme, the step of encrypting the radar signal using blockchain technology to generate encrypted data includes: hashing and encapsulating the radar signal to generate a transaction block as the encrypted data.

[0011] In this application, based on the aforementioned scheme, after hashing and encrypting the radar signal and encapsulating the data to generate a transaction block as the encrypted data, the method further includes: broadcasting the transaction block in the radar network topology; each radar verifies the received transaction block through a consensus mechanism, and stores the transaction block on the blockchain after successful verification.

[0012] In this application, based on the aforementioned scheme, transmitting the encrypted data to the cloud server includes: pre-setting trigger conditions and transmission rules in a smart contract of the blockchain; and transmitting the encrypted data to the cloud server based on the trigger conditions and transmission rules.

[0013] According to one aspect of this application, an intelligent data analysis system based on a distributed radar network is provided, comprising:

[0014] The acquisition unit is used to acquire the current operating parameters of each radar based on the sensor interface;

[0015] The attribute unit is used to preprocess the working parameters and generate attribute parameters;

[0016] A topology unit is used to determine a utility function representing the radar's effectiveness based on the attribute parameters, and to determine a dynamic selection strategy based on the utility function in order to construct a radar network topology to collect radar signals.

[0017] An encryption unit is used to encrypt the radar signal using blockchain technology to generate encrypted data;

[0018] A transmission unit is used to transmit the encrypted data to a cloud server.

[0019] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the intelligent data analysis method based on a distributed radar network as described in the above embodiments.

[0020] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the intelligent data analysis method based on a distributed radar network as described in the above embodiments.

[0021] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the intelligent data analysis method based on a distributed radar network provided in the various optional implementations described above.

[0022] In this application's technical solution, the operating parameters of each radar are acquired based on the sensor interface; the operating parameters are preprocessed to generate attribute parameters; a utility function representing the radar's operating effect is determined based on the attribute parameters; a dynamic selection strategy is determined based on the utility function to construct a radar network topology for collecting radar signals; the radar signals are encrypted using blockchain technology to generate encrypted data; and the encrypted data is transmitted to a cloud server. By constructing a radar network topology, different application scenarios and needs can be adapted, improving the flexibility and scalability of the radar network. Simultaneously, heterogeneous data collected by various types of radars can be collected in real time based on the radar network topology and encrypted at the radar side, allowing the cloud server to directly obtain all the encrypted data. This avoids data delays and data leaks that may occur when transmitting data point-to-point between radars, enhancing data transmission efficiency and security, and realizing real-time acquisition, secure encryption, and automated transmission of radar network data.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0025] Figure 1 The flowchart illustrating an embodiment of the intelligent data analysis method based on a distributed radar network is shown in the illustration.

[0026] Figure 2 The flowchart illustrating the construction of a radar network topology in one embodiment of this application is shown schematically.

[0027] Figure 3 The illustration shows a schematic diagram of an intelligent data analysis system based on a distributed radar network in one embodiment of this application.

[0028] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0029] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0030] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0033] The implementation details of the technical solution of this application are described below:

[0034] Figure 1 A flowchart illustrating an embodiment of an intelligent data analysis method based on a distributed radar network according to this application is shown. (Refer to...) Figure 1 As shown, this intelligent data analysis method based on distributed radar networks includes at least steps S110 to S150, which are described in detail below:

[0035] In step S110, the current operating parameters of each radar are obtained based on the sensor interface.

[0036] In one embodiment of this application, IoT technology and sensor interfaces are used to automatically collect real-time operating parameters from different geographical locations and different radar models. These operating parameters include, but are not limited to, frequency, power, beamwidth, and scanning speed.

[0037] Specifically, frequency refers to the number of periodic changes per unit of time. In radar systems, frequency determines the wavelength of the electromagnetic waves emitted by the radar, thus affecting the radar's detection range, resolution, and target identification capability. Higher frequency radar waves have shorter wavelengths, providing higher resolution and more accurate measurements. However, higher frequency radar waves are also more susceptible to atmospheric attenuation and obstacles. Therefore, when selecting a radar frequency, a trade-off must be made between factors such as resolution, detection range, and anti-jamming capability.

[0038] Power refers to the work done per unit time. In radar systems, power determines the intensity of electromagnetic waves emitted by the radar, which in turn affects the radar's detection range and anti-jamming capability.

[0039] Beamwidth refers to the width of the electromagnetic wave beam emitted or received by a radar antenna in a specific direction, usually measured in degrees. Beamwidth determines the radar's detection range and target resolution. Narrow beams offer higher target resolution and more accurate measurements, but have a relatively smaller detection range. Conversely, wide beams cover a wider area, but have lower target resolution. Therefore, a trade-off must be made based on the application scenario and target characteristics when selecting beamwidth.

[0040] Scanning speed refers to the rate at which a radar antenna scans in a specific direction. Scanning speed determines the radar system's ability to detect and track targets. A high scanning speed can quickly cover a wider area, improving the radar system's response speed and tracking capabilities. However, excessively fast scanning speeds can lead to reduced target resolution and decreased measurement accuracy.

[0041] In summary, frequency, power, beamwidth, and scan rate are crucial parameters describing radar performance and operational characteristics. When designing and using radar systems, it is essential to select and configure them appropriately based on the application scenario and target characteristics to achieve optimal detection results and performance.

[0042] The above process, by acquiring the current operating parameters of each radar through the sensor interface, ensures the real-time nature and accuracy of the data. The sensor interface, acting as a bridge between the radar and the outside world, can capture the radar's operating status in real time, providing a raw and reliable data source for subsequent data processing.

[0043] In step S120, the working parameters are preprocessed to generate attribute parameters.

[0044] After obtaining the working parameters, predefined algorithms and rules are used to clean, transform, and standardize the raw data (i.e., the obtained working parameters) to eliminate noise, fill in missing values, and standardize the data format. Specifically, this includes data deduplication, outlier detection and handling, data type conversion, and data normalization or standardization. Through this series of preprocessing steps, the raw data is transformed into more structured, standardized, and meaningful attribute parameters, providing an accurate and reliable data foundation for subsequent intelligent data analysis.

[0045] In one embodiment of this application, the working parameters are preprocessed to generate attribute parameters, including:

[0046] The operating parameters are denoised and standardized to generate standard data;

[0047] The standard data is updated to reflect its status, and attribute parameters are generated.

[0048] In one embodiment of this application, machine learning algorithms can be used to clean, denoise, and standardize the collected raw parameters (i.e., operating parameters) to ensure data accuracy and consistency. Then, data fusion technology is used to integrate multi-source data to form a more comprehensive set of radar operating parameters.

[0049] Specifically, noise reduction can be achieved using methods such as frequency domain filtering, time domain filtering, or spatial filtering. Frequency domain filtering removes noise components by designing specific frequency response filters; for example, a low-pass filter can truncate high-frequency signal components to remove noise. Time domain filtering focuses on smoothing the signal over time, such as mean filtering and median filtering, suppressing noise by averaging or taking the median value over the time series. Spatial filtering removes noise signals by setting appropriate spatial filters to filter the radar signal.

[0050] The main purpose of standardization is to convert radar data into a uniform standard format or range for subsequent analysis and processing. When performing standardization, the data is scaled to a specific range (such as 0 to 1, or -1 to 1), which helps in processing signals of different magnitudes, making subsequent algorithms more stable and efficient.

[0051] The above process, through denoising and standardization, eliminates noise and redundancy in the original data (i.e., operating parameters), making it more standardized and uniform. Simultaneously, updating the standard data to generate attribute parameters further reflects the radar's real-time operating status and performance, providing more accurate data support for subsequent intelligent data analysis.

[0052] In one embodiment of this application, the standard data is updated to generate attribute parameters, including:

[0053] For multidimensional standard data, classify it according to data type and generate a data classification matrix;

[0054] Based on the data classification matrix, generate its corresponding state function;

[0055] The standard data is updated based on the data classification matrix and state function to generate attribute parameters.

[0056] After generating standard data through the above process, the standard data is updated in state. Specifically, during the generation of attribute parameters, the following steps are performed:

[0057] For multidimensional standard data, it is classified according to the data type of the parameters, generating a data classification matrix as follows:

[0058] R = [f1(D), f2(R), ..., f i (D),…,f n (D)]

[0059] Where R represents the data classification matrix, f i Let represent the classification function corresponding to the i-th data type, D represent the standard data, and n represent the dimension of the data classification matrix.

[0060] Based on the data classification matrix, its corresponding state function is generated as follows:

[0061]

[0062] in, Represents the state function. The multidimensional vector formed by the element values ​​in the data classification matrix R is represented by μ, which is the mean vector of the preset standard state, and X is the covariance matrix of the standard state. In this embodiment, the standard state represents the optimal state of radar operation; n represents the dimension of the data classification matrix, T represents the transpose operation, e represents the natural constant, and π represents pi.

[0063] Subsequently, the standard data is updated based on the data classification matrix and state function to generate attribute parameters R. ′ for:

[0064]

[0065] The above process calculates attribute parameters to measure the real-time operating status and performance of each radar, providing more accurate data support for subsequent radar network topology construction and data transmission.

[0066] In step S130, a utility function representing the radar's working effect is determined based on the attribute parameters, and a dynamic selection strategy is determined based on the utility function to construct a radar network topology to collect radar signals.

[0067] This scheme automatically generates a dynamic selection strategy based on the preprocessed attribute parameters. It comprehensively considers the radar network's coverage, signal quality, communication efficiency, and the specific attributes of each radar (such as location and performance) to dynamically determine which radars (the selected radars are called target radars) should be activated and participate in data collection. Subsequently, the radar network topology is constructed according to this strategy to ensure that the radars in the network can cooperate optimally. Once the radar network topology is complete, the selected target radars begin collecting signals, ensuring that the collected radar signals are both comprehensive and accurate, providing a solid foundation for subsequent data analysis.

[0068] like Figure 2As shown, in one embodiment of this application, a utility function representing the radar's performance is determined based on the attribute parameters, and a dynamic selection strategy is determined according to the utility function to construct a radar network topology for collecting radar signals, including:

[0069] S210, Based on the standard data and data classification matrix corresponding to the radar, a utility function is generated, which is used to measure the effect produced by the radar in operation;

[0070] S220, Based on the utility function, determine the selection probability of each radar and generate a dynamic selection strategy;

[0071] S230, according to the dynamic selection strategy, the target radar is determined, and the target radar is broadcast to the entire network to construct a radar network topology among the radars in the entire network, and radar signals are collected through the radar network topology.

[0072] In one embodiment of this application, a utility function is generated based on the standard data and data classification matrix corresponding to the radar. The utility function is as follows:

[0073] u(x)=g(x,D)

[0074] Where u(x) represents the calculated utility value, g() represents the utility function processing, x represents the element value in the data classification matrix, and D represents the standard data. In this embodiment, the utility value is used to measure the performance of a radar under the current operating parameters.

[0075] Then, based on the utility function, the selection probability of each radar is determined as follows:

[0076]

[0077] Where ε represents the utility factor obtained from training based on historical data, A represents the set of element values ​​in the data classification matrix, and x and x ′ This represents the values ​​of adjacent elements in the data classification matrix.

[0078] The selection probability is obtained through the above calculations. A higher selection probability indicates a higher probability that the radar will be selected as the target radar. Topology reconstruction is performed within a preset time after the target radar fails, and this is used to generate a dynamic selection strategy. According to the dynamic selection strategy, the radar with the highest selection probability is determined as the target radar. In this embodiment, the target radar serves as the main transmission relay, transmitting data to the cloud server.

[0079] After the target radar is identified, an automatic self-organizing network reconstruction process is initiated among the remaining radars in the network. By analyzing the location, performance, and communication capabilities of the other radars in the network, these radars are automatically selected and connected to form a new radar network topology. During this process, the network structure can be dynamically adjusted to ensure that the communication links between the radars are stable and efficient, and can cover the target area, thereby generating a radar network topology that meets practical application requirements and possesses high flexibility and scalability.

[0080] Specifically, in this embodiment, the radar network topology consists of multiple radars operating in different frequency bands, modes, and polarizations. These radars are strategically deployed within a specific area to achieve comprehensive coverage. Each radar possesses independent detection capabilities and can transmit detected target information to target radars within the network topology for unified processing. The target radars are responsible for collecting, integrating, and processing information from each radar. The received information undergoes fusion processing to eliminate redundant information and improve accuracy and reliability. Through fusion processing, a more complete and accurate description of the target can be formed, including its position, speed, and shape. The radar network topology enables comprehensive target coverage and continuous detection, improving detection accuracy and reliability. Through data fusion and collaborative detection, blind spots of individual radars can be eliminated, enhancing overall detection capabilities. The radar network topology possesses high anti-jamming and anti-destruction capabilities, ensuring stable operation in complex environments.

[0081] The above process generates a dynamic selection strategy based on attribute parameters and constructs the radar network topology, achieving intelligent management of the radar network. By constructing a utility function and a dynamic selection strategy, the optimal target radar can be automatically selected to participate in data collection, ensuring the efficient operation of the radar network. Simultaneously, the constructed radar network topology can adapt to different application scenarios and requirements, improving the flexibility and scalability of the radar network.

[0082] In step S140, the radar signal is encrypted using blockchain technology to generate encrypted data.

[0083] In one embodiment of this application, the radar signals are hashed, encrypted, and encapsulated to generate transaction blocks, which serve as the encrypted data. In this embodiment, after each radar collects radar signals, it first formats the signal data to ensure data consistency and readability.

[0084] Data can also be hashed and encrypted to increase security. After formatting and encrypting the data, transaction blocks are generated, which are then encapsulated into blockchain transactions. The encapsulation process can involve converting the data into a specific data structure and attaching necessary metadata (such as timestamps, radar identifiers, etc.).

[0085] The above process encrypts radar signals using blockchain technology to generate encrypted data, ensuring data security and privacy protection. Blockchain technology, with its decentralized and immutable characteristics, provides strong protection for data encryption. Through hash encryption and data encapsulation, the generated transaction blocks ensure the security and integrity of radar signals during transmission, preventing data leakage and unauthorized access.

[0086] In one embodiment of this application, the radar signal is hash-encrypted and data-encapsulated to generate a transaction block, which, after being used as the encrypted data, further includes:

[0087] The transaction block is broadcast in the radar network topology;

[0088] Each radar verifies the received transaction block through a consensus mechanism, and stores the transaction block on the blockchain after successful verification.

[0089] In one embodiment of this application, the encapsulated transaction block is broadcast throughout the entire radar network topology so that other radars can receive and verify this data. Specifically, a blockchain consensus algorithm is used to ensure the validity of transactions and the security of the network. In the proof-of-stake mechanism, each radar is selected to verify transactions based on the number of tokens it holds. The selected radar is responsible for creating new blocks and adding them to the blockchain. In the delegated proof-of-stake mechanism, radars elect a certain number of witnesses through voting to be responsible for transaction verification and block creation.

[0090] In this embodiment, the radar is equipped with a data processing module and a verification module. After receiving a transaction block, the radar verifies the received transaction through a consensus mechanism. This includes checking the transaction's format, signature, timestamp, etc., to ensure the transaction's legality and authenticity. If the transaction is verified as valid, it will be added to the blockchain and become part of the network consensus.

[0091] The process described above involves broadcasting transaction blocks within the radar network topology and verifying and storing them through a consensus mechanism. This step further enhances data security and reliability. Each radar verifies the transaction blocks through the consensus mechanism, ensuring the authenticity and validity of the data. Simultaneously, storing verified transaction blocks on the blockchain achieves data persistence and traceability.

[0092] In step S150, the encrypted data is transmitted to the cloud server.

[0093] In this embodiment, the encrypted data is encapsulated to ensure that its format meets the requirements for network transmission. A suitable network transmission protocol and data transmission method (such as synchronous or asynchronous transmission) are selected to securely transmit the data from the local computer or target radar to the cloud server.

[0094] During transmission, the system monitors the progress and status of data transmission to ensure that the data arrives at the cloud intact and without errors. Once the data is successfully transmitted to the cloud server, the system records the completion status of the transmission and may trigger further processing on the cloud server, such as data decryption, storage, or analysis. This process ensures the security and integrity of encrypted data during transmission and also provides reliable data input for subsequent processing on the cloud server.

[0095] In one embodiment of this application, transmitting the encrypted data to a cloud server includes:

[0096] Triggering conditions and transmission rules are pre-defined in the smart contracts of the blockchain;

[0097] Based on the aforementioned triggering conditions and transmission rules, the encrypted data is transmitted to the cloud server.

[0098] In one embodiment of this application, verified encrypted data can be transmitted to a designated target radar or cloud server via a smart contract. This is specifically achieved by setting trigger conditions and transmission rules within the smart contract. During transmission, the data remains encrypted to ensure security.

[0099] For example, triggering conditions may include a signal strength threshold, target movement speed, or a time window. For instance, when the signal strength received by the radar exceeds a preset threshold, it is considered a potential target. This threshold can be adjusted based on factors such as ambient noise levels and radar performance. Through continuous signal analysis, the smart contract can calculate the target's movement speed. If the target's movement speed exceeds a preset speed threshold, further analysis is triggered. A time window can also be set to limit the effective duration of the triggering condition. For example, the triggering condition may be more sensitive at night or during specific time periods because illegal border crossings may be more frequent.

[0100] For example, during transmission, the collected radar signal data is encapsulated into a specific format, including information such as the target's position, speed, and signal strength. According to preset rules, the smart contract selects which target radar to transmit the data to. To ensure data security, the smart contract encrypts the data before transmission. Only radars with the corresponding decryption key can decrypt and read the data. The smart contract also records the data transmission status and confirms successful transmission to the target radar.

[0101] By defining specific triggering conditions and transmission rules through smart contracts, radar monitoring systems can automatically process and analyze signal data, promptly detecting and responding to potential anomalies. This automation not only improves the system's response speed but also reduces the risk of human intervention, thereby ensuring data integrity and reliability and enhancing the efficiency and accuracy of radar monitoring.

[0102] The process described above involves transmitting encrypted data to a cloud server, with trigger conditions and transmission rules pre-defined in a blockchain smart contract. This step automates data transmission and storage. The smart contract allows for automatic data transmission, reducing human intervention and errors. Simultaneously, transmitting data to a cloud server enables centralized management and analysis, providing data support for subsequent intelligent decision-making.

[0103] In this application's technical solution, the operating parameters of each radar are acquired based on a sensor interface; these operating parameters are preprocessed to generate attribute parameters; a utility function representing the radar's performance is determined based on the attribute parameters; a dynamic selection strategy is determined according to the utility function to construct a radar network topology for collecting radar signals; the radar signals are encrypted using blockchain technology to generate encrypted data; and the encrypted data is transmitted to a cloud server. By constructing a radar network topology, different application scenarios and needs can be adapted, improving the flexibility and scalability of the radar network, enhancing data transmission efficiency and security, and realizing real-time acquisition, secure encryption, and automated transmission of radar network data.

[0104] The following describes an apparatus embodiment of this application, which can be used to execute the intelligent data analysis method based on a distributed radar network as described in the above embodiments of this application. It is understood that the apparatus can be a computer program (including program code) running on a computer device, for example, the apparatus is application software; the apparatus can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the intelligent data analysis method based on a distributed radar network described above.

[0105] Figure 3A block diagram of an intelligent data analysis system based on a distributed radar network according to an embodiment of this application is shown.

[0106] Reference Figure 3 As shown, an intelligent data analysis system based on a distributed radar network according to an embodiment of this application includes:

[0107] Acquisition unit 310 is used to acquire the current operating parameters of each radar based on the sensor interface;

[0108] The attribute unit 320 is used to preprocess the working parameters to generate attribute parameters;

[0109] Topology unit 330 is used to determine a utility function representing the radar's working effect based on the attribute parameters, and to determine a dynamic selection strategy based on the utility function in order to construct a radar network topology to collect radar signals.

[0110] The encryption unit 340 is used to encrypt the radar signal using blockchain technology to generate encrypted data;

[0111] The transmission unit 350 is used to transmit the encrypted data to the cloud server.

[0112] In this application, based on the aforementioned scheme, the preprocessing of the working parameters to generate attribute parameters includes: performing noise reduction and standardization processing on the working parameters to generate standard data; and updating the status of the standard data to generate attribute parameters.

[0113] In this application, based on the aforementioned scheme, the step of updating the state of the standard data and generating attribute parameters includes: classifying the multidimensional standard data according to data type to generate a data classification matrix; generating a corresponding state function based on the data classification matrix; and updating the state of the standard data based on the data classification matrix and the state function to generate attribute parameters.

[0114] In this application, based on the aforementioned scheme, the step of determining a utility function representing the radar's operational effectiveness based on the attribute parameters, and determining a dynamic selection strategy based on the utility function to construct a radar network topology for collecting radar signals, includes: generating a utility function based on standard data and a data classification matrix corresponding to the radar, wherein the utility function is used to measure the effectiveness produced by the radar during operation; determining the selection probability of each radar based on the utility function and generating a dynamic selection strategy; determining a target radar based on the dynamic selection strategy, broadcasting the target radar across the entire network to construct a radar network topology among radars in the entire network, and collecting radar signals through the radar network topology.

[0115] In this application, based on the aforementioned scheme, the step of encrypting the radar signal using blockchain technology to generate encrypted data includes: hashing and encapsulating the radar signal to generate a transaction block as the encrypted data.

[0116] In this application, based on the aforementioned scheme, after hashing and encrypting the radar signal and encapsulating the data to generate a transaction block as the encrypted data, the method further includes: broadcasting the transaction block in the radar network topology; each radar verifies the received transaction block through a consensus mechanism, and stores the transaction block on the blockchain after successful verification.

[0117] In this application, based on the aforementioned scheme, transmitting the encrypted data to the cloud server includes: pre-setting trigger conditions and transmission rules in a smart contract of the blockchain; and transmitting the encrypted data to the cloud server based on the trigger conditions and transmission rules.

[0118] In this application's technical solution, the operating parameters of each radar are acquired based on a sensor interface; these operating parameters are preprocessed to generate attribute parameters; a utility function representing the radar's performance is determined based on the attribute parameters; a dynamic selection strategy is determined according to the utility function to construct a radar network topology for collecting radar signals; the radar signals are encrypted using blockchain technology to generate encrypted data; and the encrypted data is transmitted to a cloud server. By constructing a radar network topology, different application scenarios and needs can be adapted, improving the flexibility and scalability of the radar network, enhancing data transmission efficiency and security, and realizing real-time acquisition, secure encryption, and automated transmission of radar network data.

[0119] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0120] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0121] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in the read-only memory 402 or a program loaded from the storage section 408 into the random access memory 403, such as executing the intelligent data analysis method based on a distributed radar network described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0122] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0123] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0124] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0127] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0128] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the intelligent data analysis method based on a distributed radar network as described in the above embodiments.

[0129] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0130] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0131] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0132] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A smart data analysis method based on a distributed radar network, characterized in that, include: Obtain the current operating parameters of each radar based on the sensor interface; The working parameters are preprocessed to generate attribute parameters; A dynamic selection strategy is generated based on the attribute parameters, and a radar network topology is constructed to collect radar signals within the radar network topology. The radar signal is encrypted using blockchain technology to generate encrypted data; The encrypted data is transmitted to the cloud server; The step of preprocessing the working parameters to generate attribute parameters includes: The operating parameters are denoised and standardized to generate standard data; The standard data is updated to reflect its status, generating attribute parameters. The step of updating the status of the standard data and generating attribute parameters includes: For multidimensional standard data, it is classified according to data type, generating a data classification matrix as follows: in, R Represents a data classification matrix. Indicates the first i There are classification functions for each data type, where D represents the standard data. n The dimension of the data classification matrix; Based on the data classification matrix, its corresponding state function is generated as follows: in, Represents the state function. Represents the data classification matrix R A multidimensional vector composed of the element values ​​in the vector. It is the mean vector of the preset standard state. X It is the covariance matrix of the standard state; The standard data is updated based on the data classification matrix and state function to generate attribute parameters.

2. The intelligent data analysis method based on a distributed radar network according to claim 1, characterized in that, The step of generating a dynamic selection strategy based on the attribute parameters and constructing the radar network topology includes: Based on the standard data and data classification matrix corresponding to the radar, a utility function is generated, which is used to measure the effect produced by the radar in operation; Based on the utility function, the selection probability of each radar is determined, and a dynamic selection strategy is generated. Based on the dynamic selection strategy, the target radar is determined and the radar network topology is constructed.

3. The intelligent data analysis method based on a distributed radar network according to claim 1, characterized in that, The step of encrypting the radar signal using blockchain technology to generate encrypted data includes: The radar signal is hashed, encrypted, and encapsulated to generate a transaction block, which serves as the encrypted data.

4. The intelligent data analysis method based on a distributed radar network according to claim 3, characterized in that, After hashing and encrypting the radar signal and encapsulating it to generate a transaction block as the encrypted data, the process further includes: The transaction block is broadcast in the radar network topology; Each radar verifies the received transaction block through a consensus mechanism, and stores the transaction block on the blockchain after successful verification.

5. The intelligent data analysis method based on a distributed radar network according to claim 1, characterized in that, The step of transmitting the encrypted data to the cloud server includes: Triggering conditions and transmission rules are pre-defined in the smart contracts of the blockchain; Based on the aforementioned triggering conditions and transmission rules, the encrypted data is transmitted to the cloud server.

6. An intelligent data analysis system based on a distributed radar network, characterized in that, include: The acquisition unit is used to acquire the current operating parameters of each radar based on the sensor interface; The attribute unit is used to preprocess the working parameters and generate attribute parameters; A topology unit is used to generate a dynamic selection strategy based on the attribute parameters and construct a radar network topology to collect radar signals within the radar network topology. An encryption unit is used to encrypt the radar signal using blockchain technology to generate encrypted data; A transmission unit is used to transmit the encrypted data to a cloud server; The step of preprocessing the working parameters to generate attribute parameters includes: The operating parameters are denoised and standardized to generate standard data; The standard data is updated to reflect its status, generating attribute parameters. The step of updating the status of the standard data and generating attribute parameters includes: For multidimensional standard data, it is classified according to data type, generating a data classification matrix as follows: in, R Represents a data classification matrix. Indicates the first i There are classification functions for each data type, where D represents the standard data. n The dimension of the data classification matrix; Based on the data classification matrix, its corresponding state function is generated as follows: in, Represents the state function. Represents the data classification matrix R A multidimensional vector composed of the element values ​​in the vector. It is the mean vector of the preset standard state. X It is the covariance matrix of the standard state; The standard data is updated based on the data classification matrix and state function to generate attribute parameters.

Images