Online frequency selection data transmission method and system and online frequency selection platform

Abstract

The invention discloses an online frequency selection data transmission method and system and an online frequency selection platform, and relates to the field of data transmission, and the method comprises the steps: collecting multi-band electromagnetic radiation data; performing environmental interference compensation on the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set; generating a data table according to the corrected electromagnetic radiation data set, and dispersing and storing the corrected electromagnetic radiation data to a plurality of physical nodes; encrypting the corrected electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set, and sending the encrypted electromagnetic radiation data set to an online frequency selection platform through a dynamic block transmission strategy; and in response to the received electromagnetic radiation mode and the target area returned by the online frequency selection platform, performing data interaction on the electromagnetic radiation mode and the target area with the plurality of physical nodes, generating a three-dimensional space distribution diagram of the electromagnetic field through a preset map API, and displaying the three-dimensional space distribution diagram through the client. According to the invention, the efficiency and security of data transmission can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data transmission, and in particular to an online frequency selection data transmission method, system and online frequency selection platform. Background Art

[0002] With the rapid development of electronic and communication technology, the level of electromagnetic radiation in cities continues to rise and the frequency band range continues to expand. Electromagnetic radiation not only poses a potential threat to human health, but may also interfere with the normal operation of electronic equipment, and even cause equipment failure, affecting the stability of social production and life. Therefore, real-time monitoring and analysis of electromagnetic radiation environment has become an important topic in the current research field. The electromagnetic radiation monitoring system can provide an important basis for environmental management, equipment maintenance and public health by collecting, transmitting, storing and analyzing electromagnetic radiation data.

[0003] However, with the increase in the number of monitoring sites, the efficiency and security of data transmission become key issues. At present, the monitoring process of the existing electromagnetic radiation monitoring system is as follows: first, electromagnetic radiation data is collected through sensors, and TCP / IP protocol or HTTP protocol is used for data transmission. The collected electromagnetic radiation data is stored in a centralized database in chronological order, and the electromagnetic radiation data is displayed through a two-dimensional chart.

[0004] In the above-mentioned prior art, although the centralized database is convenient for data management and query, the read and write performance of the database will become a bottleneck when facing high-concurrency data transmission of large-scale equipment. A large amount of data is written to the database at the same time, which will cause a sharp increase in the I / O load of the database, thereby causing data transmission delays and packet loss problems. In addition, the transmission strategy of transmitting the collected electromagnetic radiation data to the centralized database in chronological order is prone to congestion of the data transmission channel when facing high-concurrency data transmission of large-scale equipment, thereby causing transmission delays and packet loss problems.

[0005] In summary, with the increase in data transmission volume, when facing high-concurrency data transmission of large-scale equipment, the electromagnetic radiation monitoring system in the existing technology is prone to problems such as transmission delay and packet loss, which greatly reduces the efficiency and security of data transmission. Summary of the invention

[0006] The embodiments of the present application provide an online frequency selection data transmission method, system and online frequency selection platform, which are used to effectively alleviate the problems of transmission delay, packet loss and the like occurring in the prior art, thereby improving the efficiency and security of data transmission.

[0007] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0008] In a first aspect, an online frequency selection data transmission method is provided, which is applied to an online frequency selection system, wherein the online frequency selection system includes a central control device, an intelligent hardware terminal group, and a client, wherein the central control device is connected to the intelligent hardware terminal group, wherein the central control device includes a local database, wherein the local database includes at least one physical node, and wherein the central control device is deployed with an online frequency selection platform, wherein the online frequency selection platform includes a cloud data center and an analysis engine, wherein the method includes:

[0009] Collect multi-band electromagnetic radiation data of multiple monitoring sites through the intelligent hardware terminal group;

[0010] Performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set;

[0011] Generate a data table according to the corrected electromagnetic radiation data set, and based on the data table, disperse and store the corrected electromagnetic radiation data to a plurality of physical nodes, wherein the data type of the electromagnetic radiation data stored in each physical node is consistent;

[0012] The electromagnetic radiation data set after encryption and correction is obtained to obtain an encrypted electromagnetic radiation data set, and the encrypted electromagnetic radiation data set is sent to the online frequency selection platform through a dynamic block transmission strategy, wherein the cloud data center is used to receive data blocks of the encrypted electromagnetic radiation data set, and classify and store all the data blocks of the encrypted electromagnetic radiation data set, and the analysis engine is used to decrypt each data block in blocks, and determine the electromagnetic radiation pattern and the target area with similar electromagnetic environment characteristics based on the electromagnetic radiation data after block decryption;

[0013] In response to receiving the electromagnetic radiation pattern and the target area returned by the online frequency selection platform, the electromagnetic radiation pattern and the target area are interacted with multiple physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated through a preset map API and displayed through the client.

[0014] In a possible implementation manner of the first aspect, performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set includes:

[0015] Acquire current environmental data, and use a preset electromagnetic wave attenuation formula to calculate the attenuation coefficient of the electromagnetic wave in the air based on the current environmental data;

[0016] Environmental interference compensation is performed on the multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data and obtain a corrected electromagnetic radiation data set.

[0017] In another possible implementation manner of the first aspect, the intelligent hardware terminal group includes n sensors, where n is an integer greater than 2, and the environmental interference compensation is performed on the multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set, including:

[0018] Obtaining the installation location of each sensor in the intelligent hardware terminal group;

[0019] For each electromagnetic radiation data, determining the geographical location information of the monitoring site;

[0020] Calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site using a distance calculation formula according to the installation location and the geographical location information of the monitoring site;

[0021] Calculating an average value of all the straight-line distances, and using the average value as the electromagnetic wave propagation distance;

[0022] Calculating the product of the attenuation coefficient and the propagation distance of the electromagnetic wave to obtain the attenuation of the electromagnetic wave in the air;

[0023] The multi-band electromagnetic radiation data is compensated according to the attenuation to obtain a corrected electromagnetic radiation data set.

[0024] In another possible implementation manner of the first aspect, generating a data table according to the corrected electromagnetic radiation data set, and dispersively storing the corrected electromagnetic radiation data to multiple physical nodes based on the data table includes:

[0025] Generate a data table including data type, timestamp, and geographic location information according to the corrected electromagnetic radiation data set, wherein the data table includes all electromagnetic radiation data in the corrected electromagnetic radiation data set, and each electromagnetic radiation data corresponds to a data type, timestamp, and geographic location information;

[0026] The data table is divided into multiple sub-tables according to the data type, and the electromagnetic radiation data corresponding to each sub-table is dispersedly stored in the corresponding physical nodes, wherein the multiple sub-tables include at least one real-time data sub-table, at least one historical data sub-table and at least one alarm data sub-table.

[0027] In another possible implementation manner of the first aspect, encrypting the corrected electromagnetic radiation dataset to obtain an encrypted electromagnetic radiation dataset includes:

[0028] Using a preset encryption algorithm to encrypt the corrected electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set;

[0029] Get the current network bandwidth and current network delay;

[0030] Calculate the temporary key generation parameter using a preset parameter calculation formula according to the current network bandwidth and the current network delay;

[0031] Calculate a first Hash value of the temporary key generation parameter, and use the first Hash value as the temporary key;

[0032] The temporary key is bound to the encrypted electromagnetic radiation dataset to obtain an encrypted electromagnetic radiation dataset.

[0033] In another possible implementation manner of the first aspect, the dynamic block transmission strategy includes:

[0034] Calculating the ratio of the current network bandwidth to the preset reference bandwidth to obtain a network bandwidth factor;

[0035] Calculating the inverse of the product of the network bandwidth factor and the temporary key to obtain a data block size coefficient;

[0036] Acquire the data set size of the encrypted electromagnetic radiation data set, and use the product of the data set size and the data block size coefficient as the size threshold of each data block, and adopt a dynamic partitioning strategy to dynamically partition the encrypted electromagnetic radiation data set into a plurality of data blocks, wherein the size of each of the data blocks is smaller than the size threshold;

[0037] For each of the data blocks, calculating a second Hash value of the product of the timestamp and the geographic location information, and using the second Hash value as a unique classification identifier of the data block;

[0038] Binding the unique classification identifier to the corresponding data block, and transmitting all the data blocks one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier;

[0039] The dynamic partitioning strategy includes:

[0040] Setting the initial data block size to the size threshold;

[0041] Reading the data in the encrypted electromagnetic radiation data set in sequence, and when the accumulated data amount reaches the current data block size, dividing the read data into a data block;

[0042] Calculate the average size of the divided data blocks;

[0043] If the average size is greater than 90% of the size threshold and less than the size threshold, keeping the current data block size unchanged;

[0044] If the average size is less than 90% of the size threshold, increase the current data block size by 10%;

[0045] If the average size is equal to the size threshold, reducing the current data block size by 5%;

[0046] Repeat the above steps until all the encrypted electromagnetic radiation data sets are divided.

[0047] In another possible implementation manner of the first aspect, the analysis engine performs block-by-block decryption on each of the data blocks, and determines a target area of ​​an electromagnetic radiation pattern and similar electromagnetic environment characteristics based on the electromagnetic radiation data after block decryption, including:

[0048] The analysis engine uses the temporary key to decrypt each data block to obtain the electromagnetic radiation data after block decryption;

[0049] Extracting frequency spectrum features of the electromagnetic radiation data after block decryption;

[0050] Performing K-means clustering analysis on the geographical location information of all the monitoring sites and the electromagnetic radiation data after block decryption to obtain k cluster centers;

[0051] Calculate the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center;

[0052] The cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics are merged to obtain m target areas, where m is less than or equal to k;

[0053] Determine the boundary of each target area according to the spatial distribution characteristics, and obtain target areas with similar electromagnetic environment characteristics;

[0054] The spectral features are input into a pre-built CNN model to determine the electromagnetic radiation pattern.

[0055] In another possible implementation manner of the first aspect, performing data interaction between the electromagnetic radiation pattern and the target area and the plurality of physical nodes, and generating and displaying a three-dimensional spatial distribution map of the electromagnetic field through a preset map API, includes:

[0056] generating an electromagnetic radiation intensity distribution map according to the electromagnetic radiation pattern and the target area;

[0057] The electromagnetic radiation intensity distribution map is dynamically matched with the geographical location data of the plurality of physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated in combination with a preset map API.

[0058] In a second aspect, the present application provides an online frequency selection system, comprising:

[0059] A central control device, comprising a local database, the local database comprising at least one physical node, and the central control device is deployed with an online frequency selection platform;

[0060] The intelligent hardware terminal group is connected to the central control device.

[0061] In a third aspect, the present application provides an online frequency selection platform, including:

[0062] Cloud data centers; and

[0063] Analytical engine.

[0064] Through the above technical solution, the multi-band electromagnetic radiation data of multiple monitoring sites are collected through the intelligent hardware terminal group, and the data is compensated for environmental interference to ensure the accuracy and reliability of the data, providing a high-quality data basis for subsequent analysis. Secondly, a decentralized storage strategy is adopted to store the corrected electromagnetic radiation data in multiple physical nodes according to the data type, avoiding the I / O load pressure of the centralized database during high-concurrency data transmission, thereby greatly improving the database's read and write performance and data storage efficiency. In addition, by encrypting the electromagnetic radiation data set and combining it with a dynamic block transmission strategy, the data is transmitted in encrypted form to the online frequency selection platform, which not only enhances the security of data transmission, but also alleviates the congestion problem of the data transmission channel through block transmission, effectively reducing transmission delay and packet loss rate. The cloud data center of the online frequency selection platform receives and classifies the encrypted data blocks, and the analysis engine decrypts each data block in blocks, and determines the target area of ​​electromagnetic radiation patterns and similar electromagnetic environment characteristics based on the decrypted data, which not only improves the efficiency and accuracy of data analysis, but also provides a scientific basis for real-time monitoring and evaluation of the electromagnetic radiation environment. Finally, by exchanging data between the analysis results and multiple physical nodes and using the preset map API to generate a three-dimensional spatial distribution map of the electromagnetic field, the spatial distribution characteristics of electromagnetic radiation are intuitively displayed through the client, providing visualization support. In summary, the existing electromagnetic radiation monitoring system effectively solves the problems of transmission delay, packet loss, and database read and write performance bottlenecks faced by large-scale equipment with high-concurrency data transmission, and significantly improves the efficiency and security of data transmission.

[0065] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flowchart of an online frequency-selective data transmission method provided in an embodiment of the present application;

[0067] Figure 2 A software architecture diagram of an online frequency selection platform provided in an embodiment of the present application;

[0068] Figure 3 A system topology diagram of an electromagnetic radiation environment automatic monitoring system provided in an embodiment of the present application;

[0069] Figure 4 A schematic diagram of the structure of an online frequency selection system provided in an embodiment of the present application;

[0070] Figure 5 A schematic diagram of the structure of an online frequency selection platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0072] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0073] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0074] Figure 1 The following is a schematic diagram showing a flow chart of an online frequency selection data transmission method according to an embodiment of the present application. Figure 1As shown, an embodiment of the present application provides an online frequency selection data transmission method, which is applied to an online frequency selection system. The online frequency selection system includes a central control device, an intelligent hardware terminal group and a client. The central control device is connected to the intelligent hardware terminal group. The central control device includes a local database, and the local database includes at least one physical node. The central control device is deployed with an online frequency selection platform. The online frequency selection platform includes a cloud data center and an analysis engine. The method may include the following steps.

[0075] S110, collecting multi-band electromagnetic radiation data of multiple monitoring sites through a group of intelligent hardware terminals;

[0076] S120, performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set;

[0077] S130, generating a data table according to the corrected electromagnetic radiation data set, and dispersively storing the corrected electromagnetic radiation data to multiple physical nodes based on the data table, wherein the data type of the electromagnetic radiation data stored in each physical node is consistent;

[0078] S140, encrypting the corrected electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set, and sending the encrypted electromagnetic radiation data set to the online frequency selection platform through a dynamic block transmission strategy, wherein the cloud data center is used to receive data blocks of the encrypted electromagnetic radiation data set, and classify and store all data blocks of the encrypted electromagnetic radiation data set, and the analysis engine is used to decrypt each data block in blocks, and determine the electromagnetic radiation pattern and the target area with similar electromagnetic environment characteristics based on the electromagnetic radiation data after block decryption;

[0079] S150, in response to receiving the electromagnetic radiation pattern and target area returned by the online frequency selection platform, exchanging data between the electromagnetic radiation pattern and the target area and multiple physical nodes, and generating a three-dimensional spatial distribution map of the electromagnetic field through a preset map API and displaying it through a client.

[0080] In this embodiment, the central control device can be a tablet computer, a desktop computer, a laptop computer, a handheld computer, a wearable device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, or other devices with a processor. Of course, the central control device can also be a server. The embodiment of the present application does not impose any special restrictions on the specific form of the central control device.

[0081] In the online frequency selection system, the intelligent hardware terminal group is the core component of data collection, responsible for collecting electromagnetic radiation data in multiple frequency bands from multiple monitoring sites. Monitoring sites are usually distributed in different geographical locations, covering a variety of environments such as cities, villages, and industrial areas to ensure the comprehensiveness and representativeness of the data. Each terminal device in the intelligent hardware terminal group is equipped with an electromagnetic radiation sensor that can detect the intensity of electromagnetic radiation in multiple frequency bands from low frequency to high frequency. The working principle of the sensor is based on electromagnetic induction. When electromagnetic waves pass through the sensor, an induced current is generated inside the sensor. The intensity of the induced current is proportional to the intensity of the electromagnetic wave. By measuring the intensity of the induced current, the intensity of the electromagnetic radiation can be calculated.

[0082] Figure 2 A software architecture diagram of an online frequency selection platform provided in an embodiment of the present application is shown, wherein data collected by a group of intelligent hardware terminals is sent to a site data collection server through a 4G / 5G communication module, and the data collection server is used to parse the received data and store the data in a local database.

[0083] In this embodiment, the sensor is also equipped with a GPS module to record the geographical location information of each monitoring site to ensure the geographical location accuracy of the data. In actual implementation, the intelligent hardware terminal group will automatically collect data at preset time intervals (for example, every 2 seconds or every minute) and transmit the data to the central control device in real time. In order to ensure the continuity and integrity of the data, the terminal device is usually equipped with a backup power supply and a local storage module to prevent data loss due to network interruption or power failure.

[0084] Since electromagnetic radiation data is easily interfered by environmental factors during the collection process, such as weather conditions, building shielding, interference from other electronic devices, etc., it is necessary to compensate for environmental interference on the original data to improve the accuracy of the data. The process of environmental interference compensation first requires obtaining the current environmental data, including meteorological parameters such as temperature, humidity, air pressure, wind speed, and geographical information such as the height and density of surrounding buildings. These environmental data can be obtained through environmental sensors in the intelligent hardware terminal group or external meteorological stations. Next, the preset electromagnetic wave attenuation formula is used to calculate the attenuation coefficient of electromagnetic waves in the air based on the current environmental data. The electromagnetic wave attenuation formula usually takes into account factors such as the frequency, propagation distance, dielectric constant and conductivity of the electromagnetic wave. For example, for high-frequency electromagnetic waves, the humidity of the air has a greater effect on its attenuation, so the influence of humidity needs to be specially considered when calculating the attenuation coefficient. According to the calculated attenuation coefficient, the original electromagnetic radiation data is compensated.

[0085] Specifically, for the electromagnetic radiation data of each monitoring site, its geographic location information is first determined, and then the distance calculation formula is used to calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site based on the installation location of the sensor and the geographic location information of the monitoring site. The average value of all straight-line distances is calculated, and the average value is used as the propagation distance of the electromagnetic wave. Finally, the product of the attenuation coefficient and the electromagnetic wave propagation distance is calculated to obtain the attenuation of the electromagnetic wave in the air, and the original electromagnetic radiation data is compensated according to the attenuation. In this way, the influence of environmental factors on the electromagnetic radiation data can be effectively eliminated, and a corrected electromagnetic radiation data set can be obtained, providing a more accurate basis for subsequent data processing and analysis.

[0086] After the corrected electromagnetic radiation data set is generated, it needs to be organized into a structured data table for subsequent storage and query. The data table generation process first needs to determine the data type, timestamp, and geographic location information of each electromagnetic radiation data. The data type usually includes electromagnetic radiation intensity, frequency range, sensor ID, etc.; the timestamp records the specific time of data collection; and the geographic location information records the latitude and longitude coordinates of the monitoring site. Based on the above information, a data table containing all electromagnetic radiation data is generated, in which each data item corresponds to a data type, timestamp, and geographic location information.

[0087] Next, divide the data table into multiple sub-tables according to the data type. For example, real-time data, historical data, and alarm data can be stored in different sub-tables. The real-time data sub-table is used to store the latest electromagnetic radiation data, the historical data sub-table is used to store electromagnetic radiation data in the past period of time, and the alarm data sub-table is used to store electromagnetic radiation data that exceeds the preset threshold. The electromagnetic radiation data corresponding to each sub-table will be distributed and stored in multiple physical nodes. The physical node is a component of the local database of the central control device, and each physical node is responsible for storing a specific type of electromagnetic radiation data. By distributing the data to multiple physical nodes (such as Figure 2 The site data on the data storage side shown in the figure can effectively improve the data read and write performance and avoid excessive load on a single node. In addition, the data type stored in each physical node is consistent, which facilitates data classification management and fast query. In actual implementation, distributed database technology, such as Hadoop, can be used to achieve decentralized storage and management of data.

[0088] In order to ensure the security of electromagnetic radiation data during transmission, the corrected electromagnetic radiation data set needs to be encrypted. The encryption process first uses a preset encryption algorithm (such as AES or RSA) to encrypt the electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set. The encryption algorithm usually uses a symmetric key or an asymmetric key. The symmetric key encryption speed is fast and suitable for the encryption of large amounts of data; the asymmetric key has high security and is suitable for the distribution and management of keys. In actual implementation, a hybrid encryption method can be used, that is, using a symmetric key to encrypt data and using an asymmetric key to encrypt symmetric keys. Next, the current network bandwidth and network delay are obtained, and the temporary key generation parameters are calculated based on these network parameters. The temporary key generation parameter is usually a random number or a hash value based on a timestamp, which is used to generate a temporary key. The first hash value of the temporary key generation parameter is calculated, and the hash value is used as a temporary key. The temporary key is bound to the encrypted electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set. In this way, the security of the data during transmission can be ensured, and even if the data is intercepted, it cannot be decrypted. Next, the encrypted electromagnetic radiation data set is sent to the online frequency selection platform using a dynamic block transmission strategy. The dynamic block transmission strategy first calculates the ratio of the current network bandwidth to the preset benchmark bandwidth to obtain the network bandwidth factor. The inverse of the product of the network bandwidth factor and the temporary key is calculated to obtain the data block size coefficient. The data set size of the encrypted electromagnetic radiation data set is obtained, and the product of the data set size and the data block size coefficient is used as the size threshold of each data block. The dynamic partitioning strategy is adopted to dynamically divide the encrypted electromagnetic radiation data set into multiple data blocks, where the size of each data block is less than the size threshold. For each data block, the second hash value of the product of the timestamp and the geographic location information is calculated, and the hash value is used as the unique classification identifier of the data block. The unique classification identifier is bound to the corresponding data block, and all data blocks are transmitted one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier. After receiving the data block, the cloud data center classifies and stores the data block according to the unique classification identifier. The analysis engine decrypts each data block in blocks, decrypts the data block using the temporary key, and obtains the electromagnetic radiation data after block decryption. The spectrum characteristics of the electromagnetic radiation data after block decryption are extracted, and the geographic location information and electromagnetic radiation data of all monitoring sites are subjected to K-means clustering analysis to obtain k cluster centers. Calculate the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center, merge cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics, and obtain m target areas, where m is less than or equal to k. According to the spatial distribution characteristics, determine the boundaries of each target area and obtain target areas with similar electromagnetic environment characteristics. Input the spectrum characteristics into the pre-built CNN model to determine the electromagnetic radiation pattern.

[0089] After receiving the electromagnetic radiation pattern and target area returned by the online frequency selection platform, it is necessary to interact with these data with multiple physical nodes to generate a three-dimensional spatial distribution map of the electromagnetic field. The data interaction process first requires matching the electromagnetic radiation pattern and target area with the electromagnetic radiation data stored in the physical node. The matching process is usually based on geographic location information and timestamps to ensure data consistency and accuracy. For example, for each target area, the electromagnetic radiation data in the area can be extracted from the physical node and compared with the electromagnetic radiation pattern to verify the accuracy of the pattern.

[0090] Next, an electromagnetic radiation intensity distribution map is generated according to the electromagnetic radiation pattern and the target area. The electromagnetic radiation intensity distribution map is usually in the form of a heat map, and the intensity of electromagnetic radiation is represented by the depth of color. The process of generating a heat map first requires converting the electromagnetic radiation data into geographic spatial data, and then using an interpolation algorithm (such as Kriging) to generate a continuous electromagnetic radiation intensity distribution map. The electromagnetic radiation intensity distribution map is dynamically matched with the geographic location data of multiple physical nodes to ensure the accuracy and real-time nature of the distribution map. Finally, combined with a preset map API (such as Google Maps or Baidu Maps), a three-dimensional spatial distribution map of the electromagnetic field is generated. The three-dimensional spatial distribution map is usually in the form of a stereo column chart or contour map, and the change in electromagnetic radiation intensity is represented by height or color. The process of generating a three-dimensional spatial distribution map first requires matching the electromagnetic radiation intensity distribution map with the geographic coordinate system of the map API, and then using three-dimensional rendering technology (such as WebGL or Three.js) to generate a three-dimensional graphic. By displaying the three-dimensional spatial distribution map on the client, users can intuitively view the spatial distribution of the electromagnetic field and understand the electromagnetic radiation intensity and pattern in different regions.

[0091] Figure 3 FIG. 4 shows a system topology diagram of an automatic electromagnetic radiation environment monitoring system provided by an embodiment of the present application. Figure 3 As shown in the figure, the electromagnetic radiation environment automatic monitoring system processes the data monitored by different online frequency selection sub-sites, converts the serial port data into IP data through the data transmission unit, transmits it through the wireless communication network, and sends the data to the cloud server. The data is pulled, processed, parsed and stored on the server terminal. The required results are displayed on the online frequency selection interface.

[0092] This embodiment collects multi-band electromagnetic radiation data of multiple monitoring sites through a group of intelligent hardware terminals, and compensates for environmental interference on the data to ensure the accuracy and reliability of the data, providing a high-quality data basis for subsequent analysis. Secondly, a decentralized storage strategy is adopted to store the corrected electromagnetic radiation data in multiple physical nodes according to the data type, avoiding the I / O load pressure of the centralized database during high-concurrency data transmission, thereby greatly improving the database's read and write performance and data storage efficiency. In addition, by encrypting the electromagnetic radiation data set and combining the dynamic block transmission strategy, the data is transmitted in encrypted form to the online frequency selection platform in blocks, which not only enhances the security of data transmission, but also alleviates the congestion problem of the data transmission channel through block transmission, effectively reducing transmission delay and packet loss rate. The cloud data center of the online frequency selection platform receives and classifies the encrypted data blocks, and the analysis engine decrypts each data block in blocks, and determines the target area of ​​electromagnetic radiation patterns and similar electromagnetic environment characteristics based on the decrypted data, which not only improves the efficiency and accuracy of data analysis, but also provides a scientific basis for real-time monitoring and evaluation of the electromagnetic radiation environment. Finally, by exchanging data between the analysis results and multiple physical nodes and using the preset map API to generate a three-dimensional spatial distribution map of the electromagnetic field, the spatial distribution characteristics of electromagnetic radiation are intuitively displayed through the client, providing visualization support. In summary, the existing electromagnetic radiation monitoring system effectively solves the problems of transmission delay, packet loss, and database read and write performance bottlenecks faced by large-scale equipment with high-concurrency data transmission, and significantly improves the efficiency and security of data transmission.

[0093] In one implementation of this embodiment, environmental interference compensation is performed on multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set, including the following steps:

[0094] S210, obtaining current environmental data, and using a preset electromagnetic wave attenuation formula to calculate the attenuation coefficient of the electromagnetic wave in the air based on the current environmental data;

[0095] S220 , performing environmental interference compensation on the multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set.

[0096] In the process of collecting electromagnetic radiation data, environmental factors have a significant impact on the accuracy of the data. In order to eliminate these effects, it is first necessary to obtain the current environmental data, including meteorological parameters such as temperature, humidity, air pressure, wind speed, and geographic information such as the height and density of surrounding buildings. These environmental data can be obtained in real time through environmental sensors in the intelligent hardware terminal group or external meteorological stations. For example, the temperature sensor can measure the current air temperature, the humidity sensor can measure the water vapor content in the air, the air pressure sensor can measure the atmospheric pressure, and the wind speed sensor can measure the speed of air flow. In addition, geographic information can be obtained through a geographic information system (GIS). Next, the preset electromagnetic wave attenuation formula is used to calculate the attenuation coefficient of electromagnetic waves in the air based on the current environmental data. The electromagnetic wave attenuation formula takes into account factors such as the frequency of the electromagnetic wave, the propagation distance, the dielectric constant and conductivity of the air. For example, for high-frequency electromagnetic waves, the humidity of the air has a greater effect on its attenuation, so the influence of humidity needs to be specially considered when calculating the attenuation coefficient. The electromagnetic wave attenuation formula can be expressed as:

[0097]

[0098] Among them, α is the attenuation coefficient, f is the frequency of the electromagnetic wave, c is the speed of light, μ is the magnetic permeability of air, ∈′ is the real part of the dielectric constant of air, and ∈″ is the imaginary part of the dielectric constant of air. In actual calculations, the real and imaginary parts of the dielectric constant of air can be calculated through environmental parameters such as temperature and humidity. For example, when the temperature is 25°C and the humidity is 60%, the real part of the dielectric constant of air is approximately 1.0006, and the imaginary part is approximately 0.0001. By substituting these parameters into the formula, the attenuation coefficient of electromagnetic waves in air can be calculated. For example, for an electromagnetic wave with a frequency of 1 GHz, the attenuation coefficient is approximately 0.001 dB / m under the conditions of temperature of 25°C and humidity of 60%.

[0099] After calculating the attenuation coefficient of electromagnetic waves in the air, it is necessary to perform environmental interference compensation on the multi-band electromagnetic radiation data based on this coefficient to correct the original data. The environmental interference compensation process first requires determining the geographic location information of each monitoring site and the installation location of the sensor. The geographic location information usually includes the latitude and longitude coordinates of the monitoring site, and the installation location of the sensor includes the latitude and longitude, altitude and other information of the sensor. Finally, the distance calculation formula is used to calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site. The distance calculation formula can be expressed as:

[0100]

[0101] Where d is the propagation distance of the electromagnetic wave, (x1, y1, z1) is the installation location coordinates of the sensor, and (x2, y2, z2) is the geographical location coordinates of the monitoring site. For example, if the installation location coordinates of the sensor are (10, 20, 5) and the geographical location coordinates of the monitoring site are (15, 25, 0), then the propagation distance of the electromagnetic wave is:

[0102]

[0103] Calculate the average of the electromagnetic wave propagation distances of all monitoring sites, and use the average as the electromagnetic wave propagation distance. For example, if there are 10 monitoring sites, and their electromagnetic wave propagation distances are 8.66 meters, 7.21 meters, 9.43 meters, etc., then the average propagation distance is:

[0104]

[0105] Next, calculate the product of the attenuation coefficient and the propagation distance of the electromagnetic wave to get the attenuation of the electromagnetic wave in the air. For example, if the attenuation coefficient is 0.001dB / m and the average propagation distance is 8.5 meters, the attenuation is:

[0106] Attenuation = 0.001 × 8.5 = 0.0085 dB.

[0107] According to the attenuation, the original electromagnetic radiation data is compensated. For example, if the original electromagnetic radiation data is -50dBm, the corrected electromagnetic radiation data is:

[0108] The corrected electromagnetic radiation data = -50 + 0.0085 = -49.9915 dBm.

[0109] This implementation can significantly improve the accuracy and reliability of the data by compensating for environmental interference in multi-band electromagnetic radiation data. First, by obtaining the current environmental data and calculating the electromagnetic wave attenuation coefficient, the impact of environmental factors on electromagnetic radiation data can be scientifically quantified. Secondly, by compensating the original data according to the attenuation coefficient, environmental interference can be effectively eliminated to obtain a more accurate electromagnetic radiation data set. The corrected data can not only truly reflect the actual situation of electromagnetic radiation, but also provide a reliable basis for subsequent data processing and analysis.

[0110] In one implementation of this embodiment, the intelligent hardware terminal group includes n sensors, where n is an integer greater than 2, and environmental interference compensation is performed on multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set, including the following steps:

[0111] S310, obtaining the installation location of each sensor in the intelligent hardware terminal group;

[0112] S320, for each electromagnetic radiation data, determining the geographical location information of the monitoring site;

[0113] S330, calculating the straight-line distance of the electromagnetic wave from the sensor to the monitoring site using a distance calculation formula according to the installation location and the geographical location information of the monitoring site;

[0114] S340, calculating the average value of all straight-line distances, and using the average value as the electromagnetic wave propagation distance;

[0115] S350, calculating the product of the attenuation coefficient and the electromagnetic wave propagation distance to obtain the attenuation of the electromagnetic wave in the air;

[0116] S360. Compensate the multi-band electromagnetic radiation data according to the attenuation amount to obtain a corrected electromagnetic radiation data set.

[0117] In the intelligent hardware terminal group, the installation location of each sensor is one of the key parameters for environmental interference compensation calculation. The installation location of the sensor usually includes its latitude and longitude coordinates, altitude, orientation and other information. This information can be obtained through a GPS module, a laser rangefinder or other positioning equipment. For example, the GPS module can record the latitude and longitude coordinates of the sensor in real time, the laser rangefinder can measure the height difference between the sensor and the ground or other reference points, and the orientation information can be obtained through an electronic compass. In actual implementation, the installation location information of the sensor is usually accurately measured during installation and stored in a local database or central control device for subsequent calls. For example, suppose there are 10 sensors in the intelligent hardware terminal group, and their installation location coordinates are (10, 20, 5), (15, 25, 6), (20, 30, 7), etc., where the first two numbers represent the latitude and longitude coordinates and the third number represents the altitude. By obtaining this installation location information, accurate basic data can be provided for subsequent distance calculations. In addition, the installation location information of the sensor can also be used to calibrate the measurement results of the sensor to ensure the accuracy and consistency of the data. For example, if the installation location of a sensor changes, the installation location information can be remeasured and updated to ensure that subsequent data processing and analysis are not affected.

[0118] After obtaining the installation location information of the sensor, it is necessary to determine the geographical location information of each monitoring site where the electromagnetic radiation data is located. The geographical location information of the monitoring site usually includes its latitude and longitude coordinates, altitude and other information. This information can be obtained through a GPS module, laser rangefinder or other positioning equipment. In actual implementation, the geographical location information of the monitoring site is usually accurately measured during installation and stored in a local database or central control device for subsequent calls.

[0119] After obtaining the installation location of the sensor and the geographical location information of the monitoring site, it is necessary to calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site. The straight-line distance is usually calculated using a distance calculation formula in three-dimensional space, refer to the description of S220, and this application will not repeat it here.

[0120] In actual implementation, automated scripts or software tools can be used to batch calculate the distances between all sensors and monitoring sites, and store the results in a local database or central control device for subsequent calls. For example, if there are 10 sensors and 5 monitoring sites in the intelligent hardware terminal group, 50 distance values ​​need to be calculated. Batch calculation can greatly improve calculation efficiency and reduce the workload of manual operations.

[0121] After calculating the straight-line distances between all sensors and monitoring sites, it is necessary to calculate the average of these distances and use the average as the propagation distance of the electromagnetic wave. The average is usually calculated using the arithmetic mean method, which is to add up all the distance values ​​and then divide them by the total number of distance values.

[0122] After calculating the average propagation distance of the electromagnetic wave, it is necessary to calculate the attenuation of the electromagnetic wave in the air based on the attenuation coefficient. The attenuation is the product of the attenuation coefficient and the propagation distance of the electromagnetic wave, which can be expressed as:

[0123] Attenuation = α × d;

[0124] Among them, α is the attenuation coefficient and d is the propagation distance of the electromagnetic wave.

[0125] After calculating the attenuation of electromagnetic waves in the air, the multi-band electromagnetic radiation data needs to be compensated according to the attenuation to correct the original data. The compensation process usually uses addition or subtraction operations, depending on whether the attenuation is positive or negative.

[0126] This implementation method can significantly improve the accuracy and reliability of data by compensating for environmental interference in multi-band electromagnetic radiation data. First, the installation location of the sensor and the geographical location information of the monitoring site are obtained to provide accurate basic data for subsequent distance calculations. Secondly, the distance calculation formula is used to calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site, which can accurately quantify the propagation distance of the electromagnetic wave. Next, the average value of all straight-line distances is calculated, and the average value is used as the propagation distance of the electromagnetic wave, which can further improve the accuracy of the calculation. Then, the attenuation of the electromagnetic wave in the air is calculated according to the attenuation coefficient, which can scientifically quantify the impact of environmental factors on the electromagnetic radiation data. Finally, the original data is compensated according to the attenuation amount, which can effectively eliminate environmental interference and obtain a more accurate electromagnetic radiation data set.

[0127] In one implementation of this embodiment, generating a data table according to the corrected electromagnetic radiation data set, and distributing and storing the corrected electromagnetic radiation data to multiple physical nodes based on the data table includes the following steps:

[0128] S410, generating a data table including data type, timestamp, and geographic location information according to the corrected electromagnetic radiation data set, wherein the data table includes all electromagnetic radiation data in the corrected electromagnetic radiation data set, and each electromagnetic radiation data corresponds to a data type, timestamp, and geographic location information;

[0129] S420. Divide the data table into multiple sub-tables according to data types, and disperse and store the electromagnetic radiation data corresponding to each sub-table to the corresponding physical nodes, wherein the multiple sub-tables include at least one real-time data sub-table, at least one historical data sub-table and at least one alarm data sub-table.

[0130] After the corrected electromagnetic radiation data set is generated, it needs to be organized into a structured data table for subsequent storage and query. The data table generation process first needs to determine the data type, timestamp, and geographic location information of each electromagnetic radiation data. The data type usually includes electromagnetic radiation intensity, frequency range, sensor ID, etc.; the timestamp records the specific time of data collection; and the geographic location information records the longitude and latitude coordinates of the monitoring site. Based on this information, a data table containing all electromagnetic radiation data is generated, in which each data item corresponds to a data type, timestamp, and geographic location information. For example, assuming that the corrected electromagnetic radiation data set contains 1,000 data items, the data type of each data item is "electromagnetic radiation intensity", the timestamp is "2023-10-0112:00:00", and the geographic location information is "longitude: 116.3975, latitude: 39.9087", then the generated data table can be expressed as:

[0131] Data Types Timestamp Geographical location information Electromagnetic radiation intensity (dBm) Electromagnetic radiation intensity 2023-10-0112:00 Longitude: 116.3975, Latitude: 39.9087 -49.9915 Electromagnetic radiation intensity 2023-10-0112:01 Longitude: 116.3975, Latitude: 39.9088 -50.0123 …… …… …… ……

[0132] In this way, the corrected electromagnetic radiation data set can be organized into a structured data table for subsequent storage and query. In actual implementation, a database management system (such as MySQL or PostgreSQL) can be used to generate and manage data tables. For example, a data table can be created using an SQL statement, and the corrected electromagnetic radiation data can be inserted into the data table. In this way, a large amount of electromagnetic radiation data can be efficiently stored and managed, providing reliable support for subsequent data analysis and application.

[0133] After the data table is generated, it is necessary to divide the data table into multiple sub-tables according to the data type, and store the electromagnetic radiation data corresponding to each sub-table in a dispersed manner to the corresponding physical nodes. The division of data types is usually based on the purpose and characteristics of the data, such as real-time data, historical data, and alarm data. The real-time data sub-table is used to store the latest electromagnetic radiation data, the historical data sub-table is used to store electromagnetic radiation data over a period of time, and the alarm data sub-table is used to store electromagnetic radiation data that exceeds the preset threshold. For example, assuming that the data table contains 1,000 data items, including 500 real-time data items, 300 historical data items, and 200 alarm data items, the data table can be divided into three sub-tables:

[0134] 1. Real-time data subtable: contains 500 real-time data items. The data type of each data item is "real-time electromagnetic radiation intensity", the timestamp is the latest collection time, and the geographic location information is the latitude and longitude coordinates of the monitoring site.

[0135] 2. Historical data subtable: contains 300 historical data items. The data type of each data item is "historical electromagnetic radiation intensity", the timestamp is the collection time within a period of time in the past, and the geographic location information is the longitude and latitude coordinates of the monitoring site.

[0136] 3. Alarm data subtable: contains 200 alarm data items. The data type of each data item is "alarm electromagnetic radiation intensity", the timestamp is the collection time exceeding the preset threshold, and the geographic location information is the latitude and longitude coordinates of the monitoring site.

[0137] In this way, the data table can be divided into multiple sub-tables, which is convenient for subsequent classification management and fast query. In actual implementation, distributed database technology (such as Hadoop) can be used to achieve decentralized storage and management of data. For example, the real-time data sub-table can be stored on one physical node, the historical data sub-table can be stored on another physical node, and the alarm data sub-table can be stored on a third physical node. In this way, the read and write performance of the data can be effectively improved to avoid excessive load on a single node. In addition, the data type stored in each physical node is consistent, which is convenient for classification management and fast query of data.

[0138] This implementation can significantly improve the storage efficiency and query performance of data by generating a data table based on the corrected electromagnetic radiation data set and dividing the data table into multiple sub-tables based on the data type. First, a data table containing data type, timestamp and geographic location information is generated, which can organize the corrected electromagnetic radiation data set into a structured data table for subsequent storage and query. Secondly, the data table is divided into multiple sub-tables according to the data type, which can classify and store the data, facilitating subsequent classification management and quick query. Finally, the electromagnetic radiation data corresponding to each sub-table is dispersedly stored in the corresponding physical node, which can effectively improve the read and write performance of the data and avoid excessive load on a single node.

[0139] In one implementation manner of this embodiment, encrypting the corrected electromagnetic radiation dataset to obtain the encrypted electromagnetic radiation dataset includes the following steps:

[0140] S510, encrypting the corrected electromagnetic radiation data set using a preset encryption algorithm to obtain an encrypted electromagnetic radiation data set;

[0141] S520, obtaining current network bandwidth and current network delay;

[0142] S530, calculating the temporary key generation parameter using a preset parameter calculation formula according to the current network bandwidth and the current network delay;

[0143] S540, calculating a first Hash value of a temporary key generation parameter, and using the first Hash value as a temporary key;

[0144] S550: Bind the temporary key to the encrypted electromagnetic radiation dataset to obtain the encrypted electromagnetic radiation dataset.

[0145] In order to ensure the security of electromagnetic radiation data during transmission and storage, the corrected electromagnetic radiation data set needs to be encrypted. The encryption process first uses a preset encryption algorithm (such as AES or RSA) to encrypt the electromagnetic radiation data set. AES (Advanced Encryption Standard) is a symmetric encryption algorithm with the characteristics of fast encryption speed and high security, which is suitable for encryption of large amounts of data; RSA (asymmetric encryption algorithm) has higher security and is suitable for key distribution and management. In actual implementation, a hybrid encryption method can be used, that is, using a symmetric key to encrypt data and using an asymmetric key to encrypt a symmetric key. For example, first use the AES algorithm to generate a symmetric key, and then use the symmetric key to encrypt the electromagnetic radiation data set. Then, use the RSA algorithm to encrypt the symmetric key, and bind the encrypted symmetric key to the encrypted electromagnetic radiation data set. In this way, the security of the data during transmission and storage can be ensured, and even if the data is intercepted, it cannot be decrypted.

[0146] In this way, the corrected electromagnetic radiation data set can be encrypted to ensure the security of the data during transmission and storage. In actual implementation, an encryption library (such as OpenSSL) can be used to implement the encryption algorithm. For example, the AES algorithm can be used to encrypt the electromagnetic radiation data set, and the encrypted data can be stored in a local database or central control device for subsequent calls.

[0147] After the encrypted electromagnetic radiation data set is generated, the current network bandwidth and network delay need to be obtained to provide parameters for the subsequent temporary key generation. Network bandwidth usually indicates the ability of the network to transmit data, and the unit is Mbps (megabits per second); network delay indicates the transmission time of data from the sender to the receiver, and the unit is milliseconds (ms). These network parameters can be obtained through network monitoring tools (such as Ping or Traceroute). For example, the Ping command can be used to measure network delay, and the Speedtest tool can be used to measure network bandwidth.

[0148] After obtaining the current network bandwidth and network delay, it is necessary to calculate the temporary key generation parameter based on these parameters. The temporary key generation parameter is usually a random number or a hash value based on a timestamp, which is used to generate a temporary key. The preset parameter calculation formula can be expressed as:

[0149]

[0150] The unit of network bandwidth is Mbps, the unit of network delay is ms, and the timestamp is the number of milliseconds of the current time.

[0151] After calculating the temporary key generation parameter, it is necessary to calculate the first hash value of the parameter and use the hash value as the temporary key. The hash value is usually calculated using a hash algorithm (such as SHA-256 or MD5), which can convert input data of any length into output data of a fixed length. For example, the hash value of the temporary key generation parameter calculated using the SHA-256 algorithm can be expressed as:

[0152] Temporary key = SHA-256 (temporary key generation parameters).

[0153] For example, assuming that the temporary key generation parameter is 3392313600000, the SHA-256 algorithm is used to calculate its hash value, and a 256-bit binary number can be obtained. Converting it into hexadecimal representation is the temporary key. For example, assuming that the calculated hash value is "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0u1v2w3x4y5z6", the hash value can be used as the temporary key. In this way, the temporary key can be accurately calculated. In actual implementation, a hash library (such as Python's hashlib library) can be used to implement the hash algorithm. For example, the SHA-256 algorithm can be used to calculate the hash value of the temporary key generation parameter.

[0154] After calculating the temporary key, it is necessary to bind the key to the encrypted electromagnetic radiation dataset to obtain the encrypted electromagnetic radiation dataset. The binding process usually adopts a data binding algorithm, which can associate the temporary key with the encrypted electromagnetic radiation dataset. For example, the temporary key can be used as metadata of the encrypted electromagnetic radiation dataset and stored in the head or tail of the dataset. In this way, the temporary key can be bound to the encrypted electromagnetic radiation dataset to ensure the security of the data during transmission and storage. In actual implementation, a data binding library (such as Python's struct library) can be used to implement the data binding algorithm. For example, the struct library can be used to bind the temporary key to the encrypted electromagnetic radiation dataset.

[0155] This implementation method can significantly improve the security and transmission efficiency of data by encrypting the corrected electromagnetic radiation data set using a preset encryption algorithm, and generating a temporary key to bind to the encrypted data set. First, the electromagnetic radiation data set is encrypted using an encryption algorithm such as AES or RSA to ensure the security of the data during transmission and storage. Secondly, the current network bandwidth and network delay are obtained, and the temporary key generation parameters are calculated based on these parameters, which can provide a scientific basis for the generation of temporary keys. Then, the hash value of the temporary key generation parameters is calculated, and the hash value is used as the temporary key, which can ensure the uniqueness and security of the key. Finally, the temporary key is bound to the encrypted electromagnetic radiation data set to ensure the integrity and security of the data during transmission and storage, so that a large amount of electromagnetic radiation data can be efficiently encrypted and managed.

[0156] In one implementation of this embodiment, the dynamic block transmission strategy includes the following steps:

[0157] S610, calculating the ratio of the current network bandwidth to the preset reference bandwidth to obtain a network bandwidth factor;

[0158] S620, calculating the inverse of the product of the network bandwidth factor and the temporary key to obtain a data block size coefficient;

[0159] S630, obtaining the data set size of the encrypted electromagnetic radiation data set, and taking the product of the data set size and the data block size coefficient as the size threshold of each data block, and adopting a dynamic partitioning strategy to dynamically partition the encrypted electromagnetic radiation data set into multiple data blocks, wherein the size of each data block is smaller than the size threshold;

[0160] S640: For each data block, calculate a second Hash value of the product of the timestamp and the geographic location information, and use the second Hash value as a unique classification identifier of the data block;

[0161] S650, binding the unique classification identifier to the corresponding data block, and transmitting all the data blocks one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier;

[0162] The dynamic partitioning strategies include:

[0163] S1, setting the initial data block size as the size threshold;

[0164] S2, reading the data in the encrypted electromagnetic radiation data set in sequence, and when the accumulated data volume reaches the current data block size, dividing the read data into a data block;

[0165] S3, calculating the average size of the divided data blocks;

[0166] S4. If the average size is greater than 90% of the size threshold and less than the size threshold, the current data block size is kept unchanged;

[0167] S5. If the average size is less than 90% of the size threshold, increase the current data block size by 10%;

[0168] S6. If the average size is equal to the size threshold, reduce the current data block size by 5%;

[0169] S7. Repeat the above steps until all encrypted electromagnetic radiation data sets are divided.

[0170] In the dynamic block transmission strategy, the network bandwidth factor is one of the key parameters that determine the data block size. The network bandwidth factor is obtained by calculating the ratio of the current network bandwidth to the preset benchmark bandwidth. The preset benchmark bandwidth is usually a fixed value that represents the transmission capacity of the network under ideal conditions, such as 100Mbps. The current network bandwidth can be obtained in real time through network monitoring tools.

[0171] After calculating the network bandwidth factor, you need to calculate the data block size coefficient based on this factor and the temporary key. The data block size coefficient is another key parameter that determines the data block size. It is obtained by calculating the inverse of the product of the network bandwidth factor and the temporary key. The temporary key is usually a hash value, such as "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0u1v2w3x4y5z6", which can be converted into a numerical value for calculation. For example, assuming the value of the temporary key is 1234567890 and the network bandwidth factor is 0.8, the data block size coefficient is:

[0172]

[0173] After calculating the data block size coefficient, it is necessary to calculate the size threshold of each data block based on the coefficient and the size of the encrypted electromagnetic radiation dataset. The dataset size is usually in bytes and can be obtained through the file system or database management system. For example, assuming that the size of the encrypted electromagnetic radiation dataset is 1GB (1,073,741,824 bytes), the data block size coefficient is 1.012×10 -9 , then the size threshold of each data block is:

[0174] Size threshold = data set size × data block size coefficient = 1,073,741,824 × 1.012 × 10-9 ≈ 1.086 MB.

[0175] In this way, the size threshold of each data block can be accurately calculated. In actual implementation, a dynamic partitioning strategy can be used to dynamically divide the encrypted electromagnetic radiation data set into multiple data blocks. The dynamic partitioning strategy first sets the initial data block size as the size threshold (1.086MB), and then reads the data in the encrypted electromagnetic radiation data set in sequence. When the cumulative data volume reaches the current data block size, the read data is divided into a data block, and the size of each data block is less than the size threshold. It should be noted that there is a situation where the cumulative data volume of a data block is much lower than the initial data block size. In this case, the cumulative data volume of the data block is its own data volume. This situation is caused by the following reasons: 1. In the process of dividing the data block, the total size of the encrypted electromagnetic radiation data set may not be an integer multiple of the initial data block size. This means that when the last block of data is divided, the remaining data volume may not be enough to fill a complete data block. For example, assuming that the total size of the encrypted electromagnetic radiation data set is 10.86MB and the initial data block size is 1.086MB, it can theoretically be divided into 10 complete data blocks (each 1.086MB). However, if the total size of the data set is 10.86MB+0.5MB=11.36MB, the size of the last data block is only 0.5MB, which is much smaller than the initial data block size (1.086MB). In this case, the cumulative data volume of the last data block is its own data volume (0.5MB). 2. The data distribution in the encrypted electromagnetic radiation data set may be uneven, and the cumulative data volume of some data blocks may be much smaller than the initial data block size. For example, suppose that the data in the data set is sparse in some areas, resulting in the cumulative data volume of some data blocks failing to reach the initial data block size when dividing the data blocks. In this case, even if the total size of the data set is large enough, the cumulative data volume of some data blocks may still be much smaller than the initial data block size.

[0176] After the encrypted electromagnetic radiation data set is divided into multiple data blocks, a unique classification identifier needs to be generated for each data block. The unique classification identifier is obtained by calculating the second hash value of the product of the timestamp and the geographic location information. The timestamp usually indicates the specific time of data collection, such as "2023-10-0112:00:00", which can be converted into a numerical value for calculation. The geographic location information usually indicates the longitude and latitude coordinates of the monitoring site, such as "longitude: 116.3975, latitude: 39.9087", which can be converted into a numerical value for calculation. For example, assuming that the value of the timestamp is 1696156800000, the value of the geographic location information is 116.3975×39.9087≈4645.67.

[0177] Next, calculate the second hash value, which can be expressed as:

[0178] Unique classification identifier = SHA-256 (product).

[0179] In actual implementation, a hash library (such as Python's hashlib library) can be used to implement the hash algorithm. For example, the SHA-256 algorithm can be used to calculate the hash value of the product of the timestamp and the geographic location information.

[0180] After generating a unique classification identifier for each data block, it is necessary to bind the identifier to the corresponding data block, and transmit all data blocks one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier. The binding process usually adopts a data binding algorithm, which can associate the unique classification identifier with the data block. For example, the unique classification identifier can be used as metadata of the data block and stored in the header or tail of the data block. In this way, the unique classification identifier can be bound to the data block to ensure the integrity and traceability of the data during transmission and storage.

[0181] Due to the uneven distribution of data in the encrypted electromagnetic radiation dataset, there are cases where the cumulative data volume of some data blocks is close to but has not completely reached the current data block size threshold, or the cumulative data volume of some data blocks is far from the current data block size threshold. Therefore, during the data block division process, the cumulative data volume of the data block is close to but has not completely reached (is far from reaching) the current data block size threshold, which leads to the average size being too low or deviating from the size threshold. At this time, the following dynamic division strategy is executed:

[0182] After setting the initial data block size, the data in the encrypted electromagnetic radiation dataset needs to be read sequentially, and the data whose accumulated data volume reaches the current data block size is divided into a data block. The reading process usually adopts a streaming reading method, that is, reading data byte by byte or block by block to reduce memory usage. In this way, the encrypted electromagnetic radiation dataset can be divided into multiple data blocks, and the size of each data block is less than or equal to the current data block size.

[0183] After dividing the data blocks, it is necessary to calculate the average size of the divided data blocks to evaluate the rationality of the data block size. The average size is usually calculated using the arithmetic average method, that is, adding the sizes of all divided data blocks and then dividing by the number of data blocks.

[0184] After calculating the average size of the divided data blocks, the data block size needs to be adjusted based on the relationship between the average value and the size threshold. If the average size is greater than 90% of the size threshold and less than the size threshold, the current data block size remains unchanged. For example, assuming the size threshold is 1.086MB (1,086,000 bytes) and the average size is 1,000,000 bytes, 90% of the size threshold is 977,400 bytes. Since 1,000,000 bytes is greater than 977,400 bytes and less than 1,086,000 bytes, the current data block size remains unchanged. In this way, it can be ensured that the size of the data block is neither too large to cause transmission delays nor too small to cause low transmission efficiency during network transmission.

[0185] If the average size of the divided data blocks is less than 90% of the size threshold, the current data block size needs to be increased by 10% to improve the efficiency of data transmission. For example, assuming the size threshold is 1.086MB (1,086,000 bytes) and the average size is 900,000 bytes, then 90% of the size threshold is 977,400 bytes. Since 900,000 bytes is less than 977,400 bytes, the current data block size is increased by 10%, that is, the new data block size is:

[0186] New data block size = 1,086,000 × 1.10 = 1,194,600 bytes. In this way, the efficiency of data transmission can be effectively improved.

[0187] If the average size of the divided data blocks is equal to the size threshold, the current data block size needs to be reduced by 5% to optimize the efficiency of data transmission. For example, assuming that the size threshold is 1.086MB (1,086,000 bytes) and the average size is 1,086,000 bytes, the current data block size is reduced by 5%. In this way, the efficiency of data transmission can be effectively optimized.

[0188] After adjusting the data block size, the above steps need to be repeated until all encrypted electromagnetic radiation data sets are divided. The repetitive process includes reading data, dividing data blocks, calculating the average size, adjusting the data block size, etc., to ensure that the size of the data block is neither too large to cause transmission delays nor too small to cause low transmission efficiency during network transmission. The dynamic partitioning strategy can significantly improve the efficiency and flexibility of data transmission. First, setting the initial data block size as the size threshold can provide a scientific starting point for data partitioning. Secondly, reading data and dividing data blocks in sequence can ensure that the size of the data block is neither too large to cause transmission delays nor too small to cause low transmission efficiency during network transmission. Next, calculating the average size of the divided data blocks can evaluate the rationality of the data block size. Then, adjusting the data block size according to the relationship between the average size and the size threshold can optimize the efficiency of data transmission. Finally, repeating the above steps until all data sets are divided can ensure that the size of the data block always remains in the optimal state during network transmission. In this way, a large amount of encrypted electromagnetic radiation data can be efficiently divided and managed.

[0189] This implementation method first calculates the ratio of the current network bandwidth to the preset reference bandwidth to obtain the network bandwidth factor, which can provide a scientific basis for the division of the data block size. Secondly, the inverse of the product of the network bandwidth factor and the temporary key is calculated to obtain the data block size coefficient, which can accurately determine the size threshold of each data block. Then, a dynamic partitioning strategy is adopted to dynamically divide the encrypted electromagnetic radiation data set into multiple data blocks, which can effectively improve the efficiency of data transmission. Then, the second hash value of the product of the timestamp and the geographic location information is calculated, and the hash value is used as the unique classification identifier of the data block, which can ensure the uniqueness and traceability of the data block. Finally, the unique classification identifier is bound to the corresponding data block, and all data blocks are transmitted one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier, which can ensure the integrity and security of the data during transmission and storage. Through the dynamic block transmission strategy, the efficiency and security of data transmission can be significantly improved.

[0190] In one implementation of this embodiment, the analysis engine decrypts each data block, and determines the target area of ​​the electromagnetic radiation pattern and similar electromagnetic environment characteristics based on the electromagnetic radiation data after the block decryption, including the following steps:

[0191] S710, the analysis engine decrypts each data block using the temporary key to obtain decrypted electromagnetic radiation data;

[0192] S720, extracting spectrum characteristics of the electromagnetic radiation data after block decryption;

[0193] S730, performing K-means cluster analysis on the geographical location information of all monitoring sites and the decrypted electromagnetic radiation data to obtain k cluster centers;

[0194] S740, calculating the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center;

[0195] S750, merging cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics to obtain m target areas, where m is less than or equal to k;

[0196] S760, determining the boundary of each target area according to the spatial distribution characteristics, and obtaining target areas with similar electromagnetic environment characteristics;

[0197] S770, input the spectrum features into the pre-built CNN model to determine the electromagnetic radiation pattern.

[0198] After receiving the encrypted data blocks, the analysis engine needs to decrypt each data block using the temporary key to restore the original electromagnetic radiation data. The decryption process usually uses a decryption algorithm corresponding to the encryption algorithm. For example, if the AES algorithm was used for encryption, the AES algorithm must also be used for decryption. The temporary key is generated by a hash algorithm, and the decrypted data includes electromagnetic radiation intensity, frequency range, timestamp, and geographic location information.

[0199] After the decrypted electromagnetic radiation data is restored, its spectrum features need to be extracted for further analysis. Spectral features usually include the frequency distribution, intensity distribution, peak frequency, etc. of electromagnetic radiation. The process of extracting spectrum features usually uses the Fourier transform (FFT) algorithm, which can convert time domain signals into frequency domain signals. For example, assuming that the decrypted electromagnetic radiation data contains 1000 data items, the electromagnetic radiation intensity of each data item is -49.9915dBm, and the frequency range is 1GHz, then the FFT algorithm can be used to convert these data into frequency domain signals to obtain spectrum features.

[0200] After extracting the spectrum features, it is necessary to perform K-means clustering analysis on the geographic location information and electromagnetic radiation data of all monitoring sites to determine the spatial distribution characteristics of electromagnetic radiation. The K-means clustering algorithm is a commonly used unsupervised learning algorithm that can divide the data into k clusters, and the center of each cluster is called the cluster center.

[0201] After obtaining k cluster centers, it is necessary to calculate the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center. The average electromagnetic radiation intensity usually indicates the average electromagnetic radiation level of the area represented by the cluster center, and the spatial distribution characteristics indicate the distribution of electromagnetic radiation in the area. For example, assuming that cluster center 1 contains 3 monitoring stations, whose electromagnetic radiation intensities are -49.9915dBm, -50.0123dBm, and -50.0331dBm, respectively, then the average electromagnetic radiation intensity is:

[0202]

[0203] The spatial distribution characteristics can be obtained by calculating the geographical location information of the cluster center and the variance of the electromagnetic radiation intensity. For example, assuming that the geographical location information of cluster center 1 is (116.3975, 39.9087) and the variance of the electromagnetic radiation intensity is 0.0004, the spatial distribution characteristics can be expressed as (116.3975, 39.9087, 0.0004). In this way, the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center can be accurately calculated.

[0204] After calculating the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center, cluster centers with similar characteristics need to be merged to obtain m target areas. Similarity is usually determined by calculating the Euclidean distance or cosine similarity between cluster centers. If the Euclidean distance is less than a preset threshold (e.g., 0.0002), the two cluster centers are merged into one target area. In this way, cluster centers with similar characteristics can be accurately merged to obtain m target areas.

[0205] After merging the cluster centers, it is necessary to determine the boundaries of each target area based on the spatial distribution characteristics to obtain target areas with similar electromagnetic environment characteristics. The convex hull algorithm is usually used to determine the boundaries. For example, assuming that target area 1 contains 3 monitoring stations, and their geographical location information is (116.3975, 39.9087), (116.3976, 39.9088), (116.3977, 39.9089), the convex hull algorithm can be used to determine the boundaries of the target area. For example, the convex hull algorithm can be expressed as:

[0206] 1. Find the leftmost point (116.3975,39.9087).

[0207] 2. Starting from that point, find the next point in a clockwise direction so that all other points are to the right of this point.

[0208] 3. Repeat the above steps until you return to the starting point.

[0209] In this way, the boundaries of the target area can be accurately determined.

[0210] After determining the target area, the spectral features are input into a pre-built CNN model to determine the electromagnetic radiation pattern. CNN (Convolutional Neural Network) is a commonly used deep learning model suitable for processing image and spectrum data. The pre-built CNN model usually includes multiple convolutional layers, pooling layers, and fully connected layers, which can automatically extract spectral features and classify them. In this embodiment, these spectral features can be converted into image format and input into the CNN model for classification. For example, the CNN model can output the classification results of the electromagnetic radiation pattern, such as "high frequency radiation", "low frequency radiation", "pulse radiation", etc. In this way, the electromagnetic radiation pattern can be accurately determined.

[0211] This embodiment uses an analysis engine to decrypt each data block in blocks, and determines the target area of ​​electromagnetic radiation patterns and similar electromagnetic environment characteristics based on the decrypted electromagnetic radiation data, which can significantly improve the accuracy and efficiency of electromagnetic environment monitoring. First, the data block is decrypted using a temporary key to restore the original electromagnetic radiation data and ensure the integrity and security of the data. Secondly, the spectrum features are extracted to provide a scientific basis for subsequent electromagnetic radiation pattern analysis. Then, the cluster centers are determined using K-means cluster analysis, which can accurately divide the spatial distribution characteristics of electromagnetic radiation. Then, the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center are calculated, which can provide a reliable basis for the subsequent target area merging. Then, cluster centers with similar characteristics are merged to accurately determine the target area. Finally, the electromagnetic radiation pattern is determined using the CNN model, which can provide a scientific basis for electromagnetic environment monitoring.

[0212] In one implementation of this embodiment, the electromagnetic radiation pattern and the target area are interacted with multiple physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated and displayed through a preset map API, including the following steps:

[0213] S810, generating an electromagnetic radiation intensity distribution map according to the electromagnetic radiation pattern and the target area;

[0214] S820, dynamically correspond the electromagnetic radiation intensity distribution map with the geographic location data of multiple physical nodes, and generate a three-dimensional spatial distribution map of the electromagnetic field in combination with a preset map API.

[0215] After determining the electromagnetic radiation pattern and target area, an electromagnetic radiation intensity distribution map can be generated based on this information. The electromagnetic radiation intensity distribution map is usually in the form of a heat map, which uses color depth to represent the strength of the electromagnetic radiation intensity. The process of generating a heat map first requires converting the electromagnetic radiation data into geospatial data, and then using an interpolation algorithm to generate a continuous electromagnetic radiation intensity distribution map. In actual implementation, geographic information system (GIS) software (such as ArcGIS or QGIS) can be used to generate heat maps. For example, ArcGIS software can be used to perform interpolation analysis on electromagnetic radiation data.

[0216] After generating the electromagnetic radiation intensity distribution map, it is necessary to dynamically correspond the distribution map with the geographic location data of multiple physical nodes, and generate a three-dimensional spatial distribution map of the electromagnetic field in combination with the preset map API. The dynamic correspondence process usually uses a geographic coordinate matching algorithm, which can match the geographic coordinates of the electromagnetic radiation intensity distribution map with the geographic coordinates of the physical nodes. For example, assuming that the geographic location information of physical node 1 is (116.3975, 39.9087), the geographic location information of physical node 2 is (116.3976, 39.9088), and the geographic location information of physical node 3 is (116.3977, 39.9089), the geographic location information of these physical nodes can be matched with the geographic coordinates of the electromagnetic radiation intensity distribution map using a geographic coordinate matching algorithm. In this way, the dynamic correspondence between the electromagnetic radiation intensity distribution map and the geographic location data of the physical nodes can be accurately achieved.

[0217] After dynamic correspondence is achieved, it is necessary to combine the preset map API to generate a three-dimensional spatial distribution map of the electromagnetic field. The preset map API usually includes Google Maps API, Baidu Maps API, etc. For example, Google Maps API can be used to superimpose the electromagnetic radiation intensity distribution map with the geographic location data of the physical node to generate a three-dimensional spatial distribution map.

[0218] This implementation method can significantly improve the intuitiveness and accuracy of electromagnetic environment monitoring by exchanging data between the electromagnetic radiation pattern and the target area and multiple physical nodes, and generating a three-dimensional spatial distribution map of the electromagnetic field in combination with a preset map API. First, the electromagnetic radiation intensity distribution map is generated according to the electromagnetic radiation pattern and the target area, which can provide a scientific basis for the subsequent generation of a three-dimensional spatial distribution map. Secondly, the electromagnetic radiation intensity distribution map is dynamically matched with the geographic location data of the physical node, which can accurately achieve data matching and superposition. Finally, the three-dimensional spatial distribution map is generated in combination with the preset map API, which can intuitively display the spatial distribution of the electromagnetic field. In this way, a large number of three-dimensional spatial distribution maps can be efficiently generated and managed, providing reliable support for electromagnetic environment monitoring and management.

[0219] The present application also provides an online frequency selection system. Figure 4 As shown, including:

[0220] A central control device, including a local database, the local database includes at least one physical node, and the central control device is deployed with an online frequency selection platform;

[0221] The intelligent hardware terminal group is connected to the central control device.

[0222] The present application also provides an online frequency selection platform, such as Figure 5 As shown, including:

[0223] Cloud data centers; and

[0224] Analytical engine.

[0225] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0226] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of the processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0227] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0228] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0229] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0230] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0231] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0232] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0233] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An online frequency-selective data transmission method, characterized in that: Applied to an online frequency selection system, the online frequency selection system includes a central control device, an intelligent hardware terminal group and a client, the central control device is connected to the intelligent hardware terminal group, the central control device includes a local database, the local database includes at least one physical node, and the central control device is deployed with an online frequency selection platform, the online frequency selection platform includes a cloud data center and an analysis engine, the method includes: Collect multi-band electromagnetic radiation data of multiple monitoring sites through the intelligent hardware terminal group; Performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set; Generate a data table according to the corrected electromagnetic radiation data set, and based on the data table, disperse and store the corrected electromagnetic radiation data to a plurality of physical nodes, wherein the data type of the electromagnetic radiation data stored in each physical node is consistent; The electromagnetic radiation data set after encryption and correction is obtained to obtain an encrypted electromagnetic radiation data set, and the encrypted electromagnetic radiation data set is sent to the online frequency selection platform through a dynamic block transmission strategy, wherein the cloud data center is used to receive data blocks of the encrypted electromagnetic radiation data set, and classify and store all the data blocks of the encrypted electromagnetic radiation data set, and the analysis engine is used to decrypt each data block in blocks, and determine the electromagnetic radiation pattern and the target area with similar electromagnetic environment characteristics based on the electromagnetic radiation data after block decryption; In response to receiving the electromagnetic radiation pattern and the target area returned by the online frequency selection platform, the electromagnetic radiation pattern and the target area are interacted with multiple physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated through a preset map API and displayed through the client.

2. The method according to claim 1, characterized in that: The performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set includes: Acquire current environmental data, and use a preset electromagnetic wave attenuation formula to calculate the attenuation coefficient of the electromagnetic wave in the air based on the current environmental data; Environmental interference compensation is performed on the multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data and obtain a corrected electromagnetic radiation data set.

3. The method according to claim 2, characterized in that The intelligent hardware terminal group includes n sensors, where n is an integer greater than 2, and the environmental interference compensation is performed on the multi-band electromagnetic radiation data according to the attenuation coefficient to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set, including: Obtaining the installation location of each sensor in the intelligent hardware terminal group; For each electromagnetic radiation data, determining the geographical location information of the monitoring site; Calculate the straight-line distance of the electromagnetic wave from the sensor to the monitoring site using a distance calculation formula according to the installation location and the geographical location information of the monitoring site; Calculating an average value of all the straight-line distances, and using the average value as the electromagnetic wave propagation distance; Calculating the product of the attenuation coefficient and the propagation distance of the electromagnetic wave to obtain the attenuation of the electromagnetic wave in the air; The multi-band electromagnetic radiation data is compensated according to the attenuation to obtain a corrected electromagnetic radiation data set.

4. The method according to claim 3, characterized in that Generating a data table according to the corrected electromagnetic radiation data set, and dispersively storing the corrected electromagnetic radiation data to multiple physical nodes based on the data table, includes: Generate a data table including data type, timestamp, and geographic location information according to the corrected electromagnetic radiation data set, wherein the data table includes all electromagnetic radiation data in the corrected electromagnetic radiation data set, and each electromagnetic radiation data corresponds to a data type, timestamp, and geographic location information; The data table is divided into multiple sub-tables according to the data type, and the electromagnetic radiation data corresponding to each sub-table is dispersedly stored in the corresponding physical nodes, wherein the multiple sub-tables include at least one real-time data sub-table, at least one historical data sub-table and at least one alarm data sub-table.

5. The method according to claim 4, characterized in that The encrypted and corrected electromagnetic radiation dataset is used to obtain an encrypted electromagnetic radiation dataset, including: Using a preset encryption algorithm to encrypt the corrected electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set; Get the current network bandwidth and current network delay; Calculate the temporary key generation parameter using a preset parameter calculation formula according to the current network bandwidth and the current network delay; Calculate a first Hash value of the temporary key generation parameter, and use the first Hash value as the temporary key; The temporary key is bound to the encrypted electromagnetic radiation dataset to obtain an encrypted electromagnetic radiation dataset.

6. The method according to claim 5, characterized in that The dynamic block transmission strategy includes: Calculating the ratio of the current network bandwidth to the preset reference bandwidth to obtain a network bandwidth factor; Calculating the inverse of the product of the network bandwidth factor and the temporary key to obtain a data block size coefficient; Acquire the data set size of the encrypted electromagnetic radiation data set, and use the product of the data set size and the data block size coefficient as the size threshold of each data block, and adopt a dynamic partitioning strategy to dynamically partition the encrypted electromagnetic radiation data set into a plurality of data blocks, wherein the size of each of the data blocks is smaller than the size threshold; For each of the data blocks, calculating a second Hash value of the product of the timestamp and the geographic location information, and using the second Hash value as a unique classification identifier of the data block; Binding the unique classification identifier to the corresponding data block, and transmitting all the data blocks one by one to the cloud data center of the online frequency selection platform according to the unique classification identifier; The dynamic partitioning strategy includes: Setting the initial data block size to the size threshold; Reading the data in the encrypted electromagnetic radiation data set in sequence, and when the accumulated data amount reaches the current data block size, dividing the read data into a data block; Calculate the average size of the divided data blocks; If the average size is greater than 90% of the size threshold and less than the size threshold, keeping the current data block size unchanged; If the average size is less than 90% of the size threshold, increase the current data block size by 10%; If the average size is equal to the size threshold, reducing the current data block size by 5%; Repeat the above steps until all the encrypted electromagnetic radiation data sets are divided.

7. The method according to claim 5, characterized in that The analysis engine decrypts each of the data blocks in blocks, and determines the electromagnetic radiation pattern and the target area with similar electromagnetic environment characteristics based on the electromagnetic radiation data after block decryption, including: The analysis engine uses the temporary key to decrypt each data block to obtain the electromagnetic radiation data after block decryption; Extracting frequency spectrum features of the electromagnetic radiation data after block decryption; Performing K-means clustering analysis on the geographical location information of all the monitoring sites and the electromagnetic radiation data after block decryption to obtain k cluster centers; Calculate the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center; The cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics are merged to obtain m target areas, where m is less than or equal to k; Determine the boundary of each target area according to the spatial distribution characteristics, and obtain target areas with similar electromagnetic environment characteristics; The spectral features are input into a pre-built CNN model to determine the electromagnetic radiation pattern.

8. The method according to claim 1, characterized in that: The step of performing data interaction between the electromagnetic radiation pattern and the target area and the plurality of physical nodes, and generating and displaying a three-dimensional spatial distribution map of the electromagnetic field through a preset map API includes: generating an electromagnetic radiation intensity distribution map according to the electromagnetic radiation pattern and the target area; The electromagnetic radiation intensity distribution map is dynamically matched with the geographical location data of the plurality of physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated in combination with a preset map API.

9. An online frequency selection system, applied to the online frequency selection data transmission method according to any one of claims 1 to 8, characterized in that: include: A central control device, comprising a local database, the local database comprising at least one physical node, and the central control device is deployed with an online frequency selection platform; The intelligent hardware terminal group is connected to the central control device.

10. An online frequency selection platform, applied to the online frequency selection platform according to any one of claims 1 to 8, characterized in that: include: Cloud data centers; as well as Analytical engine.

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